In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

Metaheuristics is an interesting research area with significant advances in solving problems with optimisation. Substantial advancements in metaheuristic are being made, and various new algorithms are being developed every day. The analyses in this area will undoubtedly be helpful for future improvements. This paper's main objective is to conduct a literature review of some recent algorithms motivated by nature to compare their features. This paper reviews some recently published nature inspired algorithms such as squirrel search algorithm (SSA), improved squirrel search algorithm (ISSA), grey wolf optimiser (GWO) algorithm, random walk grey wolf optimiser (RW_GWO) algorithm, sailfish optimiser (SAO) algorithm, sandpiper optimisation algorithm (SOA), search and rescue operations (SRO) algorithm, slime mould optimisation (SMO) algorithm, grasshopper optimisation algorithm (GOA) and opposition based learning grasshopper optimisation algorithm (OBLGOA). This paper focuses on a brief introduction of these algorithms and key concepts involved in formulation of swarm intelligence. Finally, this work outlines the directions for conducting effective future research.

This paper deals with high-order unstable systems, which are dangerous and more difficult to control. Their presence is increasingly prevalent, posing a great challenge to both traditional PID-based industrial designs and various advanced control strategies which are difficult to implement on common industrial control platforms. In this paper, the generalized desired dynamic equational (G-DDE) PID controller, developed by authors earlier, is proposed as a viable alternative. In addition to guarantee the closed-loop stability, its simple structure and tuning procedure are specifically appealing to practitioners. Simulations and experimental results show advantages of G-DDE PID in reference tracking, disturbance rejection and robustness, thus making G-DDE PID a convenient and effective control strategy for high-order unstable systems, readily implementable on common industrial platforms.

This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%.

A major challenge in the online delivery of engineering courses is the implementation of the lab component. With most campuses being closed due to COVID-19 pandemic, many of the engineering courses’ labs are either cancelled, converted to a totally virtual, or remote lab setting. The paper proposes a novel hybrid virtual-physical laboratories for a control theory course that is delivered online. The virtual labs are implemented using a 3D animated digital twin of a motor. The physical labs are implemented using a low-cost take-home lab kit that was sent to students.

The use of the proposed hybrid approach achieves the benefits of both virtual labs and physical labs. Specifically, the virtual labs form a sandbox where the student can safely experiment and try new designs without worrying about damaging equipment. This will also form a gentle introduction to the utilization of the physical labs. The physical labs allow the student to see the actual control system components, and hardware troubleshooting.

With the acceleration of industrialization and informatization, industrial cyber-physical systems (ICPSs) have played an increasingly important role in industrial applications. However, due to the deep integration of transmission network, ICPSs are inevitably exposed to cyber attacks. This paper investigates the problem of secure state estimation for nonlinear ICPSs with multiple transmission channels subject to denial-of-service (DoS) attacks. With the help of a multi-gain switching mechanism and the Takagi-Sugeno (T-S) fuzzy models, a new nonlinear resilient observer scheme is proposed and the resilience is quantified by characterizing attack duration and frequency. Specifically, to improve the resilience against DoS attacks, the observer gains are delicately designed for different attack patterns. Compared with the existing results which focus on the linear ICPSs with single transmission channel subject to cyber attacks, the considered secure estimation problem for nonlinear ICPSs with multiple transmission channels is more challenging and has greater practical significance. Finally, the effectiveness of the presented scheme is illustrated with the simulation results on a single-link rigid robot system and the experimental results on a rotary inverted pendulum built in a hardware-in-the-loop testing platform.

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation controller (NSOFC) which is a combination of a cerebellar model articulation controller (CMAC) and sliding mode control (SMC). We also present a new Gaussian membership function (GMF) that is designed by the combination of the prior and current GMF for each layer of CMAC. In addition, the relevant data of the prior GMF is used to check tracking errors more accurately. The inputs of the proposed controller can be mixed simultaneously between the prior and current states according to the corresponding errors. Moreover, the controller uses a self-organizing approach which can increase or decrease the number of layers, therefore the structures of NSOFC can be adjusted automatically. The proposed method consists of a NSOFC controller and a compensation controller. The NSOFC controller is used to estimate the ideal controller, and the compensation controller is used to eliminate the approximated error. The online parameters tuning law of NSOFC is designed based on Lyapunov’s theory to ensure stability of the system. Finally, the experimental results of a 2 DOF robot arm are used to demonstrate the efficiency of the proposed controller.

Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies, such as are applied for disorders affecting lymphatic and blood circulation. In this paper, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We first demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. We second demonstrate the utility of this garment for dynamic massage therapy. These findings show the potential of such a wearable robot for the treatment of several health disorders associated with lymphatic and blood circulation, such as lymphedema and blood clots.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

This article presents the design of a fault diagnosis strategy to deal with faults in multiple actuators in a quad-rotor under the influence of external disturbances. The faults are modeled as partial loss of effectiveness. The proposed fault diagnosis strategy is based on a finite-time sliding-mode observer that estimates the full state and provides a set of residuals using only the output information. Moreover, such a strategy is able to detect, isolate, and identify faults in multiple actuators despite the presence of external disturbances. Experimental results on the Quanser’s QBall 2 platform show the performance of the proposed scheme.

In this paper, the attitude tracking control problem of a rigid body is investigated where the states are quantized. An adaptive backstepping based control scheme is developed and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector state 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 proposed control scheme.

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method.

In this paper, an adaptive fuzzy tracking control scheme by combining Lyapunov stability theory and backstepping technique is proposed for a quadrotor unmanned aerial vehicle. First, a fuzzy logic system is used to approximate the unmodeled dynamics of the quadrotor system. Second, command filtering is utilized to compute the derivatives of the virtual control signals to avoid the complex analytic derivation of these derivatives. Third, a nonlinear disturbance observer is applied to compensate for external disturbance and approximation error of the fuzzy logic system. Lyapunov stability analysis shows that the quadrotor system can be stabilized by the proposed controller with high control accuracy. Finally, the experimental results are given to verify the effectiveness of the proposed control strategy.

This paper proposes an adaptive lane change trajectory planning scheme to road friction and vehicle speed for autonomous driving, while considering both the maneuver safety and the comfort of occupants. In regard to achieve smooth trajectory, a 7th-order polynomial function is constructed to ensure continuity of the planned trajectory up to the derivative of the curvature (jerk). Unlike traditional planning methods that only consider very limited maneuvering conditions, the proposed scheme adapts to a wide range of road friction and vehicle speed, while ensuring enhanced occupants' ride comfort and acceptance. The proposed trajectory planning scheme creatively integrates all the dynamic constraints which are defined by road friction, safety, comfort and human-like driving style. It is shown that the proposed lane change planning algorithm reduces to the identification of exclusively the lane change duration given a constant forward speed. Illustrative simulation examples in MATLAB/Simulink have been conducted to demonstrate the validity of the proposed scheme. The acceptable traceability of the planned lane change trajectories is further demonstrated through path tracking analysis of a full-vehicle model in CarSim. Finally, experimental tests have been conducted based on Quanser's latest self-driving car (QCar) to verify the practical effectiveness of the proposed trajectory planning scheme.

This paper investigates an adaptive neural network control strategy for a two-degree-of-freedom helicopter system with input saturation and unknown external disturbances. Firstly, the radial basis function neural network is used to compensate the uncertainty and input saturation error of the system. Furthermore, a disturbance observer is designed to deal with complex disturbances composed of unknown disturbances and neural network errors. By constructing and analyzing the Lyapunov function, the stability of the helicopter system is strictly guaranteed. Finally, the numerical simulations and experiments conducted on the Quanser laboratory platform reveal that the proposed control strategy is suitable and effective.

This study considers an adaptive neural control for a two degrees of freedom helicopter nonlinear system preceded by system uncertainties, input backlash, and output constraints. First, a neural network is adopted to handle the hybrid effects of input backlash nonlinearities and system uncertainties. Subsequently, a barrier Lyapunov function is introduced to limit the output signals for further ensuring the safe operation of the system. The bounded stability of the closed-loop system is analyzed employing the direct Lyapunov approach. In the end, the simulation and experiment results are provided to demonstrate the validity and efficacy of the derived control.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

The helicopter can play an important role in military and civil applications owing to its super maneuvering ability, which is closely related to its control system. To improve control performance, this study presents an adaptive sliding mode control strategy merging an adaptive neural network for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. By setting up the Lyapunov function, the asymptotic stability of the closed-loop system is guaranteed, the astringency of the neural network weight renewal course is pledged, and the asymptotic attitude adjustment and trajectory tracking for the desired set point are realized. The availability of the adaptive radial basis function sliding mode control is finally verified via the simulation and real implementation on a nonlinear 2-DOF helicopter platform.

