Fuzzy Adaptive Optimization Prescribed Performance Control for Nonlinear Vehicle Platoon
Kewen Li, Yongming Li
Abstract
This paper investigates the reinforcement learning-based adaptive fuzzy optimization control problem for third-order nonlinear vehicle platoon. The unknown nonlinear dynamic is approximated using fuzzy logic system (FLS). By the aid of prescribed performance technique, the designed quadratic spacing errors can be ensured to remain within a preset region. By constructing barrier type optimal cost function, and employing the actor–critic FLSs construction, a fuzzy adaptive optimization prescribed performance control algorithm is developed, which further verifies the individual stability of each vehicle, and all signals of vehicle platoon system are bounded. In addition, the strong string stability of vehicle platoon system can be ensured using the couple sliding mode surface. Finally, simulations are conducted to demonstrate the effectiveness and feasibility of the proposed results.