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This paper proposes a novel method for active collision avoidance to protect the human who enters a robot’s workspace in a human-robot collaborative environment. The proposed method uses a somatosensory sensor to monitor the robot’s workspace and detect anyone attempting to enter it. When someone enters the workspace, a Kinect detects and calculates the position of his or her skeleton points in real-time. However, due to the measurement errors and noise of the device, the tracking error increases over time. Therefore, the proposed method applies an improved particle filter (IPF) to accurately estimate the position of the skeleton points. In order to detect the human-robot collision in real-time, the proposed method uses cylinders to establish the bounding box model for human bones and robots and the human-robot collision is replaced by the collision between the cylinders, greatly improving the efficiency of collision detection. Moreover, taking human safety and productivity into account, the robot velocity control is carried out based on the distance between the robot and human. Then, the proposed method uses a rule-based logic system to analyze human motion so that the robot can take appropriate measures to avoid humans. Finally, the dynamic roadmap (DRM) approach plans new paths in real-time to allow robots to bypass humans. By actively avoiding collisions, the proposed method ensures that the robot will never touch the human body. The significant advantage of the proposed method is that it can detect humans in real-time, analyze their behavior and protect humans without any modification to the robot. The proposed method has been tested in practical applications, and the results show that it can successfully guarantee the safety of people entering the robot’s workspace.

A robust asynchronous switching fault-tolerant control method is proposed to solve the problems of uncertainties, unknown disturbances, time-varying delays and partial actuator failures in multi-phase batch processes. Firstly, an asynchronous system composed of subsystems with different dimensions including stable and unstable case is established to describe such multi-phase batch processes more accurately. Then introducing the output tracking error, the established switching model of different dimensions is extended. On this basis, a robust asynchronous switching fault-tolerant control law is designed, which improves the system’s ability to cope with negative factors such as actuator failure and can obtain greater adjustment freedom. Secondly, by using relevant theories and methods, the sufficient conditions in the form of linear matrix inequality (LMI) are given to ensure the exponential stability of the system and the asymptotic stability at each phase. By solving these LMIs conditions, the shortest running time under stable case, the longest running time under unstable case and the control law gain of each phase are obtained. Finally, the effectiveness and feasibility of the proposed method are verified with injection molding process.

In order to improve the harmonic compensation performance of active power filter (APF) in distribution network, based on deadbeat control theory, the command current prediction algorithm and current tracking control strategy are optimized in this article. Firstly, the command current repetitive prediction in abc coordinate system is transferred to dq for improving its accuracy in lead compensation, and the equivalent for fractional delay beat is achieved by Lagrange Interpolation Polynomial to solve the problem of inaccurate prediction caused by grid frequency fluctuation. Then, considering the inherent half-sampling-period delay of sinusoidal PWM (SPWM), an improved deadbeat control strategy for current tracking is proposed by estimating the output current of next sampling period. Because the output current in next sampling period is replaced by that in current sampling period with traditional deadbeat control strategy, this estimation could make up for the defect of low control precision caused by that replacement. After that, adding error repetitive correction into the improved deadbeat control channel to reduce the periodic tracking error of output current. Finally, the stability and accuracy of the improved control system are analyzed theoretically, and its feasibility and effectiveness are verified by the simulation and hardware-in-the-loop (HIL) experiments.

A sensorless control based on the exact tracking error dynamics passive output feedback (ETEDPOF) methodology is proposed for executing the angular velocity trajectory tracking task on the “full-bridge Buck inverter–DC motor” system. When such a methodology is applied to the system, the tracking task is achieved by considering only the current sensing and by using some reference trajectories for the system. The reference trajectories are obtained by exploiting the flatness property associated with the mathematical model of the “full-bridge Buck inverter–DC motor” system. Experimental tests are developed for different desired angular velocity trajectories. With the aim of obtaining the experimental results in closed-loop, a “full-bridge Buck inverter–DC motor” prototype, Matlab-Simulink, and a DS1104 board from dSPACE are employed. The experimental results show the effectiveness of the proposed control.

