Consensus-based total-amount cooperative tracking control for multi-motor locomotive traction system

In locomotive traction system, unavoidable factors (such as idling and skidding) typically lead to the decline of traction performance of one or more motors, thereby resulting in the fluctuation of total torque traction amount. In this paper, the consensus-based total-amount cooperative tracking control (TACTC) is proposed to maintain the consensus of total torque traction amount with the given reference instruction. First, a disturbance observer is employed to estimate uncertain disturbances, then the output torque and observed values are fed back to design the total amount cooperative tracking control protocol, which is used to coordinate traction torque output redundancy of each individual motor. Simulation results show that the proposed approach is effective in reducing tracking time and tracking errors.     

Improved order-reduction method for cooperative tracking control of time-delayed multi-spacecraft network

This article investigates the order-reduction method for multi-spacecraft cooperative tracking control problems considering non-uniform time delays. The tracking error system is constructed as a linear time-varying (LTV) system since the orbit of the reference point is an ellipse. To facilitate the controller design, a model transformation method is proposed to transform the LTV system into a linear time-invariant (LTI) system with norm-bounded uncertainties. By using the sliding-mode control (SMC) technique, a delay-dependent cooperative tracking controller is designed to guarantee multiple followers to track the leader. Then, an order-reduction method is proposed to reduce the order of sufficient conditions in the form of linear matrix inequalities (LMIs), which make sure that the tracking error system is asymptotically stable. A numerical example is finally provided to illustrate the effectiveness of the designed controller and the improved performance of the order-reduction method.     

Optimal tracking performance for networked control systems with quantization

This paper investigates the optimal tracking performance of the multiple-input multiple-output (MIMO) discrete-time networked control systems (NCSs) considering the quantization of communication channel. The tracking performance is adopted for the H₂ square error criterion. The optimal tracking performance expression is obtained by using the co-prime factorization, the partial factorization, the inner–outer factorization and the spectral decomposition methods. Moreover, the paper also includes the exploration of the optimal tracking performance with input power constraint. The obtained results have demonstrated that the optimal tracking performance is influenced by the non-minimum phase zeros, unstable poles and their directions, the reference signal and the quantization interval. Moreover the theoretical results have also been proven using a number of different examples.     

Event-triggered tracking control for couple-group multi-agent systems

This paper mainly investigates the event-triggered tracking control for couple-group multi-agent systems in a disturbance environment, where the topology of the agents is switching. Consensus protocol is designed for the case that some agents reach a consistent value, while the other agents reach another consistent value. Then, event-triggered control laws are designed to reduce the frequency of individual actuation updating for discrete-time agent dynamics. Moreover, by applying the Lyapunov function method, a sufficient condition of couple-group consensus is established in terms of a matrix inequality when the communication topology is switching. Finally, simulation examples are given to demonstrate the effectiveness of the proposed methods.     

Flatness-based adaptive sliding mode tracking control for a quadrotor with disturbances

In this paper, a flatness-based adaptive sliding mode control strategy is presented to solve the trajectory tracking problem of a quadrotor. According to the differential flatness theory, the typical under-actuated quadrotor dynamics is transformed into a fully-actuated one. Based on this model, backstepping sliding mode controllers are designed to solve the trajectory tracking problem. To improve the robustness to disturbances, extended state observers are applied as a feedforward compensation of disturbances. Moreover, considering the high-order dynamics and possible instability caused by large observer gains, the adaptive method is applied to compensate for the estimation error. The effectiveness of the proposed control scheme is verified in simulations.     

Adaptive tracking control for nonlinear heterogeneous multi-agent systems with unknown dynamics

This paper is concerned with the tracking control problem for nonlinear heterogeneous multi-agent systems with a static leader, where the leader’s state is only available to a small portion of follower agents. The considered multi-agent system is composed of first- and second-order follower agents with unknown nonlinearities and unknown disturbances, and the communication graph of follower agents is fixed and directed. A robust adaptive neural network controller is designed for each follower agent. By applying the Lyapunov theory with the singular value analysis method, it is shown that all follower agents will synchronize to the leader agent with bounded residual errors. A numerical example is presented to demonstrate the effectiveness of the theoretical findings.     

Adaptive learning tracking for uncertain systems with partial structure information and varying trial lengths

This paper considers the adaptive iterative learning control (ILC) for continuous-time parametric nonlinear systems with partial structure information under iteration-varying trial length environments. In particular, two types of partial structure information are taken into account. The first type is that the parametric system uncertainty can be separated as a combination of time-invariant and time-varying part. The second type is that the parametric system uncertainty mainly contains time-invariant part, whereas the designed algorithm is expected to deal with certain unknown time-varying uncertainties. A mixing-type adaptive learning scheme and a hybrid-type differential-difference learning scheme are proposed for the two types of partial structure information cases, respectively. The convergence analysis under iteration-varying trial length environments is strictly derived based on a novel composite energy function. Illustrative simulations are provided to verify the effectiveness of the proposed schemes.     

