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.

Unmanned aerial vehicles (UAVs) due to their hovering and vertical take-off and landing, have acquired a lot of interest from researchers. In order to implement UAVs, different techniques and methods are used. QNET VTOL 2.0 board in the field of unmanned aerial vehicles is an important platform designed for NI ELVIS II. In this research a comparative study of PID tunning for QNET VTOL 2.0 board, using LabVIEW is presented. Three tuning methods heuristic(error and trial) method, intelligent tunning using fuzzy logic and ANFIS(adaptive neuro fuzzy inference system) are chosen for comparison on the basis of literature. The proposed PID tunning methods are applied to the existing PID controller for controlling the pitch angle of QNET VTOL 2.0 board. The main purpose of this study is to compare the performance of proposed three tuning methods on the basis of step characteristic (rise time settling time , peak time and maximum overshoot). From the experimental results it is found that implementation of ANFIS has improved the simulation results significantly as compare to the self-tuned fuzzy PID and conventional PID.

This paper presents a state feedback control design with an event-triggered scheme for the Maglev Train (MagT.) system with a saturated control input. In a state feedback control design that precisely tracks the position of the coil current and coil voltage of actuated levitation system, we first derived the mathematical model in the form of a state–space model from the MagT. model; then, we implemented the saturated control input to encounter the uncertainties. A new event-triggered mechanism, in which the threshold parameter remains constant with resulting states, is utilized to save the constrained network resources and improve information exchange efficiency. Using the Lyapunov–Krasovskii functional method, a defensive control is established in the form of linear matrix inequalities (LMIs) for the resultant system. Finally, it is shown through the most practical approach example that the proposed method in this paper is effective for ensuring better system performance.

Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model specification of the manufacturer or simple numerical methods such as linear regression. However, this approach does not guarantee physically plausible parameters and can only be applied to kinematic chains consisting of rigid bodies. In this article, we describe a differentiable simulator that can be used to identify the system parameters of real-world mechanical systems with complex friction models, holonomic as well as non-holonomic constraints. To guarantee physically consistent parameters, we utilize virtual parameters and gradient-based optimization. The described Differentiable Newton-Euler Algorithm (DiffNEA) can be applied to a class of dynamical systems and guarantees physically plausible predictions. The extensive experimental evaluation shows, that the proposed model learning approach learns accurate dynamics models of systems with complex friction and non-holonomic constraints. Especially in the offline reinforcement learning experiments, the identified DiffNEA models excel. For the challenging ball in a cup task, these models solve the task using model-based offline reinforcement learning on the physical system. The black-box baselines fail on this task in simulation and on the physical system despite using more data for learning the model.

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.

A general controller scheme for stabilizing a non-linear system, which has its origin from the linear system theory, is proposed in this paper. The proposed controller can stabilize the non-linear system subjected to initial conditions. An effective way to obtain the controller parameters is presented with the knowledge
of the system model. The controller is designed for the linear time-invariant (LTI) system, which can reject any disturbance acting on it. Paper emphasis the idea of an integrator controller in disturbance rejection. The concept is extended to the application to non-linear systems where the non-linearities are assessed as the disturbance to the refined linear part of the system. Boundedness and convergence of the non-linear system with the controller are proved to justify system stabilization. Hardware implementation of the controller on the 2 dof helicopter model is presented with experimental results, which validates the
proposed control scheme.

This paper explores a novel system architecture of heterogeneous collaborative system for powerline inspection using human-robotic teaming. The research uses Quanser’s Autonomous QCar and QDrone to demonstrate the results. This heterogeneous powerline inspection system consists of three separately controlled elements that work collaboratively within a centralized controlled space and those individual elements are as follows: 1) A control system operated by the human that takes cares of the Autonomous car and drone functionality, 2) An autonomous unmanned ground vehicle system that will take tasks from the human control system to drive through the path determined, and 3) An autonomous unmanned aerial vehicle system that conducts the powerline search and inspection within a specified range. All three systems operate together as one heterogeneous system, which demonstrates the application of human-robotic teaming. This paper will demonstrate how the proposed system architecture is constructed and the results obtained from it in an indoor environment is discussed.

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.