This paper proposes an adaptive fuzzy observer based fault tolerant controller for a pneumatic active suspension system considering unknown parameters, actuator failures, and displacement constraints. A pneumatic spring is used for a quarter car model to enhance the vibration attenuation performance. Since the pneumatic system contains uncertain nonlinearities, fuzzy logic systems are utilized to approximate unknown nonlinear functions of unmodeled dynamics and various masses of passengers. Besides, a nonlinear disturbance observer is proposed to estimate the effects of the actuator failures, approximation errors, and external disturbances. By utilizing the disturbance estimation and fuzzy approximation techniques, an adaptive fault tolerant control (FTC) is designed to enhance the output performance of the vehicle suspension. Meanwhile, the command filtered scheme is introduced to solve the explosion of complexity problem in the traditional backstepping approach by avoiding virtual controller derivatives. In contrast to previous results, the proposed control can handle the fault tolerant problem and ensure the tracking error of vertical displacement converges into a small-predefined boundary by introducing the prescribed performance function. Moreover, the stability of the closed-loop system is analyzed according to the Lyapunov theory. Finally, comparative simulation examples and experimental studies are performed on the active pneumatic suspension test bench to verify the feasibility of the proposed scheme.

In this paper, the iterative learning control (ILC) problem is investigated for the motion system of an open container with sloshing constraints in industrial fluid packaging. Initially, a broader class of second-order in space and first-order in time linear time-invariant singular distributed parameter system is decomposed by analyzing the motion system of an open container with sloshing constraints. Meanwhile, to eliminate the influence of singular terms on the system, a closed-loop D-type ILC algorithm is designed, and the corresponding convergence conditions are manifested. Then the convergence of the control algorithm is proved strictly. The resulting tracking error of systems can converge to any small tracking accuracy. Finally, a numerical example is given to verify the convergence and effectiveness of the closed-loop D-type ILC algorithm.

This article investigates the adaptive and exponential synchronization problems of fractional-order Takagi-Sugeno (T-S) fuzzy complex networks (FOTSFCN) with coupling time-varying delays and uncertainty. Firstly, the FOTSFCN model is established by embedding T-S fuzzy logic to reduce the complexity and robustness of the FOTSFCN. Secondly, based on the adaptive pinning control strategy, we obtain the asymptotic synchronization and exponential synchronization criteria to achieve synchronous control of FOTSFCN. Furthermore, according to the Lyapunov stability theory, we prove that the FOTSFCN controlled by the proposed controller is robust stable and the effect of coupling time-varying delays and uncertain disturbances on the tracking error can be subsided in view of the linear matrix inequalities (LMIs). Finally, the effectiveness and applicability of the theoretical results are presented through two illustrative examples.

In this paper, an adaptive fuzzy finite-time control problem for pure-feedback switched nonlinear systems with prescribed performance is investigate. By utilizing the prescribed performance technique, we mean that the system output is converge to the predefined bounds. By using the dynamic surface control (DSC) and the backstepping technique, we establish a novel adaptive fuzzy finite-time pure-feedback switching control method. In such a scheme, the Butterworth low-pass filter (LPF) is used to construct the fuzzy observer, which can estimate the immeasurable states and avoid algebraic loop problem. In accordance with the stability theory of common Lyapunov function (CLF), the designed controller can ensures that the tracking error converges to a small neighborhood of the origin in finite time. Finally, an example is given to illustrate this method and verify its validity and effectiveness.

In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with the vertical displacement constraint of sprung mass and actuator saturation. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamic theory to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, the prescribed performance function (PPF) is suggested to guarantee that the tracking error of the sprung mass displacement does not violate the constraint boundaries. Based on the command filtered backstepping control with PPF, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, the comparative simulation examples for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed control.

