This paper presents a common framework for designing tune mass dampers (TMD) and active mass dampers (AMD) to control the vibrations of shear buildings. This common framework is used for tuning the parameters of TMD and AMD devices in order to achieve a particular control objective. Thus, TMD and AMD can be better compared in practical applications. As an application example, two scaled TMD and AMD prototypes are tuned and installed in a two-storey building model. The parameters of the TMD and AMD are obtained by using this common framework and the same control objective. Finally, the performance assessment of these scaled prototypes is presented, highlighting the theoretical limitations and implementation problems, which can be used as practical guidelines for future real implementations.

This thesis proposes a testbed capable of validating experimental controller designs.Because it is undesirable to implement unvalidated controller designs on physical hardware, the need for an effective testbed arises. For the testbed to be useful, it should be easy to use and contain all the hardware and software necessary to implement a controller and analyze its response. To accomplish these goals, a testbed is created by joining the National Instruments ELVIS III board with the Quanser Controls Board. Three controllers are implemented to evaluate the usefulness of the testbed. The controllers are designed using the neoclassical design technique. The neoclassical technique is an experimental design methodology that, until now, has never been implemented on a physical system. The neoclassical technique uses principles from both modern and classical approaches to control to match the transfer function of a system to an optimal transfer function; thus, simplifying the design process. Neoclassical controllers are designed in continuous time, discrete time, and discrete time with a state estimator. A PID controller is designed and used to evaluate the usefulness of the neoclassical design technique. PID controllers have been around for many years and are a highly common method of control. They have been studied extensively and there are several techniques available for controller design. Because the PID is well-known, it provides a good standard to evaluate the neoclassical technique against. A design and performance analysis is then conducted after all controllers have been implemented. The testbed is evaluated on how well it performed as well as the difficulty required to implement the individual controller designs. The performance of the neoclassical controllers is also considered. A comparison is made between the neoclassical controllers and PID controller to determine if the neoclassical method improves efficiency of controller design. It will be shown that while the testbed analyzed in this thesis may be useful as an instructional aid, it is not particularly useful in validating experimental controllers.

We study the problem of estimating and tracking an unknown trajectory with a multi-rotor UAV in the presence of modeling error and external disturbances. The reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation frame work to estimate the unmeasured multi-rotor states, modeling error, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

Reinforcement learning is the process of using trialand-error with finite rewards to encourage an agent to take the optimal action when presented with the state of its environment. Our agent is a single-inverted pendulum, attached to a cart on a one-dimensional track, with a 4-dimensional continuous state space:
[x,α, x˙,α˙] – corresponding to the x-position (right positive), angle (counterclockwise positive from 0 vertical), x-velocity, and angular velocity, as indicated in Figure 1.

Position resolution is a major problem in teleoperation applications with significant disparity between master and slave workspace. While rate mode control is suitable for slave free motion operations, it poses significant stability and performance challenges for task manipulation. In this paper, we propose a hybrid control scheme that offers seamless transition between position and rate control modes based on the environment location information collected from a range sensor. The system incorporates the strengths of position and rate control modes while masking their shortcomings. Experiments to determine the viability of this method are carried out on a single degree-of-freedom teleoperation test-bed.

Unmanned aircraft have had a great impact on society, their applications in various fields are increasing day by day, so it becomes necessary to propose, develop or validate new designs and concepts that help minimize operating errors or improve the safety of aircraft. One way to understand the operating principle of these unmanned aircraft is through laboratory prototypes or test benches; these prototypes allow experimentation and testing in controlled environments to minimize errors in their operation. This article presents a novel configuration for a laboratory prototype of a tandem-rotor helicopter with two degrees of freedom (DOF), and the dynamic modeling, control, signal filtering, and implementation of the control and filtering system using a microcontroller and a low-cost prototype made with easily accessible materials. This is to promote education, research, and technological development in this area of engineering.

In this paper, we propose a new class nonlinear hybrid controller (NHC) for swinging-up and stabilizing the (under-actuated) rotary inverted pendulum system. First, the swing-up controller, which drives the pendulum up towards the desired upright position, is designed based on the feedback linearization and energy control methods. Then, the modified super-twisting sliding mode control is proposed based on the new sliding surface to stabilize both the fully-actuated (the rotary arm) and under-actuated (the pendulum) state variables. In the proposed NHC, around the upright position, the stabilization controller is applied, and in different circumstances aside from the upright position, the swing-up controller is used. We show that with the proposed NHC: (i) in the swing-up stage, the pendulum is able to reach the desired upright position; and (ii) in the stabilization stage, the closed-loop rotary inverted pendulum is asymptotically stable. We demonstrate the effectiveness of the proposed NHC through extensive experiments. In particular, (i) the faster swing-up under the similar control effort is obtained, compared with the existing fuzzy logic swing-up controller; (ii) the better stabilization control performance for the convergence of the angular positions of the rotary arm and pendulum is attained and the chattering is alleviated, compared with the existing sliding mode stabilization controllers; (iii) the better stabilization control accuracy with the faster convergence time and lower peak overshoot is accomplished, compared with the existing Fuzzy-LQR controller; and (iv) the good robustness against sudden external disturbances is achieved.

