Reinforcement-Learning-Based Fixed-Time Optimal Formation Control for Multiple Mobile Robots With Prescribed Performance

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Abstract
This study investigates reinforcement-learning-based fixed-time optimal formation control for multiple nonholonomic mobile robots with prescribed performance constraints. First, the constrained formation error dynamics is established using a leader-follower approach. Meanwhile, a barrier function is employed to transform the constrained formation error dynamics into an unconstrained form. Then, an adaptive control technique and a critic-only reinforcement learning strategy are utilized to design a fixed-time optimal control law for the unconstrained error dynamics. Rigorous theoretical derivations demonstrate that the proposed control law guarantees that the constrained formation error converges to near zero within a fixed time, optimizing the performance index while satisfying the prescribed performance requirement. Finally, the feasibility of the proposed method is verified through simulations and experiments.
Funding Information
  • National Natural Science Foundation of China (52175046, 51939001)
  • Sichuan Science and Technology Program (2024YFFK0037, 2024ZYD0165)

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