This paper proposes the trajectory tracking problem between an autonomous underwater vehicle (AUV) and a mobile surface ship, both equipped with optical communication transceivers. The challenging issue is to maintain stable connectivity between the two autonomous vehicles within an optical communication range. We define a directed optical line-of-sight (LoS) link between the two vehicle systems. The transmitter is mounted on the AUV, while the surface ship is equipped with an optical receiver. However, this optical communication channel needs to preserve a stable transmitter-receiver position to reinforce service quality, which typically includes a bit rate and bit error rates. A cone-shaped beam region of the optical receiver is approximated based on the channel model; then, a minimum bit rate is ensured if the AUV transmitter remains inside of this region. Additionally, we design two control algorithms for the transmitter to drive the AUV to the angle of the maximum achievable data rate and maintain it in the cone-shaped beam region and under an uncertain oceanic environment. Lyapunov function-based analysis that ensures asymptotic stability of the resulting closed-loop tracking error is used to design the proposed Non-linear Proportional and Derivative (NLPD) controller. Numerical simulations are performed using MATLAB/Simulink to show the controllers’ ability to achieve favorable tracking in the presence of the solar background noise within competitive times. Finally, results demonstrate the proposed NLPD controller improves the tracking error performance more than 70% under nominal conditions and 35% with model uncertainties and disturbances compared to the original Proportional and Derivative (PD) strategy.

In this paper, the iterative learning control (ILC) problem is investigated for a class of time-invariant parabolic singular distributed parameter systems. Initially, the singular distributed parameter systems is decomposed into infinite number of singular systems based on the separation principle. Meanwhile, the slow-fast subsystems are introduced via singular value decomposition method. Then, a novel mixed PD-type ILC algorithm with finite dimension is designed for the low dimensional slow part and the corresponding convergence conditions are manifested. With the proposed controller, the output error of high dimensional fast complement can satisfy the given value instead of neglecting the effect of high dimensional modes. Furthermore, under the aforesaid ILC law and the appropriate number of the low dimensional slow part, the resulting tracking error of parabolic singular distributed parameter systems can converge to any small tracking accuracy. Finally, simulation results on the distributed building automatic temperature system verify the convergence and effectiveness of the mixed PD-type ILC algorithm.

The intraoperative movement of target organs is currently a key and difficult problem that restricts clinical surgery diagnosis and treatment, especially radiotherapy. Unexpected posture changes can cause the deviation of the target area during the operation, thereby affecting the treatment effect and even causing severe complications. Therefore, it is necessary to track the movement of target organs in real-time and accurately to improve the effect and safety of the surgical treatment. This paper proposes an intraoperative organ motion tracking method based on the fusion of inertial navigation and electromagnetic navigation., which can directly measure the target organ’s movement under non-invasive or minimally invasive conditions using the human natural cavity. The proposed method for identifying the magnetic field’s interference state uses the extended Kalman filter to fuse the inertial measurement unit and the electromagnetic positioning information to achieve target organ movement tracking under different interference conditions. It effectively improved the real-time and robustness of intraoperative organ tracking. Simultaneously, based on the 9-axis inertial measurement unit and a 6-DOF electromagnetic positioning system, the catheter-type target organ tracker was developed, and then under the simulated electromagnetic interference environment, dynamic and static verification experiments were carried out, respectively. The maximum tracking error of displacement and attitude is 2.75 mm and 0.127 rad, respectively, under severe electromagnetic disturbance through experiments. Under non-electromagnetic interference conditions, its displacement and attitude’s maximum tracking errors are 0.94 mm and 0.011 rad, respectively. The results prove that the target organ tracking method based on the fusion of inertial navigation and electromagnetic navigation is feasible. It can realize the tracking and measurement of the target organ’s movement within the clinical permission error range.

The morphing aircraft is a promising solution to achieve an optimal flight performance according to various mission scenarios. To apply the morphing technology in hypersonic vehicles, a variable-sweep-wing morphing waverider was proposed in previous studies, and its aerodynamic performance and dynamic model were presented. In this paper, this morphing waverider is assumed to perform a long-range unpowered glide mission over a wide speed range from hypersonic to subsonic speed. First, the accurate longitudinal dynamic model for the morphing waverider is presented, with all the additional forces and moments that stem from morphing. And a control-oriented longitudinal dynamic model is proposed and it separates the additional morphing forces and moments from derivatives of state variables. Then, an adaptive super twisting sliding mode controller is applied for this morphing waverider to track the optimized trajectory during the entire morphing process. Finally, the overall performance of this controller is examined by nominal condition simulations and Monte Carlo runs. Under the nominal condition simulations, the adaptive super twisting algorithm based sliding mode controller reduces mean tracking error of the pitch angle by 25% compared with the traditional sliding mode controller. In the 500 Monte Carlo runs, the ASTA controller reduces maximum tracking error of the pitch angle by 47% compared with the SMC controller.

