This study reports the adaptive asymptotic tracking control problem for flexible-joint (FJ) robot systems, the output tracking error can be kept within the prescribed range in the initial stage of system operation, as time approaches infinity, the asymptotic tracking result can be obtained. The prescribed performance function and the positive integrable time-varying function are introduced simultaneously in the control design of FJ robot systems for the first time. The control scheme is designed under the frame of the adaptive backstepping method and command filtered technique, which successfully avoids the problem of complexity explosion. The radial basis function neural networks are used to deal with unknown uncertainties and the adaptive laws are designed to approximate the norms of weight vectors and approximation errors. Finally, the feasibility of the proposed scheme is proved by the simulation and the experiment of the 2-link FJ robot on the Quanser platform.

This study proposes an adaptive quantized fault-tolerant control for a nonlinear two-degree of freedom (2-DOF) helicopter system with an unknown dead zone and actuator failures. First, a hysteresis quantizer is employed to reduce the jittering during signal quantization. Second, a radial basis function neural network is adopted to address the uncertainty in the nonlinear helicopter system. Bounded estimates, smoothing functions, and adaptive parameters are used to compensate for the effects of unknown dead zones, actuator faults, and quantization inputs. Subsequently, an adaptive neural network quantized fault-tolerant control strategy is developed for the nonlinear 2-DOF helicopter system using a backstepping technique. In addition, the closed-loop system is proved to be semi-globally uniformly bounded using rigorous Lyapunov stability analysis. Finally, the effectiveness of the derived control strategy is demonstrated through simulation and experimental results.

We present a data-driven dissipative verification method for LTI systems based on using multiple input-output data. We assume that the supply-rate functions have a quadratic difference form corresponding to the general dissipativity notion known in the behavioural framework. We validate our approach in a practical example using a two-degree-of-freedom planar manipulator from Quanser, with which we demonstrate the applicability of multiple datasets over one-shot of data recently proposed in the literature.

It is recently discovered that the distributed control architectures can be used to drive the agents to desired different positions by using a modified Laplacian matrix. In addition, adaptive control methods are powerful tools that can deal with the presence of unknown control effectiveness. The first contribution of this paper is to theoretically present a new adaptive control for the purpose of achieving desired behaviors in the networked robots in the presence of unknown control effectiveness. The second contribution is to present experimental results in order to demonstrate the efficacy of using distributed adaptive control architectures in the dynamics of multi agent system, where we used Quanser’s QBot-2e’s, differential-drive mobile robot research platforms.

The effective utilization of pneumatic cylinders in precise control applications requires the correct determination of unpredictable, variable, and non-linear friction resistance characteristics. Even though the friction characteristics depend on many structural and operating variables, the existing expensive experimental methodologies are prone to error, time-consuming, and have excessive computational burden. In this study, the full automation of friction force estimation processes has been designed and implemented with MATLAB/Stateflow including the execution of all experimental steps, processing of signals, extraction and classification of data, and estimation of parameters. An operational graphical user interface has been developed for entering the required input values of the experimental procedures, connecting to testing equipment, carrying out experiments, and presenting obtained model parameters. The friction force parameters of three pneumatic cylinders, two of which were identical, have been determined both with the automated method and the manual method. The automated method has produced better results compared to the manual method while reducing the total estimation duration drastically. The pressure tracking performance tests conducted to verify the friction model parameters have exhibited better tracking performances with the automated method results.

In this manuscript a robust stabilization controller for the Furuta’s pendulum in presence of parametric uncertainties is proposed. We used a normal form transformation and partial feedback linearisation to tackle the problem with a cascade system approach, then a continuous sliding mode algorithm stabilize the cascade system. A closed-loop stability analysis is presented to proof asymptotic stability of the Furuta’s pendulum equilibrium point. Also, experiments over a real Furuta’s pendulum are presented to corroborate the theoretical results shown.