Natural disasters such as earthquakes and strong winds will lead to vibrations in ultra-high or high-rise buildings and even the damages of the structures. The traditional approaches resist the destructive effects of natural disasters through enhancing the performance of the structure itself. However, due to the unpredictability of the disaster strength, the traditional approaches are no longer appropriate for earthquake mitigation in building structures. Therefore, designing an effective intelligent control method for suppressing vibrations of the flexible buildings is significant in practice. This paper focuses on a single-floor building-like structure with an active mass damper (AMD) and proposes a hybrid learning control strategy to suppress vibrations caused by unknown time-varying disturbances (earthquake, strong wind, etc.). As the flexible building structure is a distributed parameter system, a novel finite dimension dynamic model is firstly constructed by assumed mode method (AMM) to effectively analyze the complex dynamics of the flexible building stucture. Secondly, an adaptive hybrid learning control based on full-order state observer is designed through back-stepping method for dealing with system uncertainties, unknown disturbances and immeasurable states. Thirdly, semi-globally uniformly ultimate boundedness (SGUUB) of the closed-loop system is guaranteed via Lyapunov’s stability theory. Finally, the experimental investigation on Quanser Active Mass Damper demonstrates the effectiveness of the presented control approach in the field of vibration suppression. The research results will bring new ideas and methods to the field of disaster reduction for the engineering development.

Bilateral telerobotic systems have attracted a great deal of interest during the last two decades. The major challenges in this field are the transparency and stability of remote force rendering, which are affected by network delays causing asynchrony between the actions and the corresponding reactions. In addition, the overactivation of stabilizers further degrades the fidelity of the rendered force field. In this article, a real-time frequency-based delay compensation approach is proposed to maximize transparency while reducing the activation of the stabilization layer. The algorithm uses a regulated bound-limited multiple Fourier linear combiner to extract the dominant frequency of force waves. The estimated weights are used in conjunction with the relatively phase-lead harmonic kernels to reconstruct the signal and generate a compensated wave to reduce the effect of the delay. The reconstructed force will then pass through a modulated time-domain passivity controller to guarantee the stability of the system. We will show that the proposed technique will reduce the force-tracking error by 40% and the activation of the stabilizer by 79%. It is shown, for the first time, that through the utilization of online adaptive frequency-based prediction, the asynchrony between transmitted waves through delayed networks can be significantly mitigated while stability can be guaranteed with less activation of the stabilization layer.

Before the pandemic, the United Nations Sustainable Development Goals included cutting learning poverty in half by 2030. The COVID-19 pandemic has had severe negative impact on education throughout the world and set back progress toward this goal. Science, Technology, Engineering and Mathematics (STEM) education include laboratory experiences. Engineering and Technology program Accreditation Agencies deem labs critical to an engineer's education and require it in the criteria for international accreditation. While converting traditional instruction to virtual instruction posed a challenge to all, developing countries faced higher constraints of limited bandwidth, connectivity, and household access to technology. Once the access problems are resolved, universities still have the challenge of providing inclusive access to online laboratory experiments, particularly for engineering students. This paper presents current solutions for online Engineering laboratories and proposes an online lab management system and a federated lab model appropriate for developing countries that the Latin American and Caribbean Consortium of Engineering Institutions (LACCEI) is currently developing and piloting.

In this paper, an integrated fault-tolerant control (FTC) strategy is proposed for unmanned helicopter system under aggressive maneuvers. In order to handle the effect of strong nonlinearities caused by large pitch angle, linear parameter varying (LPV) technique is adopted in the helicopter system modeling. Then, LPV model-based unknown input observer (UIO) design is conducted to simultaneously realize actuator faults and system state estimation, based on which an active fault-tolerant controlleris also constructed. Considering bi-directional robustness interactions between fault as well as state observer and active fault-tolerant controller, an integrated fault-tolerant controller design is further developed. Besides, actuator saturation is also considered in LPV modeling as well as integrated fault-tolerant controller design of the helicopter system. In order to guarantee the robust performance of proposed integrated fault tolerant controller design of helicopter system, energy-to-energy strategy is adopted. And the linear matrix inequality (LMI) toolbox is utilized for gain calculation. Finally, comparative experimental tests are carried out to show the effectiveness and advantages of the proposed FTC strategy.