This paper introduces a new continuous integral sliding mode control algorithm, where the discontinuous function of the super-twisting control law is replaced with a continuous disturbance observer for the substantial chattering attenuation. In the present integral sliding mode control, the discontinuous function generates chattering that is undesirable for several real-time applications. The proposed control strategy decreases the amplitude of the controller gain compared to the existing integral sliding mode controls, and as a consequence of this, the attenuation of chattering is achieved to a great extent. The efficacy of the proposed control algorithm is validated successfully on the single-input single-output Inverted Pendulum and 2-DOF Helicopter nonlinear coupled multi-input multi-output systems. The simulation and experimental results demonstrate the successful application of the proposed control approach to follow reference inputs and acquire robustness and stabilization of the system in the presence of limited matched perturbations and nonlinearities.

High-rate global navigation satellite system (GNSS) has emerged as an effective method to recover seismic waveforms without saturation and drifts, but it has the limitation of relatively lower sampling rate and higher noise level compared to seismic instruments. In this study, we present a new seismogeodetic method by integrating GNSS and accelerometer data to obtain optimal real-time seismic waveforms. Unlike traditional integration methods based on GNSS techniques of relative positioning or precise point positioning, the new method uses a GNSS time difference technique and inherits its unique advantage in real-time and high-accuracy velocity solutions. Furthermore, by incorporating the tightly coupled structure, it can overcome the cascading problem and provide more accurate and robust waveforms compared to its loosely coupled counterpart. The performance of this method is first compared with the traditional loosely coupled approach in challenging environments through a set of shake table experiments. With three GNSS satellites, this approach method can improve the accuracy of velocities and displacements by 42 and 87 per cent, respectively. With four or more GNSS satellites, the average improvements of the method reach 25 and 41 per cent for the velocities and displacements, respectively. We then validate the full performances of the method through simulated shake table experiments and collocated GNSS and accelerometer data during the 2016 Mw 6.6 central Italy earthquake. The simulated and real-event analyses demonstrate that the new integration method can take full advantage of the complementary characteristics of GNSS and accelerometer sensors. By providing more accurate and broad-band velocity and displacement waveforms in a real-time or near-real-time manner, this method is quite promising in earthquake early warning and rapid source inversion.

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.

The aim of this research is to develop a novel design of interval type-2 neuro-fuzzy (IT2NF) controller for active vibration control of a flexible structure during an earthquake. For this purpose, two adaptive neural network based fuzzy logic controllers are designed and combined to create the novel design of an IT2NF controller to reduce the vibrations of two-storey flexible building model that occur during earthquake disturbance effects. Accordingly, dynamic modeling of a flexible structure is realized and simulated using the MATLAB / SimMechanics. Then, an experimental setup consisting of two-storey flexible structure, active mass damper (AMD) and shaker is established. Additionally, IT2NF controller is implemented in simulation and experimental models, and the effectiveness and performance of the IT2NF controller are tested under the scaled Northridge Earthquake acceleration. The obtained simulations and experimental responses are evaluated in terms of cart displacements, deflections, and accelerations of the flexible floors showing a good agreement between the simulations and the experimental results. The results show that the designed novel IT2NF controller reduced the total deflections of first and second floor by 72.3% and 68.7%, respectively, when compared with the uncontrolled system. Additionally, it is also found that the designed IT2NF controller is able to reduce the accelerations of the first and second floor by 64.8% and 54.6%, respectively. The proposed and designed control method reported in this study can be employed as an active vibration controller for multi-degree of freedom of flexible systems under the disturbances such as earthquake excitations.

With the rising popularity of telerobotic systems, the focus on transparency with regards to haptic perception is also increasing. Transparency, however, represents a theoretical ideal as most bilateral force-reflecting telerobots introduce dynamics (stiffness and damping) between the operator and the environment. To achieve true dexterity, it will be essential to understand how humans embody the dynamics of these telerobots and thereby distinguish them from the environment they are exploring. In this short manuscript, we introduce a novel single degree-of-freedom testbed designed to perform psychophysical and task performance assessments of kinesthetic perception during telerobotic exploration. The system is capable of being configured as a rigid mechanical teleoperator, a dynamic mechanical teleoperator, and an electromechanicaal teleoperator. We performed prefatory system identification and found that the system is capable of simulating telerobotic exploration necessary to understand the impact of master-slave dynamics on kinesthetic perception.

