Performance-Prescribed Optimal Control for Target Enclosing of Vehicles via Control Barrier Function-Based Reinforcement Learning

Abstract

The target enclosing control problem for autonomous vehicles with uncertainties necessitates simultaneous consideration of control optimality, robustness, and safety-guided performance constraints. This paper presents a performance-prescribed optimal control algorithm using control barrier function (CBF)-based reinforcement learning (RL) to address the above problem, which contains two key contributions. First, a special CBF-based argument term is developed and embedded into the reward function to characterize environmental feedback regarding the risk of violating constraints, which enables the controller to confine enclosing errors within declared boundaries with minimal intervention. Second, a critic-only neural network is utilized to synthesize the optimal control policy, where a novel fixed-time updating law is presented to accelerate the weight convergence to ideal values within a fixed settling time, thereby enhancing the online learning ability and further improving control performance. Theoretical outcomes related to learning convergence, safety, stability, and robustness are rigorously verified. Simulations reveal that the proposed strategy outperforms the previously designed enclosing controllers based on the non-RL and RL ways in terms of complying with prescribed safety constraints and optimizing long-term performance.