Multi-objective Evolutionary Algorithm-based Optimal Posture Control of Humanoid Robots


This paper proposes a multi-objective evolutionary algorithm-based optimal posture controller to generate an optimal trajectory of humanoid robots against external disturbance using an iterative linear quadratic regulator (ILQR) and concurrently optimize multiple performance criteria. As the dimensionality of nonlinear system increases, it is difficult to find the weighting matrices of cost function in ILQR. In the proposed method, this problem is solved by employing a multi-objective quantum-inspired evolutionary algorithm (MQEA) to obtain nondominated solutions of the weighting matrices generating various optimal trajectories that satisfy multiple performance criteria. Among numerous nondominated solutions generated from MQEA, fuzzy measure and fuzzy integral are employed for global evaluation by integrating the partial evaluation of each of them over criteria with respect to user's degree of consideration for each criterion. The effectiveness of the proposed method is verified by computer simulations for the problem of balancing the posture of a humanoid robot against external impulse force, where the robot is modeled by a four-link inverted pendulum.