Synthesis of an adaptive control system for a class of nonlinear plants with uncertain disturbances

Abstract

This paper presents a method for synthesizing an adaptive control system for a class of nonlinear plants with uncertainty disturbances. With these control plants, we propose an adaptive identified law based on the RBF neural network for the uncertain disturbances component. From the recognition results of uncertain components, the controller for the plant is synthesized based on the sliding mode control principle for the system to track the desired state. The paper’s results are rigorously proven by mathematics; the correctness, reliability, and efficiency are confirmed by simulation on Matlab Simulink software. The control system proposed by the article is simple, easy to implement in engineering, has high control quality, and has good adaptability and disturbance resistance. The research results of the paper provide a new approach to designing control systems for nonlinear plants having the impact of uncertainty disturbances commonly encountered in practical applications such as ships, robots, and many other production systems suitable for industry.