This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time-varying disturbance observer for a discrete time dynamics nonholonomic autonomous mobile robot (AMR) with external disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air-ground cooperative, a hybrid tracking control
strategy combined with improved model predictive control (MPC) method and multiple harmonics time-varying disturbance observer is presented. Firstly, a time-varying air-ground cooperative tracking control model based on the nonholonomic constraints AMR and quadrotor is established by polar coordinate transformation. Secondly, for external disturbances estimating and solving in practical engineering, a discrete-time multiple harmonics time-varying disturbance observer is designed. A hybrid tracking control scheme of the AMR based on the estimated states and MPC method with kinematics constraints
is proposed, and a relaxing factor is designed to restrain the jump phenomenon of the MPC increment. Finally, experimental results are shown the effectiveness of the proposed control strategy

In this paper, an actuator fault accommodation controller is developed to solve the trajectory tracking problem in Quad-Rotors under the effects of faults in multiple actuators and external disturbances. The faults are modeled as partial loss of effectiveness. The proposed fault accommodation approach is composed of a fault identification module and a baseline robust-nominal controller. The fault identification module is based on a finite-time sliding-mode observer that provides a set of residuals using only the output information. The fault accommodation strategy uses fault identification to partially compensate the actuator faults allowing the usage of a baseline robust-nominal controller that deals with the external disturbances. Numerical simulations show the performance of the proposed control strategy.

In this paper, a fixed-time dual closed-loop attitude control method is investigated for a quadrotor unmanned aerial vehicle. Firstly, a fixed-time adaptive fast super-twisting disturbance observer is presented for estimating the unknown external disturbance. A modified adaptive law is employed based on an equivalent control method to obtain proper observer gains. Secondly, a fixed-time controller is designed by using a universal barrier Lyapunov function to satisfy asymmetric tracking error constraints. Then, a tracking differentiator is utilised to arrange the transition process. Finally, the implementation of the developed method in a quadrotor unmanned aerial vehicle is performed. Through stability analysis and simulation results, the effectiveness and superiority of the proposed fixed-time control method are validated.

In robot manipulators, the joint flexibility may be or not introduced intentionally. Thus, flexible joint robots (FJRs) are useful in aerospace applications and human rehabilitation, for example. Besides, FJRs appear in industrial manipulators. The feedback linearization control has been applied to many mechatronics systems, including FJRs. However, if spring damping between the links and rotors is present, the state feedback linearization design is no longer feasible. In order to overcome this situation, in this paper, an input-output feedback linearization approach is developed to achieve trajectory tracking control of FJRs. The study is complemented with simulation results, which validates the proposed theory. By assuming the presence of spring damping, a comparison between the known state feedback linearization technique and the proposed input-output feedback linearization is given, showing better results for the introduced approach.

Interactive software tools are proven to be very useful techniques with a high impact on control education. These tools make the learning process more interactive and involve students’ participation and focus. The paper describes the features, functionalities, and applications of ‘Advanced CS’, an interactive graphical user interface (GUI) tool for time domain analysis, frequency domain analysis, and designing of the modern linear control system. It covers state-space modeling, time and frequency domain analysis, PID controller designing, state feedback controller designing by using pole-placement and quadratic regulator method, and full-order and minimum-order state observer designing. The tool, whose main features are simplicity, interactive nature, ease of use, and dynamic nature, is developed using MATLAB for the enhancement of control education. Its capability of updating performance, output parameters, and graphical elements immediately on change of system parameters makes it dynamic and makes the iterative process of analysis and designing simple and easy. Its capability to interface with the hardware setup in real-time not only adds to the purpose of control education enhancement but also validates the analysis and design.

This paper presents an approximation-free control scheme for nonlinear helicopter systems with unknown dynamics. Without using the function approximation methods (e.g., neural network (NN) or fuzzy logic system (FLS)), the developed approximation free controller has a simple proportional-like structure, which can reduce the computational complexity, thus suitable for practical applications. Finally, comparative simulations are provided to show the effectiveness and superior performance of the proposed control scheme.