This research concerns a novel attitude stabilization structure for a ducted-fan aerial robot to work against modeling error and strong external transient disturbance, and it focuses on two main control targets: modeling error compensation, and the improvement of disturbance resistance along the rolling channel. For the first research objective, we proposed an adaptive nominal controller with the reconfigurable control law design based on the estimation of the modeling error found in the closed-loop. Results of simulations and corresponding flight tests verified that the proposed adaptive control structure is robust against both constant and time-varying modeling error. For the other research objective, a SAC (stability augmentation structure) was devised based on the CMG (control moment gyroscope) theory in order to provide extra moment which effectively withstands the transient disturbance beyond the CDG (critical disturbance gain). Furthermore, we studied the corresponding controller for the SAC via the SMC (sliding mode control) theory, while the working mechanism and performance of the SAC were verified through a specially devised prototype.

For real applications, rotary inverted pendulum systems have been known as the basic model in nonlinear control systems. If researchers have no deep understanding of control, it is difficult to control a rotary inverted pendulum platform using classic control engineering models, as shown in section 2.1. Therefore, without classic control theory, this paper controls the platform by training and testing reinforcement learning algorithm. Many recent achievements in reinforcement learning (RL) have become possible, but there is a lack of research to quickly test high-frequency RL algorithms using real hardware environment. In this paper, we propose a real-time Hardware-in-the-loop (HIL) control system to train and test the deep reinforcement learning algorithm from simulation to real hardware implementation. The Double Deep Q-Network (DDQN) with prioritized experience replay reinforcement learning algorithm, without a deep understanding of classical control engineering, is used to implement the agent. For the real experiment, to swing up the rotary inverted pendulum and make the pendulum smoothly move, we define 21 actions to swing up and balance the pendulum. Comparing Deep Q-Network (DQN), the DDQN with prioritized experience replay algorithm removes the overestimate of Q value and decreases the training time. Finally, this paper shows the experiment results with comparisons of classic control theory and different reinforcement learning algorithms.

Conventional input shaping commands have been successfully employed to suppress residual vibration in the payload rest-to-rest transportation process. Most of these methods introduce an impractical large and sudden variation on the acceleration profile. This paper presents a new smooth command input with adjustable time length and limited jerks. The command input is generated from the trolley displacement using a Bezier curve function by adjusting the position of the control points, which were divided into boundary and intermedium points. The boundary control points are selected to accurately move the trolley to its desired position with zero velocity and acceleration at the closing motion. The positions of the intermedium points were optimized using a particle swarm scheme for reducing maneuvering time while suppressing the payload oscillations at the end of the process and satisfying physical system constraints. Several cases were discussed for fixed cable length, variable cable involving single and multi-hoisting mechanisms, and different maneuver times. Simulated results were validated experimentally on a laboratory size crane. The results demonstrated that the proposed input Bezier-curve shaper provides an effective, reliable, and practical technique to be used for the payload transportation process. Moreover, the proposed method can generate asymmetrical acceleration and deceleration motions, which cannot be achieved using existing smoother commands.

Recent developments in experimental anthropomorphically-driven prostheses have shown their potential as highly dexterous prosthetic devices. However, these prostheses are both unwearable and lack haptic feedback regarding antagonistic tensions. Here, we present a wearable, anthropomorphically-driven prosthesis with a built-in haptic feedback system. Two distinct control schemes were proposed and compared in a user study with N=6 able-bodied participants performing the Box and Blocks test. The first control scheme was designed to provide a more intuitive, human like actuation and relaxation of the hand, while the simpler controller was designed to reduce fatigue from sustaining EMG signals. Participants performed significantly better with lower fatigue levels while using the controller designed to be intuitive as opposed to the simpler controller. In addition, task performance with both controllers was better than reported performance with standard myoelectric prostheses. These findings suggest that there is potential utility in wearable anthropomorphically-driven prostheses, and provide support for future studies aimed at exploring the utility of haptic feedback in anthropomorphically-driven prostheses.

This paper presents a simultaneous perturbation stochastic approximation (SPSA) approach for unknown but bounded disturbances in a typical closed loop system. These random disturbances are represented by a sequence of uncertainties along with the measured system output. Further, the proposed approach constructs a sequence of estimates and formulates an optimization problem which is minimized to achieve the adaptive control. In addition, the convergence conditions and additional generalizations are proposed to enhance the operation of SPSA algorithms for trail perturbation properties. To address the major challenges with unknown disturbances on the closed loop system, a balancing control problem with two degree of freedom (2DoF) ball balancer system and proportional integral derivative (PID) controller is considered. The proposed SPSA method minimizes the optimization problem to obtain the gain values of the PID controller. Simulation and experimental analysis are carried out, and the results substantiate that the PID gains optimized using SPSA provide better control when compared to conventional control approach in terms of tracking response under random uncertainties.

