Liuliu Zhang

Decentralized Output Feedback Adaptive NN Tracking Control for Time-Delay Stochastic Nonlinear Systems With Prescribed Performance

This paper studies the dynamic output feedback tracking control problem for stochastic interconnected time-delay systems with the prescribed performance. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, a new state transformation is proposed in consideration of the prescribed performance requirement. Using neural network to approximate the composite unknown nonlinear function, the corresponding decentralized output tracking controller is designed. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and that both transient-state and steady-state performances are preserved. Finally, a simulation example is given, and the result shows the effectiveness of the proposed control design method.

Output feedback control for interconnected time-delay systems with prescribed performance

This paper studies the problem of output feedback control for interconnected time-delay systems with prescribed performance. Currently, few of the existing results consider the prescribed performance control in the nonlinear interconnected time-delay systems. The subsystems are in the form of triangular structure with unmodeled dynamics. First, we design a reduced-order observer to estimate the unmeasured states online instead of the traditional full-order observer. Then, by proposing a new state transformation with the performance function, we construct a novel output feedback controller with the idea of the backstepping method. It is strictly proved that the resulting closed-loop system is stable in the sense of uniformly ultimately boundedness and both transient and steady-state performances of the outputs are preserved. Finally, a simulation example is given and the results show the effectiveness of the proposed control design method.

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

Output feedback tracking control for nonlinear time-delay systems with tracking errors and input constraints

The adaptive tracking control problem is considered for a class of nonlinear time-delay systems in the presence of input and tracking error constraint. A reduced-order observer is designed to estimate the unmeasured state variables at first. Then, a constraint variable is utilized to ensure that the tracking error is within the prescribed boundaries. An auxiliary state is introduced to deal with the input saturation constraint. With the time-delay functions unavailable, we employ adaptive RBF neural network systems to approximate unknown functions. It is proved that the resulting closed-loop system is stable in the sense of semiglobal uniformly ultimately boundedness. The simulations are performed and the results demonstrate the effectiveness of the proposed approach.

Reduced-order observer-based output feedback control of nonlinear time-delay systems with prescribed performance

This paper studies the problem of output feedback control for a class of nonlinear time-delay systems with prescribed performance. The system is in the form of triangular structure with unmodelled dynamics. First, we introduce a reduced-order observer to provide the estimate of the unmeasured states. Then, by setting a new condition with the performance function, we design the state transformation with prescribed performance control. By employing backstepping method, we construct the output feedback controller. It is proved that the resulting closed-loop system is asymptotically stable and both transient and steady-state performance of the output are preserved with the changing supply function idea. Finally, a simulation example is conducted to show the effectiveness of the main results.

Distributed Adaptive Fuzzy Containment Control of Stochastic Pure-Feedback Nonlinear Multiagent Systems With Local Quantized Controller and Tracking Constraint

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques.

Adaptive state feedback control for time-delay stochastic nonlinear systems based on dynamic gain method

ABSTRACT This paper studies the adaptive state feedback control for p normal form time-delay stochastic nonlinear systems with unknown parameters by dynamic gain technique. The power order restriction is completely removed and tracking problem is further studied. Through the inductive design method, the virtual controllers are constructed in each step, and the corresponding dynamic gain is introduced to eliminate residual terms generated by the differential operator of Lyapunov–Krasovskii functional in the subsequent step, which is used to deal with the time-delay terms. The unknown parameters are addressed by the modified tuning function method. Based on the constructed adaptive controller, the boundedness of the tracking error and other state variables can be guaranteed. Especially, if the reference signal is zero, the state variables can converge to equilibrium almost surely. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.

Decentralised adaptive control of switched interconnected high-order nonlinear systems with unknown control direction and time-delay

ABSTRACT This paper studies the dynamic state feedback control problem for a class of interconnected large-scale switched high-order nonlinear systems with unknown control direction and time-varying time-delay. The adaptive laws are designed to estimate the bounds of switched parameters under arbitrary switching for subsystems. The Nussbaum function is used to deal with the unknown control direction problem. By combining the backstepping and homogeneous domination technique, the decentralised adaptive control strategies are developed and the resulting closed-loop system is asymptotically stable. Finally, a simulation example is given and the results show the effectiveness of the proposed control design method.

Decentralized Prescribed Performance Tracking of Stochastic Time-Delay System

This chapter studies the problem of dynamic output feedback tracking control for stochastic interconnected time-delay systems with prescribed performance control. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, by proposing a new state transformation with the performance function, the corresponding decentralized output tracking controller is designed by using neural network to approximate the composite unknown nonlinear function. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and both transient and steady-state performances of the outputs are preserved. Finally, a simulation example is given, and the results show the effectiveness of the proposed control design method.

Adaptive Tracking of Time-Delay System with Unknown Dead-Zone Input

This chapter focuses on the tracking control problem for a class of nonlinear system with time delay and dead-zone input. The non-symmetric dead-zone input case is considered without the knowledge of the dead-zone parameters. The time-delay uncertainties are bounded by a nonlinear function with unknown coefficients. By constructing a novel Lyapunov functional, we design a simple and smooth adaptive state feedback controller. It is shown that the solution of the resulting closed-loop error system converges to an adjustable region exponentially. Finally, numerical examples are included to show the effectiveness of the theoretical results.