It is hard to realize the bio-inspired structure (BIS) directly in the vehicle suspension due to space limitation, even though the beneficial nonlinearity of BIS for anti-vibration has been extensively proved. This paper adopts active suspension control to take the advantage of BIS on anti-vibration to improve vehicle comfort, i.e., formulating the ideal BIS dynamics model as nonholonomic servo constraints and then designing the control via constraint-following to drive the dynamics of full vehicle active suspension system (FVASS) to follow the servo constraints. It is of novelty for this study to introduce the constraint-following controls (CFC) into the linear FVASS to follow the servo constraints, considering possibly fast time-varying uncertainties including large mismatched portions. The CFC views the nonlinear control problem from a new perspective of servo constraint. This leads to a simple and natural control that is capable of ‘exactly’ following the servo constraints of nonlinear BIS in the second-order form without the requirement of linearization and/or nonlinear cancellation. Furthermore, a robust CFC is designed based on an uncertainty decomposition technique to consider possibly fast time-varying uncertainties including large mismatched portions, which are unavoidable in underactuated systems but are usually ignored or assumed very small in existing studies. The proposed robust CFC is proved to be able to fulfill the control task with controllable constraint-following error by the Lyapunov stability analysis, even in the presence of uncertainties. Experimental and simulation results reveal the validity of the proposed approach in improving vehicle suspension performance.

Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.

Magnetorheological elastomer (MRE) material is a smart material that has attracted many scientists in recent years. MREs are known to change their mechanical properties in the presence of a magnetic field. Therefore, the material is expected to be used in the intelligent vibration system. The system’s stiffness can be controlled so that the natural frequency of the system can be adjusted to avoid resonance. The dynamic properties of the MRE were investigated under different magnetic field strengths and frequencies. These controllable properties can be applied to various applications, such as vibration absorbers and isolators. This study presents the effectiveness of MRE materials in scaled suspension systems.

Core to the subject of control theory is the feedback loop. Using the error between the reference and the measured output signal passing through a tuned controller, the feedback loop can optimize how well a system reaches its desired state. Usage of this method in a system is known as feedback control. There are many approaches in the field of feedback control, all of which aim to reduce the aforementioned error as effectively as possible. One of these is cascade control, which involves nesting at least one additional feedback loop
within another.
The objective of this report is to evaluate the effectiveness of using the cascade control method to control Quanser’s Quanser Aero [1]. More specifically, cascade control will be performed by using the Aero’s wing angle ϕ(t) as the output variable of the outer loop, and the rotational speed of the motors ω (sometimes referred to as ϕ/s) as the output variable of the inner loop. This will be done in a 1DOF (degrees of freedom) configuration, and using only one of the Aero’s motors. Compared to the default of a single loop with the angle as the only output variable, according to Visioli and Antonio [12] this configuration should provide superior disturbance rejection properties. Hopefully this sufficiently improves the performance to justify the additional effort in applying it.
To confirm this, testing various methods of implementing the cascade control system will be necessary. Then, to compare the effectiveness of cascade control over regular feedback control, some methods for implementing single loop feedback control will be tested as well.
This will all be tested using Matlab’s Simulink program. While the Quanser Aero itself naturally operates with continuous-time, the sensors and the the software used operates in discrete-time. The Simulink schemes in this report are all set to fixed-step at 0.002 second intervals.

Restoring and improving the ability to walk is a top priority for individuals with movement impairments due to neurological injuries. Powered exoskeletons coupled with functional electrical stimulation (FES), called hybrid exoskeletons, exploit the benefits of activating muscles and robotic assistance for locomotion. In this paper, a cable-driven lower-limb exoskeleton is integrated with FES for treadmill walking at a constant speed. A nonlinear robust controller is used to activate the quadriceps and hamstrings muscle groups via FES to achieve kinematic tracking about the knee joint. Moreover, electric motors adjust the knee joint stiffness throughout the gait cycle using an integral torque feedback controller. For the hip joint, a robust sliding-mode controller is developed to achieve kinematic tracking using electric motors. The human-exoskeleton dynamic model is derived using Lagrangian dynamics and incorporates phase-dependent switching to capture the effects of transitioning from the stance to the swing phase, and vice versa. Moreover, low-level control input switching is used to activate individual muscles and motors to achieve flexion and extension about the hip and knee joints. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the kinematic and torque closed-loop error systems, while guaranteeing that the control input signals remain bounded. The developed controllers were tested in real-time walking experiments on a treadmill in three able-bodied individuals at two gait speeds. The experimental results demonstrate the feasibility of coupling a cable-driven exoskeleton with FES for treadmill walking using a switching-based control strategy and exploiting both kinematic and force feedback.