Output tracking control with L1-gain performance for positive switched systems

This paper concentrates on the output tracking control problem with L₁-gain performance of positive switched systems. We adopt the multiple co-positive Lyapunov functions technique and conduct the dual design of the controller and the switching signal. Through introducing a new state variable, which is not the output error, the output tracking control problem of the original system is transformed into the stabilization problem of the dynamics system of this new state. The proposed approach is still effective even the output tracking control problem of any subsystem is unsolvable. According to the state being available or not, we establish the solvability conditions of the output tracking control problem for positive switched systems, respectively. In the end, a number example demonstrates the validity of the presented results.     

Event-triggered dynamic output feedback tracking control for large-scale interconnected systems with disturbances

This paper is concerned with the event-triggered dynamic output feedback tracking control for large-scale interconnected systems with disturbances. For each node, a novel event-triggered mechanism is driven by local relative output tracking error to determine whether the signal will be transmitted. A two-step optimization is applied for dynamic output feedback controller design which guarantees robust stability of the system with an optimal H_∞ disturbance attenuation level. Finally, a simulation example of master-slave multiple vehicles is given to illustrate the effectiveness of the proposed scheme.     

Robust adaptive visual tracking control for uncertain robotic systems with unknown dead-zone inputs

This paper is concerned with the image-based visual servoing (IBVS) control for uncalibrated camera-robot system with unknown dead-zone constraint, where the uncertain kinematics and dynamics are also considered. The control implementation is achieved by constructing a smooth inverse model for dead-zone-input to eliminate the nonlinear effect resulting from the actuator constraint. A novel adaptive algorithm, which does not require a priori knowledge of the parameter intervals of dead-zone model, is proposed to update the parameter values online, and the dead-zone slopes are not required the same. Furthermore, to accommodate the uncertainties of uncalibrated camera-robot system, adaptation laws are developed to estimate the uncertain parameters, simultaneously avoiding singularity of the image Jacobian matrix. With the full consideration of unknown dead-zone constraint and system uncertainties, an adaptive robust visual tracking control scheme together with dead-zone compensation is subsequently established such that the image tracking error converges to the origin. Based on a 3-DOF manipulator, simulations are conducted to verify the tracking performance of the proposed controller.     

Fault tolerant cooperative control for affine multi-agent systems: An optimal control approach

The goal of this paper is to propose an optimal fault tolerant control (FTC) approach for multi-agent systems (MASs). It is assumed that the agents have identical affine dynamics. The underlying communication topology is assumed to be a directed graph. The concepts of both inverse optimality and partial stability are further employed for designing the control law fully developed in the paper. Firstly, the optimal FTC problem for linear MASs is formulated and then it is extended to MASs with affine nonlinear dynamics. To solve the Hamilton-Jacobi-Bellman (HJB) equation, an Off-policy Reinforcement Learning is used to learn the optimal control law for each agent. Finally, a couple of numerical examples are provided to demonstrate the effectiveness of the proposed scheme.     

Distributed RISE control for spacecraft formation reconfiguration with collision avoidance

In this paper, we investigate the distributed formation reconfiguration problem of multiple spacecraft with collision avoidance in the presence of external disturbances. Artificial potential function (APF) based virtual velocity controllers for the spacecraft are firstly constructed, which overcome the local minima problem through introducing auxiliary inputs weighted by bump functions. Then, based on the robust integral of the sign of the error (RISE) control methodology, a distributed continuous asymptotic tracking control protocol is proposed, accomplishing both formation reconfiguration and the collision avoidance among spacecraft and with obstacles. Furthermore, using tools from graph theory, Lyapunov analysis and backstepping technique, we show the stability and collision avoidance performance of the closed-loop multiple spacecraft system. Numerical simulations for a spacecraft formation are finally provided to validate the effectiveness of the proposed algorithm.     

Robust event-driven tracking control with preassigned performance for uncertain input-quantized nonlinear pure-feedback systems

This paper presents a simplified design methodology for robust event-driven tracking control of uncertain nonlinear pure-feedback systems with input quantization. All nonlinearities and quantization parameters are assumed to be completely unknown. Different from the existing event-driven control approaches for systems with completely unknown nonlinearities, the main contribution of this paper is to design a simple event-based tracking scheme with preassigned performance, without the use of adaptive function approximators and adaptive mirror models. It is shown in the Lyapunov sense that the proposed event-driven low-complexity tracker consisting of nonlinearly transformed error surfaces and a triggering condition can achieve the preselected transient and steady-state performance of control errors in the presence of the input quantization.     

Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming

In this paper, the data-driven adaptive dynamic programming (ADP) algorithm is proposed to deal with the optimal tracking problem for the general discrete-time (DT) systems with delays for the first time. The model-free ADP algorithm is presented by using only the system’s input, output and the reference trajectory of the finite steps of historical data. First, the augmented state equation is constructed based on the time-delay system and the reference system. Second, a novel data-driven state equation is derived by virtue of the history data composed of input, output and reference trajectory, which is considered as a state estimator.Then, a novel data-driven Bellman equation for the linear quadratic tracking (LQT) problem with delays is deduced. Finally, the data-driven ADP algorithm is designed to solve the LQT problem with delays and does not require any system dynamics. The simulation result demonstrates the validity of the proposed data-driven ADP algorithm in this paper for the LQT problem with delays.     

Multi-agent containment control with random link failures over dynamic cooperative networks

In this contribution, the containment control problem for a team of agents with discrete-time second-order dynamics over dynamic cooperative networks is discussed, where data from the controller to the actuator may be lost randomly and it is described by a random variable obeying Bernoulli distribution. A random-based distributed controller is designed by using the information from neighbors. Based on the hybrid tools of graph theory and nonnegative matrix, it is shown that the implementation of containment control is related to the dynamic cooperative networks and the successful rate of information transmission. Finally, a simulation is carried out to demonstrate the result in this paper.     

Human-swarm collaboration with coverage control under nonidentical and limited sensory ranges

Human intelligence plays a significant role in the operation of a multi-agent system. This study proposes a control framework that allows a human operator to collaboratively interact with a swarm robot to accomplish environmental exploration, detection, and coverage. A r_i-limited Voronoi partition is proposed herein for improving the all-territory sensing range for coverage control. Subsequently, an interactive control framework and control algorithms are presented for an abstract task function that allows a human operator to control the movement of a swarm robot in a working environment. Environmental information is fed back to the master devices so that the human operator can realize the swarm robots coverage control situation. Stability and position tracking with static coverage control and input-to-state stability with dynamic coverage control of the human-swarm system are investigated. The efficiency and efficacy of the proposed system are validated via numerical examples and experiments.     

Predictor-based optimal robust guaranteed cost control for uncertain nonlinear systems with completely tracking errors constraint

In this paper, a novel composite controller is proposed to achieve the prescribed performance of completely tracking errors for a class of uncertain nonlinear systems. The proposed controller contains a feedforward controller and a feedback controller. The feedforward controller is constructed by incorporating the prescribed performance function (PPF) and a state predictor into the neural dynamic surface approach to guarantee the transient and steady-state responses of completely tracking errors within prescribed boundaries. Different from the traditional adaptive laws which are commonly updated by the system tracking error, the state predictor uses the prediction error to update the neural network (NN) weights such that a smooth and fast approximation for the unknown nonlinearity can be obtained without incurring high-frequency oscillations. Since the uncertainties existing in the system may influence the prescribed performance of tracking error and the estimation accuracy of NN, an optimal robust guaranteed cost control (ORGCC) is designed as the feedback controller to make the closed-loop system robustly stable and further guarantee that the system cost function is not more than a specified upper bound. The stabilities of the whole closed-loop control system is certified by the Lyapunov theory. Simulation and experimental results based on a servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems

This paper concerns an adaptive fuzzy tracking control problem for a class of switched uncertain nonlinear systems in strict-feedback form via the modified backstepping technique. The unknown nonlinear functions are approximated by the generalized fuzzy hyperbolic model (GFHM). It is shown that if the designed parameters in the controller and adaptive laws are appropriately selected, then all closed-loop signals are bounded and the stability of the system can be kept under average dwell time methods. In the end, simulation studies are presented to illustrate the effectiveness of the proposed method.     

Iterative learning control for boundary tracking of uncertain nonlinear wave equations

This work presents a framework of iterative learning control (ILC) design for a class of nonlinear wave equations. The main contribution lies in that it is the first time to extend the idea of well-established ILC for lumped parameter systems to boundary tracking control of nonlinear hyperbolic distributed parameter systems (DPSs). By fully utilizing the system repetitiveness, the proposed control algorithm is capable of dealing with time-space-varying and even state-dependent uncertainties. The convergence and robustness of the proposed ILC scheme are analyzed rigorously via the contraction mapping methodology and differential/integral constraints without any system dynamics simplification or discretization. In the end, two examples are provided to show the efficacy of the proposed control scheme.     

Network-based PI control for output tracking of continuous-time systems with time-varying sampling and network-induced delays

For a continuous-time linear system with constant reference input, the network-based proportional-integral (PI) control is developed to solve the output tracking control problem by taking time-varying sampling and network-induced delays into account. A traditional PI control system is introduced to obtain the equilibriums of state and control input. Using the equilibriums, a discrete-time PI tracking controller in a network environment is constructed. The resulting network-based PI control system is described by an augmented system with two input delays and the output tracking objective is transformed into ensuring asymptotic stability of the augmented system. A delay-dependent stability condition is established by a discontinuous augmented Lyapunov–Krasovskii functional approach. The PI controller design result of in-wheel motor as a case study is provided in terms of linear matrix inequalities. Matlab simulation and experimental results resorting to a test-bed for ZigBee-based control of in-wheel motor are given to validate the proposed method.