This paper proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/ Simulink simulations and experimental tests based on Quanser latest self-driving car (QCar) have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints.

Current research on quadrotor modeling mainly focuses on theoretical analysis methods and experimental methods, which have problems such as weak adaptability to the environment, high test costs, and long durations. Additionally, the PID controller, which is currently widely used in quadrotors, requires improvement in anti-interference. Therefore, the aforementioned research has considerable practical significance for the modeling and controller design of quadrotors with strong coupling and nonlinear characteristics. In the present research, an aerodynamic-parameter estimation method and an adaptive attitude control method based on the linear active disturbance rejection controller (LADRC) are designed separately. First, the motion model, dynamics model, and control allocation model of the quad-rotor are established according to the aerodynamic theory and Newton–Euler equations. Next, a more accurate attitude model of the quad-rotor is obtained by using a tool called CIFER to identify the aerodynamic parameters with large uncertainties in the frequency domain. Then, an adaptive attitude decoupling controller based on the LADRC is designed to solve the problem of the poor anti-interference ability of the quad-rotor and adjust the key control parameter b0 automatically according to the change in the moment of inertia in real time. Finally, the proposed approach is verified on a semi-physical simulation platform, and it increases the tracking speed and accuracy of the controller, as well as the anti-disturbance performance and robustness of the control system. This paper proposes an effective aerodynamic-parameter identification method using CIFER and an adaptive attitude decoupling controller with a sufficient anti-interference ability.

We propose a solution to the problem of adaptive speed observer for a class of mechanical systems containing cross terms in the velocity (momenta). We focused our attention on mechanical systems with unknown constant input disturbances and Coulomb friction matrix, which generate products of unmeasurable velocities and unknown friction parameters. The adaptive speed observer proposed is based on the well-known Immersion and Invariance technique. We ensure global convergence of the speed observer, while the friction parameters estimation remain bounded. Our approach is validated through simulations and experimental results.

@article{leylaz_2021,
title = {An Optimal Model Identification Algorithm of Nonlinear Dynamical Systems With the Algebraic Method},
author = {Leylaz, G.; Ma, S.; Sun, J.-Q.},
journal = {Journal of Vibration and Acoustics},
year = {2021},
month = {04},
volume = {143},
number = {2},
institution = {University of California, Merced, USA},
abstract = {This article proposes a nonparametric system identification technique to discover the governing equation of nonlinear dynamic systems with the focus on practical aspects. The algorithm builds on Brunton’s work in 2016 and combines the sparse regression with an algebraic calculus to estimate the required derivatives of the measurements. This reduces the required derivative data for the system identification. Furthermore, we make use of the concepts of K-fold cross validation from machine learning and information criteria for model selection. This allows the system identification with less measurements than the typically required data for the sparse regression. The result is an optimal model for the underlining system of the data with a minimum number of terms. The proposed nonparametric system identification method is applicable for multiple-input–multiple-output systems. Two examples are presented to demonstrate the proposed method. The first one makes use of the simulated data of a nonlinear oscillator to show the effectiveness and accuracy of the proposed method. The second example is a nonlinear rotary flexible beam. Experimental responses of the beam are used to identify the underlining model. The Coulomb friction in the servo motor together with other nonlinear terms of the system variables are found to be important components of the model. These are, otherwise, not available in the theoretical linear model of the system. },
issn = {1048-9002},
keywords = {data-driven modeling, algebraic differential estimation, sparse regression, system identification, nonlinear dynamics, flexible manipulator, dynamics, nonlinear vibration, system identification},
language = {English},
publisher = {ASME}
}

This article proposes a nonparametric system identification technique to discover the governing equation of nonlinear dynamic systems with the focus on practical aspects. The algorithm builds on Brunton’s work in 2016 and combines the sparse regression with an algebraic calculus to estimate the required derivatives of the measurements. This reduces the required derivative data for the system identification. Furthermore, we make use of the concepts of K-fold cross validation from machine learning and information criteria for model selection. This allows the system identification with less measurements than the typically required data for the sparse regression. The result is an optimal model for the underlining system of the data with a minimum number of terms. The proposed nonparametric system identification method is applicable for multiple-input–multiple-output systems. Two examples are presented to demonstrate the proposed method. The first one makes use of the simulated data of a nonlinear oscillator to show the effectiveness and accuracy of the proposed method. The second example is a nonlinear rotary flexible beam. Experimental responses of the beam are used to identify the underlining model. The Coulomb friction in the servo motor together with other nonlinear terms of the system variables are found to be important components of the model. These are, otherwise, not available in the theoretical linear model of the system.