In this paper, a cooperative adaptive cruise control (CACC) system is presented with integrated lidar and vehicle-to-vehicle (V2V) communication. Firstly, an adaptive cruise control system (ACC) is designed for the Q-Car electrical vehicle, an autonomous car. Secondly, a CACC system and V2V communication are designed based on a new algorithm to improve the ACC system performance. Lastly, the CACC agent was trained by Deep Q learning (DQN) and tested. The proposed CACC system improved the stability of the vehicle. Experimental results demonstrate that the CACC system can decrease the average inter-vehicular distance of ACC by 44.74%, with an additional 40.19% when DQN was utilized. The vehicles communicate with each other through a WiFi module to transmit information with 1ms latency.

The aim of this paper is to propose a new data-driven control scheme for multi-input-multi-output linear time-invariant systems whose system model are completely unknown. Using a non-minimal input-output realization, the proposed method can be applied to the case where the system order is unknown, provided that its upper bound is known. A workaround against measurement noise is proposed and it is shown through simulation study that the proposed method is superior to the conventional methods when dealing with input/output data corrupted by measurement noise.

Data-driven approaches to the design of control policies for robotic systems have the potential to revolutionize our world. By continuously observing the environment and changes therein, learning algorithms can adapt and improve the control policies based on the observed data. One key requirement for these approaches to work well in real-world applications is data-efficiency: how long it takes to learn a successful control policy. This is a difficult problem for machine learning, because standard algorithms often lack the required data-efficiency for real-world applications. Further, treating the problem purely from a machine learning perspective neglects decades of research and experience from the field of control theory. The goal of this thesis is to combine insights from control theory with methodologies from machine learning to develop highly data-efficient learning algorithms for continuous control problems. The first part of this thesis considers automated controller tuning, that is, finding the optimal parameters for policies based on classical approaches from control theory without human intervention. To this end, we employ Bayesian optimization (BO), a data-efficient method that addresses global, stochastic optimization problems. In its standard formulation, BO makes only few assumptions about the underlying objective function. On the one hand, this makes BO a prime candidate to tackle a wide range of applications, but on the other hand, it limits BO's data-efficiency for the specific application controller tuning. This part of the thesis presents three separate methodologies that aim at alleviating some shortcomings of BO: First, we propose to constrain the search space locally around an initial solution to enable the optimization of high-dimensional control policies whilst retaining data-efficiency. Secondly, we propose to encode environmental conditions during experiments as context variables, which allows sharing of experience from previous experiments and thus accelerate subsequent ones. Thirdly, we consider the issue of exogenous perturbations that act on the policy's parameters, and we therefore require the optimal parameters to be robust with respect to these perturbations. The efficacy of these three methodologies is demonstrated on a wide range of simulated and real-world problems. The second part of this thesis considers a more general framework for the data-driven control approach: model-based reinforcement learning (RL). This particularly data-efficient branch of RL employs a learned model---an approximation to the true environment---to simulate artificial data instead of only relying on real-world interactions. However, the learned model always remains imperfect and as such introduces an error source to the learning problem. Hence, a key challenge in model-based RL is model-bias; small errors in the learned model that can compound when simulating new data and impede the learning process. This part of the thesis presents a novel approach to alleviate the issue of model-bias. Specifically, we use the observed data as time-dependent correction terms on top of a learned model, to retain the ability to simulate new data without accumulating errors over long prediction horizons. These correction terms are inspired by a data-driven branch of control theory: iterative learning control, which we thoroughly compare to model-based RL. Further, we motivate the proposed method from a theoretical perspective and demonstrate that it can drastically improve existing model-based approaches in practice without introducing additional tuning parameters.

In global navigation satellite system (GNSS) denied settings, such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft.

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans
is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In
this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human
interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed
detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform.
The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved
an accuracy of 96%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs

In the present work, a new online fuzzy tuning linear quadratic regulator (FT-LQR) is proposed for balancing and trajectory tracking of the rotary single inverted pendulum. The beauty of the proposed approach is that it tunes the LQR weighting matrices Q and R online by using a fuzzy controller. The idea of the proposed controller is inspired by the designer’s choice of Q and R matrices, where according to the system response, the designer changes the values of the two matrices to achieve the best performance. However, it is not easy to define these two matrices in an integrated manner. Classic methods such as trial and error require intensive efforts, time-consuming and do not guarantee the desired performance. Therefore, this empirical knowledge was applied to a fuzzy controller to adjust the weighting matrices according to the required performance and depending on the system’s state. The FT-LQR is designed through simulation, and further experimental validations using a laboratory prototype verify the correctness of the proposed approach. Moreover, it can ensure a faster response, good stability, and better robustness to external disturbances in the different operating conditions.