Human-in-the-loop telerobotic systems (HiLTS) are robotic tools designed to extend and in some circumstances improve the dexterous capabilities of the human operator in virtual and remote environments. Dexterous manipulation, however, depends on how well the telerobot is incorporated into the operator's sensorimotor control scheme. Empirical evidence suggests that haptic feedback can lead to improved dexterity. Unfortunately, haptic feedback can also introduce dynamics between the leader and follower of the telerobot that affect both stability and device performance. While concerted research effort has focused on masking these device dynamics or bypassing them altogether, it is not well understood how human operators incorporate these dynamics into their control scheme. We believe that to advance dexterous telerobotic manipulation, it is crucial to understand the process by which humans operators incorporate teleoperator dynamics and distinguish them from the dynamics of the environment. Key to this knowledge is an understanding of how advanced telerobotic architectures compare to the gold standard, the rigid mechanical teleoperators first introduced in the 1950's. In this manuscript, we present a teleoperator testbed that has reconfigurable transmissions between the leader and follower to change its dynamic behavior. The intent of this testbed is to investigate the effect of the teleoperator's dynamics on perception of and task performance in the remote/virtual environment. We describe the hardware and software components of the testbed and then demonstrate how the different teleoperator transmissions can lead to differences, sometimes significant, in the dynamics that would be felt by the operator when exploring the same environment.

This paper presented a model predictive control strategy with guaranteed nominal stability, applicable to systems with repeated integrating poles. The offset-free control law is obtained inside a one-layer optimization formulation through the development of a state-space model based on an analytical expression of the system step response. While the stability of the closed-loop system is achieved by considering an infinite prediction horizon along with the inclusion of terminal equality constraints, its feasibility to tackle model uncertainty is ensured by means of an always feasible-optimization formulation through the insertion of a suitable set of slack variables. The effectiveness of the method is illustrated for the output tracking and disturbance rejection problem in a benchmark system, “ball-and-plate” apparatus, including a nonlinear plant-model mismatch scenario.

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.

The last few years have witnessed an increasing interest in the use of humanoid robots for diverse technological applications. Since the center of mass (CoM) of a typical humanoid lies at a relatively high elevation, such a robot often experiences instability during its operation and has a high likelihood of fall. Thus, it is necessary to endow these robots with a robust method to reduce damage that may result from the impact of a fall. Prior research on humans undergoing a forward fall has revealed that the deployment of a rollover strategy along the longitudinal axis can lower the impact force experienced on the hand to effectively reduce wrist injuries. Yet, analogous research for humanoids has received scant attention. To address this research gap, in this work, we consider the optimal design, implementation, and examination of a rollover strategy-similar to the one for humans-for a humanoid robot by employing a differential dynamic programming (DDP) approach. In addition to providing an overview of the theoretical formulation of our methodology, using results from repeated forward falling experiments with a humanoid, we demonstrate that a reliable application of the rollover strategy considerably reduces the impact force vis-à-vis the bimanual fall approach, to protect the wrist of our robot. Moreover, the experiments showcase that the proposed method is also effective in reducing the impact on the torso of the humanoid. With the use of the rollover methodology, a typical humanoid can overcome a key obstacle to its broad adoption and deployment in real-world applications.

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.

Although altitude control of multirotor flying vehicles has been studied for years, it is still required to design and analyze the system by a practical-oriented procedure. The most
challenge is due to the nonlinearities and uncertainties of thrust force characteristics. To overcome the aforementioned issues, this paper proposes a systematic approach for altitude control using disturbance observer. In particularly, this paper shows that the overall system can be equivalently modelled as a multi-agent system with rank-1 physical interaction and sector bounded nonlinearities. Thanks to this modelling, a condition for absolute stability is derived. The condition is independent of the number of propeller actuators, and hence, considerably reducing the complexity of system design. The effectiveness of the proposed approach is demonstrated by both numerical simulations and experiments using a quadrotor vehicle system.