In recent years, chronic pain in joints is the most common type of musculoskeletal pain among office workers. As the work culture in the office has been changed, it is the common demand that everyone has to work with a computer. In office, the worker has to sit continuously on a personal computer at least for 3–4 h. Most of the time, this leads to chronic pain in joints, generally after the period of few years of repeated exposure to such kind of regular engagement of user-computer interfacing, which is consequently converted into a serious health concern in the considerably large-sized office community. In this paper, an attempt has been made to provide a tool to assess the level of pain in the incumbent worker to have a reliable measure and at the disposal of medical practitioners to know the degree of pain to diagnose the severity of the health concern attached to it and have an optimum analysis which is not available even today. Around 250 samples have been taken and these cases have been categorized based on level of severity of pain, i.e. normal, medium, and severe. The results obtained show a considerable difference between mean and standard deviation values of pain level. Based on the data analysis of rectified EMG data, it has been observed that there is a notable difference in the Power Spectral Density of clean EMG signal w.r.t mean and Standard Deviation. The accuracy of the experimentally determined pain intensity level is a more reliable source for medical practitioners to make their diagnosis more objective one and this can be used as one of the measures to calibrate the pain-intensity.

This paper presents the development of an artificial intelligent algorithm to control circuits structuring of flexible remote experiments in engineering fields of electronics and electricity within a switching matrix architecture. In addition, this paper presents a technical analyze and characterization of VISIR system to point its advantages and its inconveniences. The developed artificial intelligent algorithm controls the interconnections between electrical and electronic components and monitors the power supplying and measurements conducting on VISIR system. We also developed an electronic board to provide the physical possibility of connecting any component to any other component on VISIR’s switching system, and thereby manifesting a switching matrix architecture within VISIR. The developed switching matrix architecture and the developed algorithm enable to have flexible remote experiments in engineering fields of electronics and electricity while having resilient control on circuits structuring for e-learning purposes. In addition, they open the way to have more circuit combinations of experiments by offering the possibility of connecting any component to any other component while respecting the electrical limits of current and voltage.

In this paper, we present a control scheme for robotic exoskeletons to gain the desired transparency for a wide range of tasks, which leads to the provision of sufficient support to the wearer in different situations. The main goal of the study was to evaluate the performance of adaptive neural networkbased controllers in gaining transparency for powerassist robots. As these devices experience a large dynamic change during handling different tasks/loads, we studied whether neural networks (NNs) can promptly learn the dynamics and provide sufficiently smooth commands to achieve the desired transparency. Our evaluations were performed through experiments conducted on an upperlimb robotic exoskeleton with unknown dynamics handling external loads. We tested a commonly used structure of NNs with different layers of adjustable weights and numbers of neurons. We also tested the smoothness of the system response and concluded that to gain natural and comfortable feeling the desired transparency needs to be selected in accordance with the task.

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates and efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform environment.

Sample efficiency is one of the key factors when applying policy search to real-world problems.
In recent years, Bayesian Optimization (BO) has become prominent in the field of robotics due
to its sample efficiency and little prior knowledge needed. However, one drawback of BO is its
poor performance on high-dimensional search spaces as it focuses on global search. In the policy
search setting, local optimization is typically sufficient as initial policies are often available, e.g.,
via meta-learning, kinesthetic demonstrations or sim-to-real approaches. In this paper, we propose
to constrain the policy search space to a sublevel-set of the Bayesian surrogate model’s predictive
uncertainty. This simple yet effective way of constraining the policy update enables BO to scale to
high-dimensional spaces (>100) as well as reduces the risk of damaging the system. We demonstrate
the effectiveness of our approach on a wide range of problems, including a motor skills task, adapting
deep RL agents to new reward signals and a sim-to-real task for an inverted pendulum system.
Keywords: Local Bayesian Optimization, Policy Search, Robot Learning

Powered lower-limb exoskeletons provide assistive torques to coordinate limb motion during walking in individuals with movement disorders. Advances in sensing and actuation have improved the wearability and portability of state-of-the-art exoskeletons for walking. Cable-driven exoskeletons offload the actuators away from the user, thus rendering light-weight devices to facilitate locomotion training. However, cable-driven mechanisms experience a slacking behavior if tension is not accurately controlled. Moreover, counteracting forces can arise between the agonist and antagonist motors yielding undesired joint motion. In this paper, the strategy is to develop two control layers to improve the performance of a cable-driven exoskeleton. First, a joint tracking controller is designed using a high-gain robust approach to track desired knee and hip trajectories. Second, a motor synchronization objective is developed to mitigate the effects of cable slacking for a pair of electric motors that actuate each joint. A sliding-mode robust controller is designed for the motor synchronization objective. A Lyapunov-based stability analysis is developed to guarantee a uniformly ultimately bounded result for joint tracking and exponential tracking for the motor synchronization objective. Moreover, an average dwell time analysis provides a bound on the number of motor switches when allocating the control between motors that actuate each joint. An experimental result with an able-bodied individual illustrates the feasibility of the developed control methods.