This paper investigates a Takagi-Sugeno fuzzy descriptor approach for designing a input delay controller for a rotary inverted pendulum. The rotary inverted pendulum, under-actuated system, is considered a benchmark for verifying the performance of linear or nonlinear control laws in control system theory because this system has nonlinearities, such as gravity and centripetal force. The Takagi-Sugeno fuzzy modeling technique transformed the rotary inverted pendulum system into several local linear models, and a parallel distributed compensation (PDC) based fuzzy controller was designed. By constructing suitable Lyapunov-Krasovskii functionals and utilizing some mathematical techniques, a stabilization criterion for a T-S fuzzy system with input delay was derived in linear matrix inequalities. Finally, the designed controller was experimented on the ‘Quanser SRV-02’ platform compared to the linear controller designed using LMI Toolbox of MATLAB under the same conditions.

In this paper, the development and experimental validation of a novel double two-loop nonlinear controller based on adaptive neural networks for a quadrotor are presented. The proposed controller has a two-loop structure: an outer loop for position control and an inner loop for attitude control. Similarly, both position and orientation controllers also have a two-loop design with an adaptive neural network in each inner loop. The output weight matrices of the neural networks are updated online through adaptation laws obtained from a rigorous error convergence analysis. Thus, a training stage is unnecessary prior to the neural network implementation. Additionally, an integral action is included in the controller to cope with constant disturbances. The error convergence analysis guarantees the achievement of the trajectory tracking task and the boundedness of the output weight matrix estimation errors. The proposed scheme is designed such that an accurate knowledge of the quadrotor parameters is not needed. A comparison against the proposed controller and two other well-known schemes is presented. The obtained results showed the functionality of the proposed controller and demonstrated robustness to parametric uncertainty.

Dear Editor, This letter considers the control problem of an experimental flexible manipulator in position tracking, vibration suppression, and saturation compensation. Based on the backstepping technology and a Nussbaum function, we develop an anti-windup control to restrain the manipulator's vibration, realize the desire trajectory tracking, and eliminate the saturation. Applying the Lyapunov's method, the control system's stability with the proposed control is proved. Finally, the practicability and effectiveness of the control methodology are verified on a Quanser experiment platform.

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.

In this study, a new compressible flow model for small orifice openings in pneumatic proportional directional control valves has been proposed. It is crucial to precisely control pneumatic valves over all control ranges; yet, conventional flow models fail around the closed position of the valve. The main deficit of the existing studies in the literature is to assume constant values for the parameters of the flow model over changing operating conditions. It has been demonstrated that these rough assumptions are insufficient in precisely predicting the mass flow rate, particularly for small orifice openings. An enhanced experimental setup has been introduced to improve the effectiveness of the proposed model. The cracking pressure ratio and parameters of the model have been identified with experimental method. In the proposed model, new empirical coefficients have been established after a thorough investigation of the impact of supply pressure on the flow behavior of the valve. Validation studies of the model in both the filling and exhausting states of the valve have been carried out at various supply pressures and orifice openings, yielding rather promising modeling performances. In validation tests, the real pressure and the pressure produced by new model have been compared, and good agreement has been achieved with 0.0039% absolute error. According to the findings, the proposed improved flow model can be selected in precision pneumatic control applications.

Motivated by the demand of intelligent control that applicable to real industrial systems, this paper develops an innovative neuro controller for remotely operated robotic manipulators. First, the leader-following robotic devices and their communication are introduced. The remotely operated robot can either be controlled by pre-designed controller or humancomputer interaction. Then, a neuro network algorithm is utilized to update PID control gains adaptively. To minimize training time, adaptive moment estimation algorithm is integrated to the neuro PID controller to form a novel control strategy which can be implemented in closed loop. As a result, intelligent algorithms and control methodology can be combined to solve industrial problem. Furthermore, the proposed intelligent controllers are implemented to two cases of remotely operated robot: 1. predesigned motion driven for comparison; 2. Human-computer interaction. Real experimental results can successfully validate the effectiveness of the presented techniques.

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.

An adaptive neural control for uncertain 2DOF helicopter systems with input saturation and time-varying output constraints is provided. A radial basis function neural network is used to estimate the uncertainty terms present in the system. The saturation error and the external disturbance are considered as a composite disturbance, and an adaptive auxiliary parameter is introduced to compensate it. An asymmetric barrier Lyapunov function is employed to address the constraint violation of the system output. The closed-loop stability of the system is then demonstrated by Lyapunov theory analysis. Simulation results demonstrate the effectiveness of the control strategy.