The objective of this research is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) to close the existing research gaps between the effects of
unexpected uncertainties and current control systems. This research investigates system modeling and the effectiveness of error elimination through adaptation for change in a
plant/system’s output. The UAV is controlled via Wi-Fi using MATLAB. The effects of disturbances are reduced significantly by using the neural network-based adaptive controllers for
high-performance tracking in the presence of the disturbances. Robust control systems with adaptive mechanisms can improve navigation performance and operation for military, reconnaissance, and surveillance applications. The application of a robust controller improves the performance by observing the system’s behavior to identifying and reducing the unpredictable effects of disturbances to achieve a better behavior and response of the system. With the data sets of flight behaviors, the neural network is suitably used to learn froma set of training patterns. System identification uses a neural network to capture the behavior of system dynamics, which assists the neural network to train itself to act as a controller. Moreover, the controller's performance is validated through simulations and experiments to demonstrate the effectiveness of improving the dynamic system's speed and stability. This research presents a flexible, robust, and effective control system model that provides additional strength and reduces disturbances.

In this paper, the adaptive attitude tracking the problem of a rigid body is investigated where the input and output are transmitted via a network. To reduce the communication burden in a network, a quantizer is introduced in both uplink and downlink communication channels. An adaptive backstepping-based control scheme is developed for a class of multiple-input and multiple-output (MIMO) rigid body systems. The proposed control algorithm can overcome the difficulty to proceed with the recursive design of virtual controls with quantized output vector and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector output quantization and input quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the effectiveness of the proposed control scheme.

Ball and Beam system is one of the most popular and important laboratory models for teaching control systems. This paper proposes a new control strategy to the position control for the ball and beam system. Firstly, a nonlinear controller is proposed based on the backstepping approach. Secondly, in order to adapt online the dynamic control law, adaptive laws are developed to estimate the uncertain parameters. The stability of the proposed adaptive backstepping controller is proved based on the Lyapunov theorem. Simulated results are presented to illustrate the performance of the proposed approach.

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.

In this paper, the tracking control problem of a three degrees of freedom (3-DOF) helicopter with unknown actuator faults, model uncertainties and external time-varying disturbances is solved by using the proposed adaptive neural network (ANN)-based fault tolerant control (FTC) strategy. Firstly, the mathematical model of the 3-DOF helicopter is established. Then, neural networks (NNs) are utilized to approximate and compensate the unknown functions in the presence of the controlled system. It should be stressed that hyperbolic tangent functions are introduced to reduce the negative effects caused by NN approximation errors and external time-varying disturbances. Moreover, auxiliary functions are designed to compensate filter errors, which further enhance the tracking performance of the closed-loop system. Finally, simulation results are provided to verify the effectiveness of the proposed FTC scheme.

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.

A new analysis method, called aeroelastic real-time hybrid simulation (aeroRTHS), is proposed to study aeroelastic vibrations of a tall building model in a wind tunnel. The aeroRTHS method captures dynamic interactions between an aeroelastic structure and the wind loading to more accurately analyze unstable wind phenomena such as vortex induced vibration (VIV) and in doing so broadens the application of RTHS from sesimic applications to wind engineering and eventually to multi-hazard analyses. A 1 meter tall rigid model with an aspect ratio of 7.3 was mounted on a modified single-axis shake table, which is able to convert translational motions to the corresponding rotations at the base of the model, to allow for the model to behave in the wind tunnel as an aeroelastic structure. A total of 128 pressure sensors on both crosswind surfaces of the model measured the envelope pressure loading in the real-time condition. Compensation techniques were developed and utilized with respect to pneumatic fluctuations either from the shake table or pressure measurements along with testing the robust stability of the aeroRTHS. This proof-of-concept study validates the proposed aeroRTHS framework by conducting a series of tests in a boundary layer wind tunnel. A further step of the aeroRTHS testing was to assess the effects of including a passive tuned mass damper (TMD) to the numerical substructures; and their performance in suppressing VIV were investigated. Parameter studies were conducted by varying dynamic properties of the building models and also by utilizing two different mass ratios of the TMD to the building model. Results demonstrate that the feasibility of the aeroRTHS method to investigate aero-structures under VIV and reveal that the augmentation of buildings with TMDs is an effective way to attenuate cross-wind vibration in tall buildings.

