Proportional-integral-derivative controller optimization by particle swarm optimization and back propagation neural network for a parallel stabilized platform in marine operations

Abstract

The parallel stabilized platform is an important equipment to ensure the stability of marine operations, which can effectively mitigate collisions of lifted goods. The 3UPU_UP parallel stabilized platform system is proposed, and the kinematics and dynamics of the 3UPU_UP parallel stabilized platform are researched, where U denotes universal joint; P denotes prismatic joint. The platform motion can be realized by controlling the actuators to realize compensation ship motion. While conventional control algorithms are difficult to cope with complex nonlinear systems, the optimization control method has been proposed. Based on the back propagation (BP) neural network proportional-integral-derivative (PID) controller, the particle swarm optimization (PSO) is combined with the BP neural network, and the combined PSO-BP PID controller optimization algorithm is introduced into the servo control system. Compared with classic PID and BP PID, the overshoot of PSO-BP PID to the response of step signal is reduced by 10.2% and 11.48%, respectively, and 0.36s and 0.17s reduce the time required to reach stable states. By comparing the reduction of the error under various sea conditions, the upper platform tracking error curve is flatter under a PID controller based on PSO-BP, the tracking motion is closer to the theoretical value and the control accuracy is improved. The maximum tracking error of the upper platform is smaller, the overall motion vibration of the platform is reduced, and the anti-interference ability is enhanced. The PSO-BP PID controller for wave compensated platform servo system control is more reasonable, more adaptable to the complex and changing environment.