In this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an -player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton–Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method.

This paper proposes a control strategy integrating the non‐linear extended state observer (NLESO) and the non‐singular terminal sliding mode control (NTSMC) for the trajectory tracking of wheeled mobile robots subject to bounded disturbances. A new transformation method of chained model in terms of Lie derivative is presented to simplify the controller design. A specific NLESO combining linear term and non‐linear term is designed to estimate the disturbances with a faster convergence performance. A scheme for determining the gain range of NLESO is explicitly given to facilitate the tuning of experimental parameters. Meanwhile, the NTSMC achieves finite time convergence of the tracking error system and the chattering phenomenon in NTSMC is dramatically alleviated with the compensation from NLESO. The experimental results validate the strong robustness and good performance of the proposed control strategy.

Machine learning technique can help reduce computational cost of model predictive control (MPC). In this paper, a constrained deep neural networks design is proposed to learn and construct MPC policies for nonlinear input affine dynamic systems. Using constrained training of neural networks helps enforce MPC constraints effectively. We show the asymptotic stability of the learned policies. Additionally, different data sampling strategies are compared in terms of their generalization errors on the learned policy. Furthermore, probabilistic feasibility and optimality guarantees are provided for the learned control policy. The proposed algorithm is implemented on a rotary inverted pendulum experimentally and control performance is demonstrated and compared with the exact MPC and the normally trained learning MPC. The results show that the proposed algorithm improves constraint satisfaction while preserves computational efficiency of the learned policy.

We formulate the control problem of active suspension systems (ASSs) that are underactuated as constraint-following. The objective is to drive the system to follow a series of servo constraints or control goals which include both equality constraint of the reference trajectory and inequality constraint of the boundary restriction on the sprung mass displacement. The equality constraints may be holonomic or nonholonomic. The control design is implemented in two steps. First, a constraint-following control (CFC) is applied to drive ASSs to follow equality constraints, under the premise of ignoring inequality constraints. No auxiliary variables or pseudo variables are required for the control design. Second, diffeomorphism is employed to integrate inequality constraints into equality constraints, by which a novel CFC is proposed to deal with both equality and inequality constraints. The effectiveness of the proposed approach is demonstrated by rigorous proof. Furthermore, we investigate an ASS control problem in which the equality constraint is determined by the skyhook model, and the inequality contraint of the sprung mass displacement is determined by the suspension deflection and the tire dynamic displacement. Experimental and numerical simulation results on this case are presented for demonstrations.

This paper considers the problem of determining an optimal control action based on observed data. We formulate the problem assuming that the system can be modelled by a nonlinear state-space model, but where the model parameters, state and future disturbances are not known and are treated as random variables. Central to our formulation is that the joint distribution of these unknown objects is conditioned on the observed data. Crucially, as new measurements become available, this joint distribution continues to evolve so that control decisions are made accounting for uncertainty as evidenced in the data. The resulting problem is intractable which we obviate by providing approximations that result in finite dimensional deterministic optimisation problems. The proposed approach is demonstrated in simulation on a nonlinear system.

When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called ‘reality gap’. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. In this letter, we propose Bayesian Domain Randomization (BayRn), a black-box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning given sparse data from the real-world target domain. Bayesian Domain Randomization (BayRn) uses Bayesian optimization to search the space of source domain distribution parameters such that this leads to a policy which maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach in sim-to-sim as well as in sim-to-real experiments, comparing against three baseline methods on two robotic tasks. Our results show that BayRn is able to perform sim-to-real transfer, while significantly reducing the required prior knowledge.

This letter puts forward a novel controller for joint position tracking of bilateral teleoperation systems subjected simultaneously to time-varying communication delays and bounded actuation. Enhancing such systems’ robustness to the larger time delays comes prevalently at the cost of increased settling time for position synchronization. To this end, we propose a general and refined form of nP+D controller that not only mitigates the trade-off between settling time of synchronization and magnitude of time-delay but also exhibits better transient error in position convergence. These advantages are brought along through using capped joint-velocity in the controller, which offers a blessing in disguise in our presented Lyapunov-based stability analysis and allows disposing of the limitation that was originally considered on the nonlinear function's amplitude in previous nP+D controllers. We have shown that by setting conditions on the controller parameters obtained from the analytical study, the closed-loop dynamics’ asymptotic stability is ensured. The proposed controller's efficacy and outperformance are validated through numerical simulations and experimental evaluations on a bilateral teleoperation system with multi-DOF robots as the leader and follower.

In the wake of recent trends toward the idea of smart grids, interest is growing in decentralized energy supply. An urban environment with expected low wind speeds is particularly well suited for small scale wind turbines; various experiments using sustainable micro-energy sources have proven to contribute to a leveling of fluctuation in the electric network. This work is concerned with optimizing the operation of a cross-flow vertical axis wind turbine (VAWT) using magnetic braking. A first design step involved the development of a test bench prototype with emphasis on validation of available eddy current brake models. Another design step was focused on deriving a wind turbine model, in this case also a grey box model, for simulation purposes. The model developed has been based on input-output data retrieved from a VAWT prototype in a wind tunnel. A last development step within this work involved calculation of a rough maximum braking torque for a requirement analysis, combination of the models developed previously and a concept for a two-mode control strategy governed by supervisory control.