This paper presents a robust actuator fault estimation strategy design for a 3-DOF helicopter prototype which can be adapted to aggressive maneuvers. First, considering large pitch angle condition during flight, nonlinear coupling characteristic of the helicopter system is exploited. As the pitch angle can be measured in real time, a polytopic linear parameter-varying (LPV) model is developed for the helicopter system. Furthermore, considering measurement noises in the actual helicopter system, the dynamical model of helicopter system is modified accordingly. Then, based on the modified polytopic LPV model, a robust unknown input observer (UIO) is developed for the helicopter system to realize actuator fault estimation, in which both measurement noises and large pitch angle are considered. Robust performance of proposed fault estimation approach is guaranteed by using energy-to-energy strategy. And the observer gains are calculated by using linear matrix inequalities. Finally, based on a 3-DOF helicopter prototype, both simulations and experiments are conducted. The effects of measurement noises and large pitch angle on the fault estimation performance are sufficiently demonstrated. And effectiveness as well as advantages of the proposed observer is verified by using comparative analysis.

The frequent and sudden increase of load in electric vehicles depletes oxygen from the fuel cell cathode, leading possibly to oxygen starvation. This paper presents the design of an air flow controller for fuel cell systems (FCS) that aims to replenish the depleted oxygen. A dynamic inversion is used as the feed-forward compensation for the disturbance effect coming from the load current change. However, the inverted dynamics have negative relative degree (improper) and not physically realizable. To make it proper, an augmentation by delay is done, with the same delay added into demand current. The current-dependent time delay introduced in the demand current gives the FCS the opportunity to prepare a sufficient air flow that can withstand the dip in oxygen excess ratio (OER). Besides, the FCS plant is linearized at various operating points, and the dynamic feedforward controller is designed at several operating points with interpolation applied for in-between values. The simulation results show a considerable improvement in the OER response where the constraint is met and the recovery time is reduced.

The paper proposes an approach to deal with problems stemming from inaccurate elements of car driving simulators and the disadvantages of nonlinear model-based controllers, including the Backstepping and Sliding Mode Control. For this kind of model, it is challenging to design nonlinear model-based controllers to guarantee smooth and accurate movements due to unmodelled parts in the system dynamics and the appearance of external forces. Furthermore, there are well-known demerits in the aforementioned controls, such as ‘explosion of terms’ and chattering phenomena. The main contribution focuses on constructing an adaptive and robust neural network-based controller with online learning laws, which not only ensures the high accuracy of the robot’s motion under uncertain components and external factors but also mitigate the disadvantages of conventional controllers. The stability and convergence of the proposed method are proven by Lyapunov’s stability. The simulation results show the validity and efficiency of the proposed control algorithm.

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.

This paper suggests to replace PIs and PIDs, which play a key role in control engineering, by intelligent Proportional- Derivative feedback loops, or iPDs, which are derived from model-free control. This standpoint is enhanced by a laboratory experiment.

This paper suggests to replace Proportional-Integral and Proportional-Integral-Derivative regulators, which play a key rôle in control engineering, by intelligent Proportional-Derivative feedback loops, or iPDs, which are derived from model-free control. This standpoint is enhanced by a laboratory experiment.

A quadcopter control system is a fundamentally difficult and challenging problem because its dynamics modelling is highly nonlinear, especially after accounting for the complicated aerodynamic effects. Plus, its variables are highly interdependent and coupled in nature. There aresix controllers studied and analysed in this work which are (1) Proportional–Integral–Derivative (PID), (2) Proportional-Derivative (PD), (3) Linear Quadratic Regulator (LQR), (4) ProportionalLinear Quadratic Regulator (P-LQR), (5) Proportional-Derivative-Linear Quadratic Regulator (PD-LQR) and lastly (6) the proposed controller named Proportional-Double Derivative-Linear Quadratic Regulator (PD2-LQR) controller. The altitude control and attitude stabilization of the quadcopter have been investigated using MATLAB/Simulink software. The mathematical model of the quadcopter using the Newton–Euler approach is applied to these controllers has illuminated the attitude (i.e. pitch, yaw, and roll) and altitude motions of the quadcopter. The
simulation results of the proposed PD2-LQR controller have been compared with the PD, PID, LQR, P-LQR, and PD-LQR controllers. The findings elucidated that the proposed PD2-LQR controller significantly improves the performance of the control system in almost all responses. Hence, the proposed PD2-LQR controller can be applied as an alternative controller of all four motions in quadcopters

