Double-Closed-Loop Robust Optimal Control for Uncertain Nonlinear Systems

Optimal control methods have gained significant attention due to their promising performance in nonlinear systems. In general, an optimal control method is regarded as an optimization process for solving the optimal control laws. However, for uncertain nonlinear systems with complex optimization objectives, the solving of optimal reference trajectories is difficult and significant that might be ignored to obtain robust performance. For this problem, a double-closed-loop robust optimal control (DCL-ROC) is proposed to maintain the optimal control reliability of uncertain nonlinear systems. First, a double-closed-loop scheme is established to divide the optimal control process into a closed-loop optimization process that solves optimal reference trajectories and a closed-loop control process that solves optimal control laws. Then, the ability of the optimal control method can be improved to solve complex uncertain optimization problems. Second, a closed-loop robust optimization (CL-RO) algorithm is developed to express uncertain optimization objectives as data-driven forms and adjust optimal reference trajectories in a close loop. Then, the optimality of reference trajectories can be improved under uncertainties. Third, the optimal reference trajectories are tracked by an adaptive controller to derive the optimal control laws without certain system dynamics. Then, the adaptivity and reliability of optimal control laws can be improved. The experimental results demonstrate that the proposed method can achieve better performance than other optimal control methods.

Automated summary

Optimal control methods have shown promising performance in nonlinear systems, but solving optimal reference trajectories in uncertain nonlinear systems with complex optimization objectives is difficult and often ignored. To address this issue, a double-closed-loop robust optimal control (DCL-ROC) method is proposed. The method divides the optimal control process into a closed-loop optimization process and a closed-loop control process, and improves the ability to solve uncertain optimization problems. Experimental results demonstrate that the proposed method outperforms other optimal control methods.