Artificial intelligence (AI) has an increasing influence in the manufacturing and processing industries. An increasingly interesting area of application is the design of controllers for technical systems through reinforcement learning. This allows the design of a controller with reduced effort for human experts, which is normally involved in the design process, for example by eliminating the determination of suitable controller parameters. In this paper a neural controller for a rotational inverted pendulum is developed by using artificial neural networks in combination with reinforcement learning. The rotational inverted pendulum is a classic example of a nonlinear and unstable system and has been little covered in previous publications on this subject. Furthermore, the behaviour of the neural controller is compared with a conventional controller when swinging up and balancing a rotational inverted pendulum.

This paper proposes a practical and efficient control scheme for serial link flexible robot arm using the sliding mode control, reaching phase elimination and uncertainty disturbance estimation techniques to ensure robustness against any sort of uncertainty, disturbance or variation in parameter that may arise while in operation. The controllers for SMC, Reaching phase elimination and Uncertainty disturbance estimator are designed and implemented in MATLAB. The control algorithm performs as per the requirements and constraints placed on the system to drive the error in the states to zero with minimum expenditure of actuator dynamics and maintain the states once at the level. The system uses multiple techniques in conjunction with each other to guarantee optimal performance of the system for multiple scenarios. Further, the dynamic sliding and Uncertainty Disturbance Estimator techniques are being implemented for the 2-DOF flexible link robot arm for the first time.

This paper proposes a novel two-stage method for the design of a suboptimal model-matching controller in an output feedback closed-loop system (OFCLS) using the concept of squared magnitude function (SMF). A streamlined procedure for selection of a reference model, based on a linear quadratic regulator (LQR) with integral action (LQRI) having optimum values for the elements of the weighting matrices and the degree of interaction is proposed. The degrees of the numerator and denominator polynomials of the elements of the OFCLS transfer function matrix (TFM) are obtained from those of the plant and the chosen controller structure. In the first stage of the controller design, taking the LQRI-based closed-loop system (LCLS) as a reference model, the OFCLS is obtained using the approximate model-matching (AMM) technique based on the SMF concept. The approximation method involves a higher-order approximation for stable multiple-input-multiple-output (MIMO) lower-order systems. In the second stage, controller parameters are obtained using the exact model-matching (EMM) method with information about the OFCLS and plant TFMs. The proposed controller design method outperforms the method presented in the literature on integral squared error index. The simulation and experimental results illustrate the effectiveness of the proposed method.

Robotic artificial muscles (RAMs) are promising power sources for medical fields such as surgical robotics. However, existing RAMs are challenged by scalability, material costs and fabrications. The nonlinear hysteresis in fluid-driven RAMs causes oscillations in open-loop systems. To circumvent these limitations, this letter introduces hydraulically soft microtubule artificial muscle (SMAM) that is low-cost and scalable, yet simple to fabricate. The SMAM, which only requires a flexible silicone microtube and a hollow micro-coil, is elongated or contracted under a fluid pressure. The SMAM presents an ideal candidate for flexible robotic systems such as endoscopic surgical robots. Experiments are conducted to characterize the SMAMs. Results show that the hysteresis profiles between the input syringe plunger position and output position are stable regardless of its configuration, as opposed to the highly variable responses for the tendon-sheath mechanisms. A new nonlinear model is developed to characterize the asymmetric hysteresis phenomena of the SMAM. Compared to the Bouc-Wen hysteresis models, the developed model presents a better capture of hysteresis. To demonstrate the muscle capability, a SMAMs-driven pulley and a flexible surgical arm are given. The new SMAM and its asymmetric hysteresis model are expected to provide a path for the development of rapidly efficient and low-cost soft actuators for use in flexible medical devices and surgical robotic systems.

In this study, a new perspective for developing laser scanner rangefinder based data clustering system for a 2DOF robotic ball balancer was proposed. The study focused on detecting an object (i.e., ball) on the tilt-table robotic platform using the sensor fusion and data clustering systems proposed. Clustering system was modeled by following the principles of hierarchical clustering method. The developed system involving the clustering and sensor fusion algorithms was embedded in Matlab-Simulink environment to be able to run in real-time applications. The system was tested using an experimental platform including a 2DOF robotic ball balancer equipped with high resolution encoders and a laser scanner rangefinder. In the experiments, the goal was to detect the ball and its position not only on the flat but also on the tilted platform. A camera was also attached to the top of the experimental setup and used to monitor the location of the ball on the platform. By this way the results obtained using the proposed system could be verified for accuracy, performance and repeatability issues.

