Adaptive dynamic programming for trajectory tracking control of a tractor-trailer wheeled mobile robot

Abstract

Tractor-trailer wheeled mobile robots (TTWMRs) possess complex nonlinear dynamics that make their precise trajectory tracking control challenging. This paper explores an adaptive dynamic programming (ADP) approach that utilizes critic neural networks to improve tracking control for continuous-time TTWMRs. To achieve this, the decoupled kinematic and dynamic loops of the TTWMR are considered, and ADP controllers are proposed aimed at integrated trajectory and velocity tracking. Tis study defines two tracking error systems related to the kinematic and dynamic control loops, which reduces the computational load compared to previous research. The two critic neural networks approximate the optimal cost functions and enable the adaptive tuning of the control policies. Theoretical analysis demonstrates both closed-loop stability and convergence. Simulation results indicate that the proposed method offers superior tracking performance compared to earlier techniques, exhibiting lower errors and reduced control efforts. This underscores the advantages of using ADP to optimize the control of TTWMRs, even in the presence of partially unknown dynamics.