Abstract
In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
Highlights
The complexity of the dynamic system makes it generally urgent to develop process control technology
Several simulations based on different methods are provided and the results show that the hybrid algorithm has high search capacity and effective application in parameters’ optimization
In order to imitate the actual pressure of the hydraulic support, a hybrid algorithm with particle swarm algorithm (PSO)
Summary
The complexity of the dynamic system makes it generally urgent to develop process control technology. In order to improve the control accuracy of systems, many intelligence methods were researched and compared [10]. Due to the poor performance of the Z-N method, intelligence algorithms were put into use for tuning the PID controller. PSO is an excellent method for solving the problem of self-tuning the PID controller. In order to improve the global search ability and convergence characteristics, this paper combined GA and PSO for self-tuning the PID controller. The results showed the obvious improvement in optimizing the parameters of the PID controller.
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