Optimizing bioinspired neurodynamic formation control for traffic cone robots under control input constraints: A noncooperative game approach

Abstract

This study presents an optimized bioinspired neurodynamic control framework for the formation placement and recovery control of traffic cone robots (TCRs) under constrained control inputs. A dynamic error model is constructed based on positional states, upon which a backstepping controller is designed and integrated with a bioinspired neurodynamic module to mitigate infeasible control commands arising from large initial deviations, ensuring actuator feasibility. Lyapunov-based analysis demonstrates closed-loop stability and guarantees the asymptotic convergence of formation errors. In addition, a multi-parameter optimization framework grounded in noncooperative game theory is proposed to identify optimal control gains at the Nash equilibrium, minimizing a predefined performance cost. The effectiveness, robustness, and practical applicability of the approach are validated through both numerical simulations and physical experiments, demonstrating its potential for real-world TCR formation operations.