Event-triggered Fuzzy control for Nonlinear Time-Delay System with Full-State Constraints and Unknown Hysteresis

In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. The unknown time delay and state constraints problem of the concentration in the reactor seriously affect the input-output ratio and stability of the entire system. Therefore, the design difficulty of this control scheme is how to debar the effect of time delay in CSTR systems. To deal with time-varying delays, Lyapunov–Krasovskii functionals (LKFs) are utilized in the adaptive controller design. The convergence of the tracking error to a small compact set without violating the constraints can be identified by the time-varying logarithm barrier Lyapunov function (LBLF). Finally, the simulation results on CSTR are shown to reveal the validity of the developed control strategy.

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Distributed Robust Tracking Control for Multiple Euler–lagrange Systems With Full-state Constraints and Input Saturation via Event-triggered Control

10.21203/rs.3.rs-278000/v1 ◽

2021 ◽

Author(s):

Haitao liu ◽

Guangshuo Du ◽

Xuehong Tian ◽

Lanping Zou

Keyword(s):

Tracking Control ◽

State Constraints ◽

Input Saturation ◽

Communication Delay ◽

External Disturbances ◽

Position Information ◽

Delay Function ◽

Full State ◽

Full State Constraints ◽

Event Triggered

Abstract In this paper, the issue of distributed tracking control is studied for multiple Euler–Lagrange systems in presence of external disturbances and input saturation. Specifically, the full-state constraints, input saturation, communication delay, and unmeasured velocity are also considered simultaneously. Firstly, an adaptive distributed state observer is introduced to obtain the leader's time-varying position information, at the same time, a delay function is employed to compensate the communication delay. Moreover, the event-triggered control scheme is developed to reduce communication source and computation load, and the anti-saturation compensation algorithm is exploited to compensate for the influence of system saturation. Thirdly, an adaptive law is designed to offset external disturbances. What’s more, the high-gain observer is used to estimate the unmeasured velocities. Theorem analysis shows that the system errors can converge to zero. Finally, numerical simulations are present to verify the effectiveness of the proposed control strategy.

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Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820975265 ◽

2020 ◽

pp. 095965182097526

Author(s):

Yuxiang Wu ◽

Tian Xu ◽

Haoran Fang

Keyword(s):

Time Delay ◽

Tracking Control ◽

State Constraints ◽

Actuator Saturation ◽

Small Neighborhood ◽

Delay Systems ◽

Time Varying ◽

Time Delay Systems ◽

Full State ◽

Full State Constraints

This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

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