Prototyping systems with interconnected components can be a time and resource expensive process. The process consists of three main phases (design, build and analysis) with each having their own associated cost. For the case of electronic circuits, the building phase is the costliest phase among the three, being prone to human errors which causes the circuit to fail. All three phases of the prototyping process are important. However, often a disproportionate amount of time is spent on the build phase due to the difficulty of making and troubleshooting circuits by hand. In this thesis we will discuss a system that delivers students a fast and reliable method to prototype real electronic circuits in a personal laboratory. This system uses a modular hardware architecture that can interconnect electronic components automatically using a developed software. The circuit building system demonstrated that the building phase of a circuit took 17% of the total time spent on the entire prototyping process of such circuit. The automation of the building phase allows users to balance their time between the different phases of prototyping including design, build, and analysis.

This body of research is built around the development of a roaming robot for indoor settings. Quanser's ground robotics system, QBot 2e, was utilized to map parts of a room along with collecting temperature and light intensity data while automatically navigating around various obstacles. Our research includes characterization of drift in obstacle mapping, heading correction, integration of sensors, and pathfinding. The models used were implemented in MATLAB and Simulink using QUARC and run on an onboard Raspberry Pi in the QBot 2e.

We propose a robust tube-based Model Predictive Control (MPC) paradigm for nonlinear systems whose dynamics can be expressed as a difference of convex functions. The approach exploits the convexity properties of the system model
to derive convex conditions that govern the evolution of robust tubes bounding predicted trajectories. These tubes allow an
upper bound on a performance cost to be minimised subject to state and control constraints as a convex program, the solution
of which can be used to update an estimate of the optimal state and control trajectories. This process is the basis of an iteration
that solves a sequence of convex programs at each discrete time step. We show that the algorithm is recursively feasible, converges asymptotically to a fixed point of the iteration and ensures closed loop stability. The algorithm can be terminated
after any number of iterations without affecting stability or constraint satisfaction. A case study is presented to illustrate
an application of the algorithm.

The paper presents a new approach to the design of the classical PD controller for the plant in the closed loop control system. Proposed controller has two unknown adjustable parameters which are designed by the algebraic method in the two dimensional parameter space, by using the newly discovered characteristic polynomial of the row nondegenerate full transfer function matrix. The system relative stability, in regard to the chosen damping coefficient is achieved. The optimality criterion is the minimal value of the performance index and sum of squared error is taken as an objective function. The output error used in the performance index is influenced by all actions on the system at the same time: by nonzero initial conditions and by nonzero inputs. In the classical approach the output error is influenced only by nonzero inputs.
Experimental results confirm that by taking into account nonzero initial conditions, an optimal solution is obtained, which is not the case with the classical method where
optimization is performed at zero initial conditions

In this study, Modified Arithmetic Optimization Algorithm (MAO) is proposed by updating the basic arithmetic optimization (AO) algorithm with different random distribution functions. The AO algorithm is a stochastic swarm-based algorithm that uses main mathematics operators (multiplication, division, subtraction, and addition) during the updating process. In the basic AO algorithm, random coefficients derived according to uniform distribution are used, especially in the generation of the initial population, exploration, and exploitation phases. In this study, these random coefficients are updated with Chi-Square, gamma, logistic, half normal, exponential, normal, extreme value, inverse Gaussian distribution functions. The efficacy of the developed MAO is evaluated using a set of experimental series including global benchmark optimization and real engineering applications named 3 DOF Hover flight system. For global optimization, the proposed MAO algorithm was run according to 100, 500, and 1000 dimensions for 23 different benchmark functions, and the results are compared with each other. As can be seen from the results, the proposed method produced better results than the classical AO results and well-known metaheuristic techniques. It is seen that MAO performs much better, especially in cases where the number of dimensions’ increases. In addition, 3 DOF Hover Experiment sets, which is an important problem in flight control systems, were used for the engineering application of the proposed method. Linear Quadratic Regulator (LQR) control structure is used to control this experiment set. In the LQR control structure, the Q and R matrices must be optimal. A total of 10 parameters were optimized, and the results were compared with Darwinian particle swarm optimization, fractional-order Darwinian particle swarm optimization, and classical AO algorithms. For comparison, first of all, optimization has been made on the simulation model of the system. As a result of this optimization, it was determined that the results of the MAO algorithm optimized according to the half-normal and exponential distribution functions have better control performance. Then, the optimization parameters obtained for the simulation model were tested in real-time 3 DOF Hover systems and it was shown that the results found work in real-time 3 DOF Hover systems.

