Finite-Time Adaptive Neural Control for a Class of Nonlinear Systems With Asymmetric Time-Varying Full-State Constraints

Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

Differential Neural Network Identifier for Dynamical Systems With Time-Varying State Constraints

This study presents a state nonparametric identifier based on neural networks with continuous dynamics, also known as differential neural networks (DNNs). The laws for adjusting their parameters are developed using a control barrier Lyapunov functions (BLFs). The motivation for using the BLF comes from the preliminary information of the system states, which remain in a predefined time-depending set characterized by state or purely time-dependent functions. In this study, time-dependent state constraints are supposed to be known in advance continuous-time functions. The obtained learning laws require solving differential continuous-time Riccati equations and nonlinear differential equations for the learning laws that depend on the identification error and the state restrictions. The developed identifier was evaluated concerning the identifier that does not consider the state restrictions. This comparison included the numerical evaluation of the identifier for a robotic arm intended to reproduce a nonstandard flight simulator. This evaluation confirmed that the identification results were improved using the proposed learning laws and considering that the state limits were not transgressed. The quality indicators based on the mean square error were more minor by 4.2 times.

Event-triggered tracking control of uncertain p-normal nonlinear systems with full-state constraints

This paper investigates the tracking control problem of uncertain p-normal nonlinear systems with full-state constraints via event-triggered mechanism. By skillful constructing an adaptive dynamic gain and a time-varying event-triggered strategy, a state-feedback controller is proposed to achieve practical tracking. The adaptive dynamic gain is incorporated to deal with the system uncertainties and eliminate the bad effect of the sampling error. A rigorous Lyapunov stability analysis method is put forward to verify that all the closed-loop signals are uniformly bounded and the tracking error converges into a prescribed arbitrary accuracy, and full-state constraints are not violated. Compared with the existing event-triggered strategies, the proposed time-varying event-triggered strategy is low-complexity without designing the hyperbolic tangent function.