Both non-equal trail lengths and non-zero initial errors are practical challenges to learning control of robotic and mechatronic systems. Iterative learning to update input is still desired, because of the repetitive motion nature of the controlled objects. This paper concerns with the adaptive iterative learning control method for performing non-identical tasks. The time scaling technique is applied to normalize non-equal trail lengths, while the error-tracking approach is adopted for coping with initial errors. Theoretical results for performance analysis are presented in detail. The uniform convergence of the tracking error is examined, while boundedness of the variables in the closed-loop is characterized. It is shown that the fully-saturated learning algorithm plays an important role in assuring uniform boundedness of the control input. The proposed control design method does not require the magnitude transformation, and removes the assumption of identical initial conditions. The time scaling technique is verified to be effective in assuring the expected performance, for tracking tasks with non-equal trail lengths and initial errors.

This paper mainly addresses the issue of fast finite-time consensus (FTC) algorithms for a group of high-order uncertain nonlinear non-strict-feedback Multi-agent Systems (MASs) with time-varying asymmetric full state constraints. In order to guarantee the state constraints are satisfied, an appropriate asymmetric nonlinear mapping (NM) is employed to transform the original full state constrained system into a corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory and backstepping recursive design scheme, the uncertainties and non-strict-feedback form are considered to propose a distributed adaptive FTC protocol which can guarantee tracking error reaching a region in finite time. Finally, an example is given to show that the designed control law can perform effective control.

In this article, the problem of robust tracking control is investigated for a class of discrete-time switched nonlinear uncertain system in strict-feedback form, and a novel robust neural tracking control scheme through backstepping technique is proposed for the first time. In order to handle the problem of causality contradiction, the original discrete-time nonlinear switched system is transformed into a special maximum n-step ahead predictor which suits the application of backstepping technique. The radial basis function(RBF) neural networks are adopted to approximate the unknown lumped function at the last step of backstepping procedure, not necessary to approximate all the virtual control laws at each intermediate step. Thus, the process of controller design is significantly simplified in contrast with the existing results. The stability analysis proves the proposed approach guarantees the semi-globally uniformly ultimately bounded(SGUUB) of the closed-loop system, and the tracking error asymptotically converges to an arbitrarily small neighborhood of origin by appropriately designing control parameters. Finally, simulation results are provided to demonstrate the correctness and effectiveness of the proposed scheme.

The paper aims to address output feedback problem for a class of nonlinear systems subjected to unknown dead-zone. High-gain K-filters is firstly designed such that unavailable states of system can be reconstructed. Then, with the help of dynamic surface control (DSC) method, an adaptive output feedback controller design is established, which can alleviate undesired influence of the dead-zone and guarantee the semi-global stability of the system. Furthermore, the $\mathcal {L_{\infty }}$ performance of tracking error is achieved by means of initialization technique. Ultimately, numerical and practical examples clarify the efficiency of the proposed solution.

In this paper, a virtually coupled train formation (VCTF) control method based on the model predictive control (MPC) framework is proposed. The train track spacing error, velocity error, and riding comfort are chosen as the optimization goals, and the constraints of line speed, collision avoidance of the train platoon, traction/braking performance, and string stability are considered. The virtual coupling operation control problem is converted into a quadratic programming problem with constraints. This paper also proposes a coasting control strategy to improve the output of the MPC controller. Simulation results show that compared with the proportional derivative controller, the proposed control method can simultaneously reduce the tracking error and output acceleration, reducing running energy consumption. A study of a metro line is chosen to analyze the VCTF operation performance, and the simulation results show that the VCTF operation mode can improve the peak capacity and quality of service of flat hours compared with the communication-based train control operation mode.

A robust learning control strategy is presented for uncertain linear system with mismatched disturbance. A linear model with both mismatched uncertainty and disturbance is proposed firstly, in which the information of uncertainty and disturbance are unknown. Based on a reference model, a tracking error based linear model is built. What’s more, the input matrix of the linear model is assumed to be suffering from uncertainty. An extended state observer is built to approximate the uncertainty and disturbance online, and then a novel state tracking error based sliding surface is proposed. The stability of the sliding surface is guarantee by the designing parameter. A sliding mode based learning controller is designed for the closed-loop uncertain system. Moreover, a numerical simulation result on hypersonic flight vehicles is proposed to test the presented method.

