Bounded consensus tracking of second-order multi-agent systems using rectangular impulsive control

Nonlinear Dynamics - In this paper, the problem of bounded consensus tracking for second-order multi-agent systems is addressed with fixed and randomly switching directed topologies, wherein the...     

Fixed-time consensus tracking control of second-order multi-agent systems with inherent nonlinear dynamics via output feedback

Nonlinear Dynamics - This paper is devoted to the fixed-time consensus tracking control problem for a class of second-order nonlinear multi-agent systems. Firstly, a fixed-time consensus control...     

Observer-based adaptive consensus tracking control for nonlinear multi-agent systems with actuator hysteresis

Nonlinear Dynamics - This paper addresses the consensus tracking problem of a class of nonlinear multi-agent systems by using observer-based control. The systems are in output-feedback form with...     

Neural network-based adaptive output feedback formation control for multi-agent systems

This paper investigates the problem of output feedback formation tracking control for second-order multi-agent systems under an undirected connected graph and in the presence of dynamic uncertainties and bounded external disturbances. Two state tracking error measures (i.e., absolute and relative state tracking errors) are considered for each individual agent in the formation, and linear reduced-order observers are constructed based on the lumped state tracking errors which include absolute and relative state tracking errors. Chebyshev neural networks are used to approximate unknown nonlinear function in the agent dynamics on-line, and the implementation of the basis functions of Chebyshev neural networks depends only on the desired signals. The smooth projection algorithm is applied to guarantee that the estimated parameters remain in some known bounded sets. Numerical simulations are presented to illustrate the performance of the proposed controller.     

Autonomous and non-autonomous fixed-time leader–follower consensus for second-order multi-agent systems

Nonlinear Dynamics - This paper addresses the problem of consensus tracking with fixed-time convergence, for leader–follower multi-agent systems with double-integrator dynamics, where only a...     

Consensus tracking via quantized iterative learning control for singular nonlinear multi-agent systems with state time-delay and initial state error

Nonlinear Dynamics - This paper investigates the quantized iterative learning consensus tracking problem for singular nonlinear multi-agent systems (MASs) in the presence of state time-delay and...     

Leader-following second-order consensus in multi-agent systems with sampled data via pinning control

This paper studies the second-order consensus in multi-agent systems with sampled position and velocity data via pinning control. The distributed pinning consensus protocols with second-order dynamics are designed, where both sampled position and velocity data are employed. Necessary and sufficient conditions are derived for reaching second-order consensus by combining the algebraic graph theory and the analytical method. According to the obtained consensus criteria, the second-order leader-following consensus can be reached if and only if the sampling period, the coupling gain, and the pinning gains satisfy some derived algebraic inequalities. Moreover, it is found that second-order consensus in multi-agent systems cannot be reached with only sampled position data in the pinning controllers. Finally, the effectiveness and correctness of our theoretical findings are demonstrated by some numerical examples.     

Sampled-data output voltage regulation for a DC–DC buck converter nonlinear system with actuator and sensor failures

Nonlinear Dynamics - This paper considers the distributed adaptive neural consensus tracking control problem for a class of uncertain nonaffine nonlinear multi-agent systems. By making use of the...     

Adaptive neural network consensus tracking control for uncertain multi-agent systems with predefined accuracy

Nonlinear Dynamics - This paper proposes the consensus tracking control problem for a class of uncertain nonlinear multi-agent systems. By using a group of nonnegative functions, an adaptive neural...     

Neural network based robust hybrid control for robotic system: an H �� approach

A novel robust hybrid tracking control for robotic system is proposed. This hybrid control scheme combines computed torque control (CTC) with neural network, variable structure control (VSC) and nonlinear H _?? control methods. It is assumed that the nominal system of robotic system is completely known, which is controlled by using CTC method. Neural network is designed to approximate parameter uncertainties, VSC is used to eliminate the effect of approximation error, and H _?? control is employed to achieve a desired robust tracking performance. Based on Lyapunov stability theorem, it can be guaranteed that all signals in closed loop are bounded and a specified H _?? tracking performance is achieved by employing the proposed robust hybrid control. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Asymptotical consensus of fractional-order multi-agent systems with current and delay states

In this paper, we study some new fractional-order multi-agent systems with current and delay states(FMASCD). Using the generalized Nyquist's stability criterion and Gerschgorin's circle theorem, we obtain the bounded input-bounded output(BIBO)stability and asymptotical consensus of the FMASCD under mild conditions. Moreover,we give some numerical examples to illustrate our main results.     

Robust adaptive nonlinear control for uncertain control-affine systems and its applications

This paper addresses the robust tracking control problem for a class of uncertain nonlinear systems with time-varying parameters, perturbed by external disturbances. The unknown time-varying parameters and disturbances are neither required to be periodic nor to have known bounds. Depending on the characteristics of disturbance signals, two adaptive-based control algorithms are developed. First, an adaptive H _∞ control is designed that achieves: (i) an H _∞ tracking performance when the external disturbances are L ₂ signals, and (ii) the convergence of tracking error to zero if the disturbances are bounded and L ₂ signals. Then a novel adaptive control algorithm is proposed, only with the assumption of boundedness of disturbances, to drive the tracking error to zero. The designed tracking controllers are then used for controlling a cart-pendulum system, as an underactuated mechanical system, and chaos synchronization of uncertain Genesio–Tesi chaotic system. Numerical simulations are also given to demonstrate the effectiveness of the proposed control schemes.     

