An Application of Multiobjective Optimization Genetic Algorithm for Cart-Double-Pendulum-System Control

In this paper, a new multiobjective optimization genetic algorithm is applied to solve the control problem concerning the swinging-up and handstand-control of the cart-double-pendulum system (CDPS), a typical under-actuated, unsteady and nonlinear system) based on the human-simulated intelligent control (HSIC) theory. By analyzing the movement of CDPS and simulating the Human controller, we designed the sensory-motor intelligent schemas (SMIS) of HSIC controller for the CDPS. Using multiobjective optimization genetic algorithm, we can design parameters of the HSIC controller for CDPS with multiobjective requirements. And we use a test example regard to controller design problem to test the proposed method and compared with MOGA. Then the test results using the proposed method is better than ones using MOGA. Finally, we define the multi-performance indices of CDPS. The simulation results show that the response of CDPS can achieve the performance indices of CDPS which we define using the proposed method successfully.