This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.

This paper investigates command filter backstepping fault-tolerant control for a class of nonlinear systems with nonlinear faults. In particular, the system nonlinear faults are non-affine, which are handled by the filtered signal and fuzzy logic systems approximate Lemma. Command filter is used in this paper, which eliminates the “explosion of complexity” of the classical backstepping and compensates the output of the filter to the derivative of the virtual control. In addition, the controller design procedure has been shown in detail, while the stability of the closed-loop system and the convergence of the tracking error are proved via Lyapunov Theorem. Simulation examples are given to illustrate the feasibility and effectiveness of the proposed scheme.

Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.

This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear time-varying delay systems subject to full state constraints and input saturation. The nonlinear state-dependent functions (NSDFs) are introduced to handle the asymmetric time-varying full state constraints without feasibility conditions. To cope with the unknown time-varying delays, the radial basis function neural networks (RBF NNs) and the finite covering lemma are utilized. Avoiding the use of the Lyapunov-Krasovskii functionals (LKFs), no restriction on the derivative of the time-varying delays is needed. Meanwhile, the effect of the input saturation is eliminated by the augmented function with an auxiliary control signal. The adaptive finite-time tracking controller with only one adaptive parameter is constructed by applying the command filter approach and backstepping technique. It is proved that the proposed controller can ensure that all signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed scheme is demonstrated by two simulation examples.

In this paper, the tracking problem for a class of nonlinearly parametric time-delay systems with arbitrary initial state errors and iteration-varying reference trajectories is studied. An adaptive iterative learning control scheme is developed by using Lyapunov approach. A time-varying boundary layer is constructed for removing the zero initial error condition, which is necessary in normal iterative learning control algorithms. The time-varying nonlinearly parametric uncertainties and time-delay uncertainties are well compensated by using parameter separation technique, signal replacement mechanism and robust strategy. As the iteration number increases, the state tracking error asymptotically converges to a tunable residual set. In the end, a simulation example further verifies the theoretical results.

This paper focuses on particle swarm optimization algorithm (PSOA)-based $\text{H}\infty $ tracking fault-tolerant control for batch processes to resist the influence of actuator faults and unknown disturbances. First, according to a given actual process model, by introducing output tracking error, state difference and new states including output tracking error, an extended equivalent model is constructed. Then, a linear-quadratic performance function is introduced. By using the PSOA to adjust those parameters in the function, a new state space $\text{H}\infty $ tracking fault-tolerant control law is proposed under optimal control theory. Actuator faults are regarded as uncertainties here. The Lyapunov stability theory is used to solve the allowable disturbances in a certain range. The greatest merit of this design is that it has better tracking performance and stronger anti-fault and interference ability. Finally, the injection molding process and nonlinear batch reactor are taken as examples to compare with the genetic algorithm method (GA) and the traditional control method (TC), which shows that the method proposed is more practical and effective.

This paper studies the transient and steady performance constraints on flexible joint manipulator system with exogenous disturbance, and proposes a fixed-time prescribe performance tracking control method based on the singular perturbation model. For the fast subsystem, a subcontroller with superior transient performance is proposed that guarantees the suppression of flexible vibration in fixed-time. The remaining slow subsystem can be linked with rigid methods to achieve output constraints, a type of time-varying error constraint boundary is first constructed, which broadens the initial error constraint boundary of the time-varying tangent barrier Lyapunov function. Then, a slow subcontroller based on a non-singular fast-integration terminal sliding mode is constructed to make any initial tracking error converge in fixed-time. Finally, a nonlinear disturbance observer is used to counteract the exogenous disturbance. The simulation results show that the controller achieves the prescribe performance.

For the state consensus tracking of the singular multi-agent system, under the condition of the communication topology randomly switching only along time axis but unchanging along iteration axis, a distributed iterative learning control protocol is proposed. By singular value decomposition method, the singular multi-agent system is transformed into differential algebra system, and thus the state of the system is accordingly divided into two parts. Then applying the derivative of the tracking error for the first part of the state and the tracking error of the second part of the state and combining the switching topology graph, the distributed iterative learning control protocol is constructed. Furthermore, the convergence of the proposed algorithm is proved by the compression mapping method, and the convergence conditions of the algorithm are obtained. The proposed algorithm can make the state gradually approach the desired state with the increase of iterations. When the number of iterations is sufficient large, the state of each agent can completely track the desired state over a finite time interval. Finally, the simulation examples are given to further validate the effectiveness of the proposed algorithm.

