The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN

Abstract

Aiming at the triple inverted pendulum which is a strong coupling, multivariable, high-order and unsteady system, a design method of the controller based on PID neural network (PIDNN) optimized by cloud genetic algorithm (CGA) is proposed, this method is called CGA-PIDNN. CGA can be applied to learn and train the PIDNN connection weights. CGA can overcome the defect of the slow convergence rate and premature convergence for genetic algorithm (GA). PIDNN is a simple and normative network which is easy to be realized and has a good dynamic performance. The CGA-PIDNN control system of triple inverted pendulum is verified with MATLAB simulation test. The comparison results with the control effect of PIDNN control system optimized by standard GA (GA-PIDNN) are presented first. Then in LabVIEW environment, by using the combination of virtual reality technology and MATLAB, the three-dimensional (3D) animation simulation model of the triple inverted pendulum CGA-PIDNN control system is built. The simulation results indicate that CGA-PIDNN control method is effective, whose control effects are superior to those by GA-PIDNN control, it is believed that CGA-PIDNN is effective and will become a promising candidate of control methods.