Command Filter-Based Adaptive Neural Control Design for Nonstrict-Feedback Nonlinear Systems With Multiple Actuator Constraints

In this paper, a novel adaptive dynamic programming (ADP)-based event-triggered safe control method is proposed to solve the zero-sum game problem of nonlinear safety-critical systems with safety constraints and input saturation. First, the barrier function-based system transformation, the zerosum game problem with safety constraints and input saturation is transformed into an equivalent input saturation zero-sum game problem, so as to guarantee that the system does not violate the safety constraints. Furthermore, the non-quadratic utility function is introduced into the performance function to solve input saturation. Then, a critic neural network (NN) is constructed to approximate the optimal safety value function. Subsequently, a novel event-triggered scheme is developed to determine the update instant of the control law and the disturbance law. Therefore, the proposed ADP-based event-triggered safe control method can ensure that the states of nonlinear safety-critical systems satisfy the safety constraints, while greatly reducing the amount of calculation and saving communication resources. In addition, during the learning process, the concurrent learning is used to relax the persistence of excitation (PE) condition. According to the Lyapuov theory, it is proved that the weight estimation error of the critic neural network and the states are uniformly ultimately bounded (UUB), and the Zeno behavior is excluded. Finally, a simulation example verifies the effectiveness of the proposed method.

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“…where r(s, u, d saturation, and Z(u) ≥ 0 with Z(0) = 0. Inspired by [31], [39], Z(u) can be defined as…”

Section: B Barrier-function-based Zsg Problem Transformationmentioning

confidence: 99%

Event-Triggered Safe Control for the Zero-Sum Game of Nonlinear Safety-Critical Systems With Input Saturation

et al. 2022

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“…Since then, an adaptive command filter quantized control method is developed for parametric nonlinear systems in [12]. In [13], on account of the command filtered backstepping controller, an adaptive neural control has emerged for nonlinear systems. In [14], command filtered-based adaptive controller is studied for nonlinear time-delay systems.…”

Section: Introductionmentioning

confidence: 99%

Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance

Song

Sun

Song

et al. 2022

Journal of the Franklin Institute

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“…As we all know, many practical modeling problems need to consider the nonlinear characteristics of the system in addition to the time characteristics of the system. [23][24][25] The traditional PLS methods use the linear relationship to describe the inner model of the system, which cannot achieve satisfactory results for the nonlinear systems. Therefore, many methods have been proposed to solve this problem and they can be classified into four categories: (1) nonlinear reconstruction of the input data matrix; 26 (2) use the nonlinear model to describe the PLS inner model; 27,28 (3) simultaneously change the internal and external models of PLS; 29,30 (4) incorporate the PLS method into other nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

A novel dynamic nonlinear partial least squares based on the cascade structure

Wang

2022

Intl J Robust & Nonlinear

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For the collinearity problem of input variables in actual industrial process modeling, a novel dynamic nonlinear partial least squares (PLS) approach is presented to solve this problem. In the proposed method, a novel cascade structure which is composed of an autoregressive exogenous model and a radial basis function neural network is introduced as the inner model of the conventional PLS method, so that the newly established PLS method has both dynamic and nonlinear characteristics. In this improvement, an external model for extracting the latent variables of input and output is established through the original PLS model, which eliminates the collinearity between original dependent variables, and then the aforementioned cascade framework is employed to describe the dynamic and nonlinear relationships between the input and output score variables. Furthermore, in order to estimate the unknown parameters of the established dynamic nonlinear inner model, a stochastic gradient algorithm and a forgetting factor stochastic gradient algorithm are presented. Additionally, a simple cross validation method is used to determine the orders of the inner model. Finally, a numerical simulation example and an application example are given to demonstrate the effectiveness of the proposed approach.

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Command Filter-Based Adaptive Neural Control Design for Nonstrict-Feedback Nonlinear Systems With Multiple Actuator Constraints

Cited by 103 publications

References 61 publications

Event-Triggered Safe Control for the Zero-Sum Game of Nonlinear Safety-Critical Systems With Input Saturation

Event-Triggered Safe Control for the Zero-Sum Game of Nonlinear Safety-Critical Systems With Input Saturation

Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance

A novel dynamic nonlinear partial least squares based on the cascade structure

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