The energy consumption of network interfaces in networked control systems (NCSs) must be reduced for sustainability. The sleep mechanisms of energy-efficient network interfaces for networked motor control systems, in which controller and motor devices are connected by communication networks, have attracted much interest in recent years. They reduce the energy consumption of network interfaces by making the transmitters installed on the controller and motor devices enter a sleep period when no data are to be transmitted. In a conventional send-on-delta (SoD)-based sleep control method, the network interfaces decide when to enter the sleep period based on the variations of the control input and response values. Although this method effectively reduces the communication rate for reducing energy consumption, it does not explicitly consider the quality of performance (QoP) or control performance such as tracking errors. In high-precision motion control systems, ensuring a certain QoP level is crucial. This study proposes a QoP-aware sleep control method for energy-efficient networked motor control systems to maintain the QoP at a certain level while operating the sleep mechanism. Here, the QoP is defined as the tracking error between the command and the response values. In the proposed method, network interfaces decide when to enter the sleep period based on the tracking error. Experimental results confirm that the QoP-aware sleep control method outperforms the SoD-based sleep control method in terms of the tracking errors (i.e., integral square error and steady-state error) at the expense of increased communication rates.

Aiming to solving the nonlinear coordinated trajectory tracking control problem of multi-USVs under the influence of slowly changing and uncertain environmental disturbances, reducing energy consumption and ensuring tracking performance and stability, a distributed event-triggered adaptive coordinated trajectory tracking controller (DET-ACTTC) for multiple unmanned surface vessels (multi-USVs) is proposed based on the undirected communication topology. By introducing a virtual leader, a leader-follower formation is used to generate the coordinated tracking errors. Based on the expected formation and states, the aggregate tracking errors are defined, and event-triggered measurement errors are constructed to design the event-triggered conditions. An adaptive term is used to compensate for external environment disturbances, and the DET-ACTTC is deduced for each USV in the group. The tracking errors of the event-triggered coordinated trajectory tracking control (ETCTTC) system gradually converge to zero, and Zeno behavior is avoided. The theoretical results are verified by simulations.

This paper is dedicated to addressing the predefined-time consensus tracking control problem for unknown high-order nonlinear multiagent systems. The prominent difference compared with some existing papers is that the follower is modeled in the form of non-strict-feedback structure. Meanwhile, instead of being constants, the real control gains are unknown functions. A distinct advantage in our work is that the outputs of followers are able to track the output of leader within the time specified in advance. In order to get our desired predefined-time controller, radial basis function (RBF) neural networks (NNS) are applied to compensate those unknown nonlinearities. Then in the design framework of adaptive backstepping, the predefined-time virtual control laws are presented and their derivatives are approximated by using finite-time differentiators. Under our proposed predefined-time controller, it is rigorously demonstrated that the whole closed-loop system remains stable and all outputs of the followers track the reference signal in predefined time. In the end, a simulation example is given to ulteriorly verify the efficacy of the suggested predefined-time control scheme.

A filter and neural network (NN) based fault tolerant control (FTC) strategy is developed for a family of nonlinear systems expressed in strict feedback form in the event of unknown system dynamics and actuator failures. Specifically, adaptive neural network (ANN) is first utilized to facilitate the state observer design such that unmeasurable system states can be obtained. Note that ANN is only used when designing state observer instead of being used when designing controller. In our method, filter technique is introduced to construct virtual control inputs, which can not only reduce the adverse effects caused by ANN approximation errors and state estimation errors, but also deal with the expansion problem of the differential terms. Moreover, the fault tolerant tracking controller is designed by combining backstepping technique with the proposed NN with a novel weight updating law that is different from the above ANN. Theoretical analysis and simulation results demonstrate that the proposed FTC strategy can ensure that the tracking error converges to a small region of zero when there exist actuator faults and unknown system dynamics.