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 develops a neural network control for a non-linear two-degree-of-freedom unmanned helicopter system satisfying prescribed performance constraints is developed. By introducing a prescribed performance function to express the system constraints, a constrained tracking control problem is transformed into an equivalent unconstrained stability problem through error transformations. The neural network controller is designed to ensure that the tracking error converges to a small neighborhood of zero and that the prescribed performance constraints are satisfied. Furthermore, the stability and error convergence of the system are analysed through the Lyapunov direct approach. The effectiveness of the proposed control scheme is verified by simulation and experimental results.

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.

This paper presents the stabilization control of the rotational double inverted pendulum system. It is generally a non-linear, non-minimum phase, unstable, and underactuated system of high order. It has some significant real-life applications such as position control, aerospace vehicle control, and robotics. The objective is to determine the control law to the motor's output torque such that the inverted pendulum motion will be stabilized about a vertical axis and position the rotary arm to a commanded angular position. In this study, multi-PID controllers, Linear-Quadratic Regulator (LQR), and Internal Model Control (IMC) controllers are designed and MATLAB-based simulations are performed to understand and compare the performance of the three control schemes.

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.

Air-to-air refueling (AAR) has been commonly used in military jet applications. Recently, civilian applications of AAR have been garnering increased attention due to the high cost of air travel, which is largely dictated by the cost of jet fuel. There are two types of AAR approaches:
probe-drogue and flying boom systems. This work explores the probe-drogue AAR system in commercial applications. Typical AAR applications deploy a drogue connected to a long flexible hose behind a moving aircraft tanker. The drogue is connected to a probe in a receiver aircraft before initiating fuel transfer and is retracted back into the tanker when the fuel transfer is completed. In order to ensure a safe and efficient refueling operation sophisticated systems need to be developed to accommodate the turbulences encountered, particularly in respect to vibration reduction of the flexible hose and drogue. The objective of this work is to develop a probe-drogue system for helicopter AAR applications. The first project is to make a preliminary design of a new AAR system for helicopter refuelling from a modified AT-802 tanker aircraft.
The second project is to model and control the drogue system under different turbulence
conditions. As the real AAR system is not available, a flexible structure will be used instead to explore strategies for adaptive drogue system control. In order to achieve adaptive control of the flexible structure, an adaptive neuro-fuzzy (NF) controller is developed to suppress the vibration of perturbed flexible structures under variable dynamics conditions. A new hybrid training technique based on the bisection particle swarm optimization (BPSO) algorithm is proposed to optimize the NF controller parameters recursively to accommodate changes to system dynamics.
To solve the issue of controller response to nonlinearities inducing additional vibrations in the steady state solution space, a fuzzy boundary function is proposed to shape the control signal output. The parameters related to output suppression are optimized simultaneously during
recursive NF system training. The effectiveness of the proposed NF controller and hybrid training method is verified by experimental tests under different system dynamics conditions by placing mass blocks at different locations on the flexible beam. Experimental tests have shown that the proposed adaptive NF controller and hybrid training method outperform other related control schemes in terms of overshoot, undershoot, and settling time.

Composite state convergence is a novel scheme applied for the bilateral control of a telerobotic system. The scheme offers an elegant design procedure and employs only three communication channels to establish synchronization between a single-master and a single-slave robotic system. This paper expands the capability of the composite state convergence scheme to accommodate any number of master and slave systems and proposes a disturbance observer-based composite state convergence architecture where k-master systems can cooperatively control l-slave systems in the presence of uncertainties. A systematic method is presented to compute the control gains while observer gains are determined in a standard way. To validate the proposed architecture, MATLAB simulations are performed on symmetric and asymmetric arrangements of single-degree-of-freedom teleoperation systems. Finally, experimental results are obtained using Quanser’s Qube-Servo systems in QUARC/Simulink environment.

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.