Three-chamber configuration is the simplest type among the fluid soft actuators with multiple degrees of freedom in three-dimension space. However, the motion control of the three-chamber fluid soft actuator is a challenging task because of the strong coupling effect of the chambers which are connected together. This short paper develops a novel electrohydraulic control device with decoupling function. The supplied pressures of three cylinders in the electrohydraulic control device are linked by a swash plate mechanism. The axial displacement, the tilted angle, and the azimuth angle of the swash plate are driven by three electrical motors separately and corresponding to the stretched length, the bending angle, and the yaw angle of the three-chamber soft actuator. Thus, the three outputs of the soft actuator can be independently controlled. Theoretical analysis is implemented to provide basis for machine design. A prototype is fabricated and experiments under both of static and dynamic conditions are carried out to evaluate the performance of the proposed concept. The results show that the electrohydraulic control device can effectively drive the three-chamber soft actuator and reduce the difficulty of controller design.

@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.

Continuous-time dynamic solvers have emerged as a viable alternative for online optimization of model predictive control (MPC). This class of solvers emulates the behavior of MPC by tracing its solution trajectories to the Karush-Kuhn-Tucker (KKT) optimality point. In this paper, we develop an analog circuit architecture for the hardware realization of such dynamic controllers. Using the gradient dynamics as a case study, we develop a prototype solver using a Field Programmable Analog Array (FPAA) and we validate its efficacy on a quadruple water tank 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.

In this study was investigated of possibility using the recorded micro tremor data on ground level as ambient vibration input excitation data for determination and application Operational Modal Analysis (OMA) for GFRP benchmark structure. As known OMA methods (such as FDD, EFDD, SSI-UPC/SSI-PC/SSI-CVA and so on) are supposed to deal with the ambient responses. For this purpose, analytical and experimental modal analysis of GFRP benchmark structure for modal properties was evaluated. 3D Finite element model of the building was evaluated SAP2000 for the GFRP benchmark structure based on the design drawing. Ambient excitation was provided from the recorded micro tremor ambient vibration data on ground level. Enhanced Frequency Domain Decomposition (EFDD) is used for the output only modal identification. From this study, very best correlation is found between mode shapes and frequencies. Natural frequencies and analytical frequencies in average (only) %1.24 are differences.

In this study was investigated of possibility using the recorded micro tremor data on ground level as ambient vibration input excitation data for investigation and application Experimental Modal Analysis (EMA) on the bench-scale earthquake simulator (The Quanser Shake Table) for model wind tunnel. As known EMA methods (such as EFDD, SSI and so on) are supposed to deal with the ambient responses. For this purpose, analytical and experimental modal analysis of a model wind tunnel for dynamic characteristics was evaluated. 3D Finite element model of the building was evaluated for the model wind tunnel based on the design drawing. Ambient excitation was provided by shake table from the recorded micro tremor ambient vibration data on ground level. Enhanced Frequency Domain Decomposition is used for the output only modal identification. From this study, best correlation is found between mode shapes. Natural frequencies and analytical frequencies in average (only) 2.5% are differences.

In order to improve the influence of the unmanned helicopter’s own inherent noise and actual environmental disturbance on the system operation, and considering the limitation of algorithm development in LabVIEW semi-physical simulation platform, a control method of LabVIEW and MATLAB hybrid programming is proposed. The method uses MATLAB to write LQG control algorithm program, calls MATLAB script through MATLAB Script node in LabVIEW, and designs LQG controller to achieve position tracking of unmanned helicopter pitch angle and yaw angle. Using this hybrid programming method, the human-machine interface of the unmanned helicopter system can be built in the front panel of LabVIEW, and the position changes and error magnitude of the pitch and yaw angles can be observed in real time. The experimental results show that the application of LabVIEW and MATLAB hybrid programming to unmanned helicopter system simulation has good experimental analysis effect, and the LQG controller designed by this method can effectively filter the inherent noise and environmental noise of unmanned helicopter system, which improves the stability and robustness of the system.

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.

The autonomous vision-based Unmanned Aerial Vehicles (UAVs) landing is an adaptive way to land in special environments such as the global positioning system denied. There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process. Specifically, the landing UAVs are categorized into level I and II. The deep learning method will be implemented for level II UAV. The YoloV4 deep learning method will be implemented by the Level II UAV to achieve object detection of Level I UAV. Once the Level I UAV’s landing has been detected by the onboard camera of Level II UAV, it will move and land on a relative landing zone beside the Level I UAV. The experiment results show the validity and practicality of our theory.