Adaptive neural tracking control for a class of perturbed pure-feedback nonlinear systems

This paper focuses on the problem of the adaptive neural control for a class of a perturbed pure-feedback nonlinear system. Based on radial basis function (RBF) neural networks’ universal approximation capability, an adaptive neural controller is developed via the backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error eventually converges to a small neighborhood around the origin. The main advantage of this note lies in that a control strategy is presented for a class of pure-feedback nonlinear systems with external disturbances being bounded by functions of all state variables. A numerical example is provided to illustrate the effectiveness of the suggested approach.     

Consensus of delayed multi-agent dynamical systems with stochastic perturbation via dual-stage impulsive approach

Nonlinear Dynamics - This paper investigates the problem of consensus for delayed multi-agent systems with stochastic perturbation via dual-stage impulsive approach. A novel dual-stage impulsive...     

BLS-based formation control for nonlinear multi-agent systems with actuator fault and input saturation

This paper studies the formation control of a nonlinear multi-agent system based on a broad learning system under actuator fault and input saturation. Firstly, the multi-agent tracking error is proposed based on graph theory. Besides, fault tolerance should be considered when actuator fault exists. Meanwhile, the broad learning system is put forward to approximate the unknown nonlinear function in the multi-agent system. Then, an input saturation auxiliary system is introduced to reduce the adverse effects of input saturation constraints. At the same time, the disturbance observer technology is used to estimate the actuator failure as a lumped uncertainty. At last, dynamic surface control is introduced to realize formation control with actuator fault and input saturation. Obviously, it is difficult to design a controller with unknown nonlinear function, input saturation, and actuator fault existing in the multi-agent system. The Lyapunov method can prove the stability of the formation control. The simulation results verify the effectiveness of the controller.

     

Property and evolution of the running-in attractor in an actual dynamic system

In this paper, the issue of adaptive neural tracking control for uncertain switched multi-input multi-output (MIMO) nonstrict-feedback nonlinear systems with average dwell time is studied. The system under consideration includes unknown dead-zone inputs and output constraints. The uncertain nonlinear functions are identified via neural networks. Also, neural networks-based switched observer is constructed to approximate all unmeasurable states. By means of the information for dead-zone slopes and barrier Lyapunov function (BLF), the problems of dead-zone inputs and output constraints are tackled. Furthermore, dynamic surface control (DSC) scheme is employed to ensure that the computation burden is greatly reduced. Then, an observer-based adaptive neural control strategy is developed on the basis of backstepping technique and multiple Lyapunov functions approach. Under the designed controller, all the signals existing in switched closed-loop system are bounded, and system outputs can track the target trajectories within small bounded errors. Finally, the feasibility of the presented control algorithm is proved via simulation results.

     

Group consensus of discrete-time multi-agent systems with fixed and stochastic switching topologies

This paper investigates the group consensus problem for discrete-time multi-agent systems with a fixed topology and stochastic switching topologies. The stochastic switching topologies are assumed to be governed by a finite-time Markov chain. The group consensus problem of the multi-agent systems is converted into the stability problem of the error systems by a model transformation. Based on matrix theory and linear system theory, we obtain two necessary and sufficient conditions of couple-group consensus for the case of fixed topology, and one necessary and sufficient condition of mean-square couple-group consensus for the case of stochastic switching topologies. Algorithms are provided to design the feasible control gains. Then, the results are extended to the case of multi-group consensus. Finally, simulation examples are given to show the effectiveness of the proposed results.     

Observer-based adaptive fuzzy finite-time prescribed performance tracking control for strict-feedback systems with input dead-zone and saturation

This paper considers the problems of finite-time prescribed performance tracking control for a class of strict-feedback nonlinear systems with input dead-zone and saturation simultaneously. The unknown nonlinear functions are approximated by fuzzy logic systems and the unmeasurable states are estimated by designing a fuzzy state observer. In addition, a non-affine smooth function is used to approximate the non-smooth input dead-zone and saturated nonlinearity, and it is varied to the affine form via the mean value theorem. An adaptive fuzzy output feedback controller is developed by backstepping control method and Nussbaum gain method. It guarantees that the tracking error falls within a pre-set boundary at finite time and all the signals of the closed-loop system are bounded. The simulation results illustrate the feasibility of the design scheme.

     

Distributed average tracking with input saturation

Nonlinear Dynamics - In this paper, the distributed average tracking (DAT) problem of a multi-agent system with input saturation is considered, whose objective is to make each agent track the...     

Finite-time consensus of nonlinear multi-agent systems via impulsive time window theory: a two-stage control strategy

This paper concentrates on the finite-time consensus problem faced by nonlinear multi-agent systems (MASs) via impulsive time window theory with a two-stage control (TSC) strategy. The TSC strategy divides the whole control period into two parts: a variable impulsive control stage and a finite-time consensus control stage. Different from general single-stage control, TSC can dynamically adjust the time periods of impulsive control and finite-time control according to practical application requirements. Variable impulsive control is also discussed in this paper. Compared with the sampling based on traditional fixed impulsive theory, impulsive sampling in the TSC strategy occurs randomly within an impulsive time window and provides much more flexibility. In addition, a switching topology scheme is introduced in this paper to strengthen the stability of MASs. Finally, two numerical simulation examples (one leaderless case and one leader-following case) are used for the theoretical analysis.