In the electro-hydraulic servo control system, there are some problems, such as low position control accuracy, uncontrollable speed, speed impact, and asymmetric control due to the asymmetry of hydraulic cylinder. To solve the above problems, this paper proposes a Fuzzy PID control algorithm with load force compensation based on improved PSO optimization. This improved particle swarm optimization algorithm introduces crossover and mutation operations in genetic algorithm to improve the optimization performance of traditional particle swarm optimization algorithm. The mathematical model of valve controlled asymmetric cylinder is established, and the Amesim linearization analysis function is used to identify the system, establish the transfer function, and build the control system. The joint simulation and test results of Amesim and MATLAB show that the proposed control strategy can realize the simultaneous control of speed and position. Under different loads, different speeds, and different input follow signals, it does not need to adjust any parameters. Regardless of forward or reverse operation, the optimal dynamic performance and minimum steady-state error are obtained, which greatly improves the response speed of the system. The effectiveness and superiority of the improved PSO compound control strategy are verified.

A novel intelligent neural network control scheme which integrates the merits of fuzzy inference, neural network adaptivity and simple PID method is presented in this paper. This control method overcomes the defects existed in the traditional variable frequency induction motor driven hydraulic source, such as slow response, poor control precision, easy to overshoot. Permanent magnet synchronous motor driven constant pump hydraulic system is designed instead of common motor, energy saving, fast response and easy to realize closed loop control. System uses the structure of the combination of neural network control and RBF network online identification. The parameters of the controller are optimized by PSO algorithm offline and error back propagation (BP) algorithm offline, and a RBF network is built to identify the system online. The hydraulic power system’s control simulation experiments are conducted, and the experimental results at the typical working conditions of the hydraulic source show that the controller and its optimization algorithm can effectively improve the system performance, and the system has no steady-state error, good dynamic performance and good robustness, superior to conventional fuzzy controller and PID controller.