This paper addresses the 3D trajectory tracking problem of a novel underactuated underwater vehicle with 4 propellers. A 5-DOF kinematic and dynamic model of the quadrotor underwater vehicle (QUV) is established based on the underwater vehicle and quadrotor unmanned aerial vehicle movement mechanism. A double-loop control structure is then developed. By constructing a Lyapunov function for the outer-loop controller, a 5-DOF trajectory tracking error equation and a velocity virtual control law are obtained. The inner-loop controller is built based on nonsingular integral terminal sliding mode control (NITSMC). Finally, the effectiveness and robustness of our control algorithm are demonstrated through numerical simulations. In particular, given a smooth, second-order derivable 3D reference trajectory, the QUV can quickly track the trajectory and satisfactorily converges in the neighborhood of the expected value in a finite time via NTISMC, which verifies its superiority compared with the other popular backstepping control (BSC) method.

This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcement learning and adaptive dynamic programming (ADP). Different from existing literature, the load is treated as a dynamic uncertainty and a robust optimal state-feedback controller is proposed. The stability of the inverter-load system has been strictly analyzed. In order to obtain accurate output current differential signal, this paper designs a tracking differentiator. It is ensured that the tracking error asymptotically converges to zero through the proposed output-feedback controllers. A standard proportional integral controller and linear active disturbance rejection control strategy are also designed for the purpose of comparison. The simulation results show that the proposed controller has inherent robustness and does not require retuning with different applications.

This paper proposes an adaptive sliding mode control strategy based on RBF (Radial Basis Function) neural network for the supercavitating vehicle system with model uncertainties and external disturbance. Aiming at the unknown items in the model, the control strategy compensates the unknown model uncertainty and external disturbance through the RBF neural network, and derives the neural network weight update strategy according to the Lyapunov stability theory which can guarantee the closed loop system asymptotic stability. The simulation results show that this control strategy can enable the supercavitating vehicle to track reference signal when there are model uncertainties and external disturbance, and ensure the convergence of the trajectory tracking error. Compared with the control input of the sliding mode control strategy without RBF neural network, the control input of the adaptive sliding mode control strategy with RBF neural network is also reduced, which further verifies the effectiveness of the RBF adaptive sliding mode control strategy proposed in this paper.

This paper proposes an adaptive integral-type terminal sliding mode tracking control approach based on the active disturbance rejection for uncertain nonlinear systems subject to input saturation and external disturbances. Its main objective is to achieve zero tracking error in the presence of external disturbances, parametric uncertainties and input saturation; ubiquitous problems in most practical engineering systems. The proposed approach combines the robustness and chattering-free dynamics of adaptive integral-type sliding mode control with the estimation properties of a nonlinear extended state observer. It also assumes the bounds of the input saturation to be unknown. The asymptotic stability of the closed-loop system in the presence of disturbances, uncertainties and input saturation is proven using the Lyapunov theorem. The effectiveness of the proposed approach is assessed using a flexible-link robotic manipulator. The obtained results confirmed the robustness and god tracking performance of the proposed approach. Robustness, chattering-free dynamics and good tracking performance albeit input saturation are among the main features.

A novel adaptive tracking control strategy without singularity is proposed for quadrotor systems subject to uncertain dynamic parameters. Based on the design technique of bounded control, a new differentiable saturated function is proposed and subsequently adopted to ensure that the developed translational thrust remains within the preassigned range in the position control loop. A technical lemma is established to analyze the relationship between the convergence of the attitude tracking error and the position tracking error in the presence of thrust saturation. Then, by the compensation term in the form of an attitude error function, a singularity-free torque controller, which can avoid analytic calculations involving the desired attitude derivative, is presented in the attitude control loop. Finally, we prove that the proposed method can achieve asymptotic tracking without singularity. Simulation results illustrating the effectiveness of the proposed strategy are exhibited.

