Approximate Optimal Adaptive Prescribed Performance Control for Uncertain Nonlinear Systems With Feature Information
Guangjun Chen, Jiuxiang Dong
Abstract
This article investigated the performance optimization tracking control problem of strict-feedback nonlinear systems with feature information. A performance-optimized adaptive tracking control framework is proposed, which utilizes local dynamic feature information for optimization while guaranteeing the prescribed performance. When partial system dynamics are available, a feature system with performance constraints is constructed, and the optimal control method is used to improve the comprehensive performance of the control system. Based on the prescribed performance control (PPC) method, the adaptive prescribed performance optimal tracking controllers are designed to solve the actual overall dynamic unknown tracking problem, which can be combined with the optimized feature dynamics to achieve precise tracking control and reduced energy consumption. A relative direction threshold event-triggered mechanism (RDTETM) is developed to reduce the frequency of actuator updates. Compared with existing results, the advantages of the proposed control scheme are that it allows for the flexible application of dynamic information for optimization that may be obtained to improve the control system’s comprehensive performance. Finally, the simulation example is presented to verify the effectiveness of the developed control strategy.