Many established practices of modern society require rapid changes to accommodate challenges introduced by the Covid-19 pandemic. Education is one such important aspect of modern society, which demands changes, and online teaching became an attractive alternative method. Engineering profession demands hands-on experience to practice engineering. Therefore lab-practicals are one of the major requirements in Engineering studies. This paper presents a simulation platform, which allows the students to get hands-on experience in inverted pendulum control. balancing system. The PID and LQR controllers were used with the developed simulator and the results are presented. The simulation environment was developed using commonly available software in control systems laboratories, as well as with students, as the interface. The inverted pendulum system was modelled using CAD software and was imported into the modelling software to build a nonlinear system dynamics simulation. The mathematical model for the proposed system was established using systems theory. Furthermore, a prototype was fabricated according to the CAD model. Therefore virtual-system is based on a real system which allows the students to compare system performance in linear, nonlinear and real aspects. This simulation platform has been developed to conduct practicals on controller designing for the inverted pendulum system, performance comparison among different control laws, and for the development of a nonlinear model from an existing system. This approach in control engineering could be further extended, to produce a commercialized virtual-lab. The presented system can be used to develop advanced control systems including robust and optimal control as well as nonlinear control systems.

This paper is dedicated to the experimental analysis of discrete-time differentiators implemented in closed-loop control systems. To this end, two laboratory setups, namely an electro-pneumatic system and a rotary inverted pendulum have been used to implement 25 different differentiators. Since the
selected laboratory setups behave differently in the case of dynamic response and noise characteristics, it is expected that the results remain valid for a wide range of control applications. The validity of several theoretical results, which have been already reported in the literature using mathematical analysis and
numerical simulations, has been investigated, and several comments are provided to allow one to select an appropriate differentiation scheme in practical closed-loop control systems.

The following work contains an experimental method for identifying dynamic friction in a DC servomotor, whose identification and subsequent compensation is highly important. Friction has characteristics that are difficult to equate and for this reason it becomes very important and necessary to identify nonlinearities and subsequently compensate them. In this paper, based on the DC motor equation using Newton’s law of motion, feasible models are presented and friction is measured and analyzed through Matlab and a Quanser data acquisition card. Nonlinear dynamic friction torque is used and compared with the experimental results of a real servomotor.

Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.

Magnetic Levitation is a process in which an object is suspended with the support of the magnetic field. Despite being an unstable system, Magnetic Levitation Systems (MAGLEV) have profound applications in various fields of engineering. MAGLEV systems are sensitive, unstable, and nonlinear and uncertainties always pose a challenge in Controller Design. As a solution, adaptive controllers came into existence with adaptation mechanisms to cover the system uncertainties. In this study, a simple, novel, and an effective approach to the Enhanced Adaptive Control scheme is proposed for the ball position control and tracking of an unstable Magnetic Levitation System. The proposed Enhanced Model Reference Adaptive Scheme (EMRAC) follows the same phenomenon of the Model Reference Adaptive Scheme (MRAC) with a slight difference in its control strategy. The proposed scheme consists of Proportional-Integral-Velocity plus Feed Forward as the control structure and a modified version of the standard tuning rule is used as the adaptation mechanism. The control scheme is applied to a standard benchmark Magnetic Levitation System and the tracking performance of the scheme is tested by applying square and multi-sine pattern trajectories to the Magnetic Levitation System. The performance of the developed Enhanced MRAC performance is compared with that of the Proportional Integral Velocity with Feedforward Control (PIV+FF) scheme and the proposed control scheme is proven to be more suitable. The performance of the proposed scheme is also analyzed with Power Spectral Density and Root Mean Square Error to evaluate the ball position tracking control. It is inferred from the experimental results that Enhanced MRAC accommodates the changes and makes the system more reliable with good tracking ability.

In this study, the two-dimensional sloshing of water in a stepped based tank partially filled was analyzed using an arbitrary Lagrangian–Eulerian adaptive fixed-mesh method and including the Smagorinsky turbulence model. The numerical model is verified by contrasting the predictions made by the model with experimental results. The tank was subjected to controlled one-directional motion imposed using a shake table. The free surface evolution was followed using ultrasonic sensors, and a high-speed camera was used to record the experiments. The experimental and numerical analyses include a comparison of the wave height at different control points and snapshots of the free surface evolution for two imposed frequencies. Also, a detailed numerical study of the effects of the frequency of the imposed movement, the step height, and the fluid volume on the wave dynamics was performed. Moreover, the effect of fluid viscosity on the dynamics of the free surface was also studied. In brief, the numerical method proved to be accurate, experimental data were reported, and the effects on the numerical results of different physical and numerical aspects were exhaustively analyzed. The proposed results help to understand the sloshing of stepped geometries.

