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LI Guodong
ZHANG Qingchun
LIANG Yingchun
School of Mechanical and
Electrical Engineering,
Harbin Institute of Technology,
Harbin 150001, China
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GA-BASED PID NEURAL NETWORK
CONTROL FOR MAGNETIC BEARING
SYSTEMS*
Abstract:
In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA(genetic algorithm)-based PID neural network controller is designed and trained to emulate the operation of a complete system (magnetic bearing, controller, and power amplifiers). The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes), increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.
Key words:
Magnetic bearing Non-linearity PID neural network Genetic algorithm
Local minima Robust performance
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