Hierarchical approximate optimal coordinated control of modular robot manipulators through Nash-Stackelberg differential game

The hierarchical approximate optimal coordinated control of modular robot manipulators (MRMs) modeled by joint torque feedback is developed via adaptive dynamic programming (ADP). Taking advantage of the Nash-Stackelberg differential game framework, the coordinated control issue is transformed into Stackelberg differential game between MRM as leader formulated as Nash differential game and coordinate operate objective as follower. The critic neural network (NN) is built to deal with the nonlinear cost function and then derived for obtaining the approximate optimal control strategy. Theoretical analysis and experimental verification have respectively proved the effectiveness of the proposed control algorithm.