The need to design a robust, accurate and lightweight real-time control architecture for a low-cost embedded system has become a necessary step in robotics applications. Therefore, the goal of this work is to design an integrated and accurate control system for a differential wheeled mobile robot. A hierarchical control approach has been adopted in this work which, takes into account the sub-systems controller of the problem related to robot path tracking. To this end, the control structure includes the design of a sliding mode control (SMC) scheme to stabilize the robot and precisely track the path that matches the kinematics of the mechanical structure. Moreover, the control structure provides a new design of the DC motor controller for optimum performance. To obtain an integrated control system for automated tasks, an accurate position estimation and an optimized high-level path planning algorithm have been proposed. The robustness and performance of the approved control and estimation systems have been verified through real-time implementations in different experiments. The results show the high accuracy of the robot’s tracking on different paths.

It is commonly known that the design of control is difficult for the systems with non-minimum phase behavior, the coupling between the controlled variables and integral instability. The paper presents such a typical class of nonlinear system and propose a new technique generalized extended state observer-based sliding mode decoupling control. The proposed control algorithm is also able to compensate for the matched and mismatched uncertainties encountered in such nonlinear non-minimum phase systems. The decoupling control approach is used to decouple the entire system framework into subsystems. The sliding surface of the individual subsystem was composed using the estimated states of GESO. The proposed technique can nullify the effect of external disturbances, parametric uncertainties and unmodelled dynamics due to the inclusion of disturbance compensation gain in the control law. The proposed methodology enables the system to accomplish asymptotic stability and better dynamic response with lesser IAE and ITAE values than existing decoupling control strategies. An experimental application of the proposed technique to Quanser’s rotary pendulum module is investigated.

In this thesis, the problem of fault detection in an interconnected water tank system using a particle filter is investigated. The objective of the work is to use the particle filter to detect the time of leakage faults occurring in an interconnected water tank system. A physical three tank system is built using commercial off the shelf components. Water flows into the system from a sump via two pumps, and flows between the tanks through linking tubes at the bottom of each tank. Leakage faults can be induced by opening a leakage tube at the bottom of a tank. A mathematical model is built to capture the dynamics of normal and faulty behaviour. The height of water in each tank is measured using pressure sensors. The Particle filter compares water height measurements to model predictions to determine if the system is operating normally or if a fault has occurred. The particle filter can correctly detect the time of one or two fault occurrences in the system at most 8 seconds after a fault occurs. Also, this thesis investigates the effect that time delayed measurement data and incipient leakage fault have on the fault detection performance of the particle filter.

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.

As one of the most promising topics in complex control processes, data-driven techniques have been widely used in numerous industrial sectors and have developed over the past two decades. In addition, the fractional-order controller has become more attractive in applied studies. In this article, a fractional integral control is implemented for a rotary flexible joint system. Moreover, an adjusted virtual reference feedback tuning (VRFT) technique is used to tune the fractional-order integrator. In this method, fractional integral control is designed based on state feedback control. Then, VRFT is adjusted and applied to the fractional integral controller. The effectiveness of the proposed adjusted VRFT method is discussed and presented through simulation and experimental results. The tracking performance of the rotary arm and the minimization of the vibration tip is evaluated based on the proposed method. In this article, the comparison of our proposed VRFT fractional scheme is made with the classical state feedback as well as a recently developed state feedback-based fractional order integral (SF-FOI) controller. The current investigations determine the performance improvement of our proposed scheme of comparable structure to the recent SF-FOI, with the introduction of the VRFT to the SF-FOI scheme.

The objective of this work is to design a fuzzy rule-based set point weighting mechanism for fuzzy PID (FPID) controller so that an overall improved closed-loop performance may be achieved for linear as well as nonlinear process models. Till date, tuning criteria for FPID controllers are not well defined. Trial-and-error approach is primarily adopted and it is quite time-consuming and does not always ensure improved overall closed-loop behaviour. Hence, to ascertain satisfactory closed-loop performance with an initially tuned fuzzy controller, a fuzzy rule-based set point weighting mechanism is reported here. The proposed scheme is capable of providing performance enhancement with instantaneous weighting factor calculated online for each instant based on the latest process operating conditions. The proposed methodology is capable of ascertaining acceptable performances during set point tracking as well as load recovery phases. Efficacy of the proposed scheme is verified for linear as well as nonlinear process models through simulation study along with real-time verification on servo position control in comparison with the others’ reported performance augmentation schemes as well as fuzzy sliding mode control.

A Fuzzy Resource Manager (RM) to compensate communication loads in real-time systems is presented. The design is based on a new model of a Constant Bandwidth Server (CBS), which is responsible for assigning time slots to tasks with the highest priority when idle time is available. Assuming that each application can be executed at different service levels, without being below a minimum limit, a fuzzy approach is introduced that allows to adjust the time resources assigned to each task and to compensate non-linearities in time resources requests. The RM increases or decreases the virtual platform for each application and assigns a maximum process time budget for it, which is gradually used and refilled when depleted, without affecting the other applications.
The scheme self-adjusts to sudden changes in applications process time requirements.