The ability of drones to navigate through remote terrain and avoid ground-based vehicular traffic has led to an increase in remote sensing studies and drone-based transportation services. Such applications require a payload to be suspended from the drone via a cable which can introduce instability in the drone's flight resulting from the sway of the suspended payload. Therefore, to maintain a stable flight it is key to measure the position of the payload relative to the drone. This study uses a Vicon motion capture system to measure the position of the drone and simulated Light Detection and Ranging (LiDAR) measurements for the position of the payload. LiDAR measurements are simulated using a LiDAR noise model developed from experimental results using the Velodyne VLP-16 sensor. A nonlinear dynamic model of a drone-based single cable-suspended payload is developed and a discrete Extended Kalman Filter is used to obtain an accurate estimate of the position of the drone and the position of the payload relative to the drone.

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM's null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach's feasibility and effectiveness.

In recent years, robots based on fixed base cannot meet the increasingly prominent application needs such as in service and industry. Compared with traditional robots, the mobile flexible manipulator has wider workspace and more flexible characteristics. Therefore, it is gradually becoming the focus of attention. In this paper, the dynamic modeling, analysis and experimental research are investigated by employed MS-DT-TMM (Discrete Time Transfer Matrix Method for Multibody System) for a wheeled mobile robot system with a flexible manipulator. Firstly, the discrete-time transfer matrix method and its solution process are overviewed. Then, the 3-joint flexible manipulator mounted on the wheeled mobile robot is divided into 8 components. According to the idea of modeling method, the system dynamic model is integrated by using the transfer matrix equations of 8 components. Moreover, based on the existing experimental conditions, the dynamic simulation and experimental research of single degree of freedom planar flexible manipulator are carried out. The data results validate the correctness and feasibility of the proposed model. Finally, the numerical simulation research of whole mobile flexible robot is completed. The effectiveness of the model is verified by analyzing the data of wheel forces, links and joints motion, the displacements of the end effector and so on. Further, the comparison of the end errors of the mobile robot carrying rigid link and flexible link in the simulation results demonstrates the necessity of modeling the flexible component in the system.

In recent years, artists and enthusiasts deployed robotic systems that not only replicate the already existing paintings but also develop novel forms of artistic paintings. Mimicking or even trying to match human creativity makes robot art paintings interesting and fascinating. However, fusing robotics with art naturally comes with challenges such as controlling the compliant contact of the robot's end-effector before, during, and after painting on a surface. In order to overcome these challenges, in this project, we study a dynamical compliant contact model that incorporates the dynamical interaction of robot, brush, the transfer of the paint, and compliant interaction between the brush at the end-effector and the canvas. The dynamical model is developed to enable the development of compliant control and paint transfer control systems in order to achieve a smooth, uniform brushstroke on the canvas. We use Quanser 2-DOF serial link manipulator as a robotic system and model the paintbrush as a snubber mechanical system. The proposed model includes the viscous friction force during the panting process, brush deformation, and paint deposition rate from the paintbrush onto the canvas. We show that a simple PID-type controller can be added in order to achieve the desired painting speed and deformation speed of the brush. By applying a higher-level control, the numerical simulations using Matlab/Simulink show that uniform brushstrokes with the desired quality can be maintained.

The stability and fidelity of haptic simulation systems have been studied for a safer and more faithful haptic interaction with virtual environments. The theoretical analysis, which use a second-order model for the actuator does not match well with the experimental results at high sampling rates. In this work, we propose more accurate models and analyze the uncoupled stability and fidelity for a system actuated by a permanent magnet DC motor and its voltage driver when implementing a linear viscoelastic environment. Through theoretical work and experiments, we show that the stability and fidelity of the system are more accurately represented when a high-order dynamic model is used for the motor and its driver.