Real-Time Resilient Tracking Control for Autonomous Vehicles Through Triple Iterative Approximate Dynamic Programming

Enhancing control precision, mitigating external disturbances, and ensuring real-time responsiveness stand as the cornerstone of autonomous vehicle tracking endeavors, each of which intricately interwoven to uphold operational safety. In pursuit of addressing these issues, this paper presents a triple iterative control method inspired by approximate dynamic programming (ADP) tailored for real-time disturbance avoidance. The control framework orchestrates simultaneous iterations of value function, control policy, and disturbance policy, engineered to optimize tracking control amidst external disturbances cast as a zero-sum differential game, tackled adeptly through deep neural networks. Rigorous mathematical proof underpins its triple iteration, coupled with assurances of residual error convergence, solidifying its safety guarantee ability and algorithmic resilience. To validate its effectiveness, both numerical simulations and experiments on a real micro-vehicle platform were conducted. Results underscore the feasibility of this new method, showcasing its energy-saving capability and a four-times acceleration compared to conventional model predictive control (MPC) approaches when confronted with lateral disturbances. Notably, the single-step calculation time of this method on the Raspberry Pi is only 1.44ms, affirming its practical viability and real-world applicability.