This paper proposes an artificial intelligence integrated (AI) fractional order robust control for a DFIG based wind energy conversion system. To reduce the chattering phenomena in the excitation signal, fuzzy system is employed for the adaptive adjustment of the discontinuous control gain while preserving the robustness of the closed-loop system. The stability of the closed loop system with fuzzy fractional order robust control (FFORC) is ensured using fractional order Lyapunov system. The proposed FFORC control scheme is tested using processor in the loop (PIL) experiment.MATLAB/Simulink environment is used to emulate DFIG based wind energy system and a Texas Instrument (TI) DSP320F37D processor is used for interfacing the proposed control scheme with the emulated DFIG model in Simulink environment. System performance under the proposed FFORC scheme is compared with classical sliding mode control(SMC).The experimental results justifies the superiority of the proposed FFORC control scheme under all test conditions.Under ideal condition and with the proposed FFORC control scheme, the speed tracking error is approximately zero while with SMC method the peak tracking error is 0.4 radian/s. Similarly the active and reactive powers tracking is smooth with the proposed control system, while with SMC method the reactive power oscillates on both sides of the reference and it reaches 0.01 kVAR on positive side and -0.01kVAR on the negative side of the plot.Under parameters variation, system with FFORC control scheme offers minimum steady state error which is about 0.01 radian/s, while in case of SMC with saturation function a peak value of 0.6 radian/s is recorded. In case of SMC with sgn function, the speed tracking error is around 0.1 radian/s.Moreover the proposed FFORC scheme exhibits minimum chattering.

This paper presents a feedback controller for automated vehicles with time-varying external disturbance and uncertain nonlinearity. The contribution of this paper is threefold. First, a prescribed performance function with fixed time convergence characteristic is developed to construct a feedback controller, in which the fixed convergence time and convergence accuracy are preset. Second, an event-triggered strategy is designed to reduce the communication data in the controller-to-actuator channel. Third, a simple feedback controller is proposed for steering control of the automated vehicles, which eliminates the system uncertainty and the influence of external interference on the control performance. The contradiction method proves that the tracking error can be guaranteed to be within the preset range. Moreover, the numerical simulations and experiments domenstrate the effectiveness of the proposed method.

Piezoelectric actuators (PZTs) are essential elements in high-precision systems. However, the hysteresis nonlinearity introduced by PZTs degrades the control accuracy. In this paper, a high-order terminal sliding mode enhanced hysteresis observer is designed, which compensates the error between the Bouc-Wen model and the actual hysteresis. Subsequently, a novel terminal sliding mode control (TSMC) is proposed. Unlike the conventional TSMC, the novel TSMC method makes the tracking error in the sliding mode converge to the origin within a finite time regardless of the initial conditions, which improves the performance of conventional TSMC. The stability of the proposed control method is verified through the Lyapunov theory. Finally, simulations and experiments are implemented to validate the effectiveness of the proposed control method. Experimental results demonstrate that the proposed method has a more superior performance than conventional TSMC.

This paper introduces an intelligent machining system (IMS) using an adaptive-network-based fuzzy inference system (ANFIS) predictor and the particle swarm optimization (PSO) algorithm with a hybrid objective function. The proposed IMS provides suitable machining parameters for the users, to satisfy different machining requirements such as accuracy, surface smoothness, and speed. First, the key computer numerical control parameters are selected, and the actual trajectories under different machining parameters obtained by linear scales are collected. These data are analyzed to obtain the machining time, contouring error, and tracking error, corresponding to the speed, milling accuracy, and surface smoothness, respectively. Second, a data-driven approach using ANFIS is established to obtain the corresponding relationship model between the machining parameters and three aforementioned performance indices. Subsequently, to establish the IMS, we combine the trained ANFIS model and establish a hybrid objective function optimization problem solved by PSO algorithm according the specific requirement of the user. Finally, the performance and effectiveness of the proposed machining system is demonstrated by experimental practical machining.

Lane keeping control needs to achieve smooth steering operation while ensuring safety-no lane departures and maintain small lateral displacement. In this paper, we solve this challenge by developing a preview lane keeping control supervised by a safeguard controller. The preview control utilizes both tracking errors and future lane curvatures to generate the optimal steering commands. The safeguard controller is then designed to guarantee bounded tracking errors. It supervises the preview control and intervenes if and only if the tracking error is approaching the safety boundary. Both algorithms have analytical control laws and thus require little on-line computations. The integrated system is compared with a model predictive control (MPC) design in terms of both tracking performance and computing efficiency. We implemented the proposed controls on a self-driving vehicle platform and tested on open roads and in the Mcity test facility.