Optimal robust control with cooperative game theory for lower limb exoskeleton robot

For achieving trajectory tracking issue of the lower limb exoskeleton robot, a novel optimal robust control with cooperative game theory is proposed. The uncertainties are considered (possible time-varying, bounded and fast), and the fuzzy set theory is creatively adopted to describe the boundary. From the view of analytical mechanics, the trajectory tracking is treated as the constraints control problem, including holonomic and nonholonomic constraints, which need to satisfy the conditions of human motion. Combining the robust control and optimal design, optimal robust control is formulated to satisfy both performances guaranteeing and optimal. The Pareto optimal solution is obtained to guarantee the minimum control cost. In the simulation, the adaptive robust control is chosen as a comparison. The existence of Pareto optimality and the effectiveness of optimal robust control have been verified via simulation results.

Automated summary

This study proposes a novel optimal robust control method based on cooperative game theory for achieving trajectory tracking issue of the lower limb exoskeleton robot. The method considers uncertainties and adopts fuzzy set theory to describe the boundary. By combining robust control and optimal design, the optimal robust control is formulated to satisfy both performance guaranteeing and optimality. The Pareto optimal solution is obtained to ensure the minimum control cost.