High-Order Robust Control with Optimized Parameters for Collaborative Robots

Uncertainties exist in control fields which have a significant impact both on system performance and control cost. A fuzzy approach is applied to describe uncertainties of a collaborative robot based on the membership function. Then a high-order robust control is designed to compensate for uncertainties. The performances of uniform boundedness and uniform ultimate boundedness are obtained. To increase system performance and decrease control cost, the optimal control parameters are solved based on game theory to balance performance and cost. In the end, the control effects are verified based on simulation results by comparing with other classical control methods.