Research On The AMB Control Based On Neural Networks And Improved PSO Algorithms

Magnetic bearings with no friction, long life, no lubrication, high turning precision, is widely used in aerospace, mechanical processing, power transmission, energy, transportation and other fields. Magnetic bearing control system is an equipment involved electromagnetic, control theory, mechanical theory, rotor dynamics and other subjects of the complex non-linear open-loop unstable system. The research object is the active magnetic bearings. In this paper, improved particle swarm optimization is used to optimize neural network and adjust the PID controller parameters online to achieve closed-loop control of magnetic suspension rotor. In this paper, I do the following work.Magnetic bearing control system is described the basic structure and working principle. The mathematical model of Magnetic bearing system is established by analyzing the relationship between the electromagnetic magnetic bearing control system. The disadvantage of PID control algorithm is described and the improvement measures are proposed. The magnetic bearing PID controller design is proposed on the base of PID algorithms. Then the program is set right parameters to simulation. Deeply analysis the simulation results presented by BP neural network PID controller.It is proposed a magnetic bearing BP neural network PID controller design. On the basis of the complexity of the system to set BP neural network structure, select 2 input neurons,15 hidden layer neurons, three output neurons. The two inputs are the displacement of the rotor magnetic deviation and deviation the rate of change, three outputs are corresponding to the three parameters of PID controller. A group of sample based on input s and outputs data is used to train BP neural network. BP network based on system performance and then adjust the network weights coefficient line to set the optimal PID controller parameters.The particle swarm optimization is introduced to solve the advantage of the BP algorithm. Particle swarm algorithm is improved by analyzing the various parameters on algorithm performance, the corresponding improved method, which highlight the dynamic variation of ideas based on improved particle swarm optimization. The algorithm is mainly about the particle swarm algorithm to improve the weight of inertia, that is, firstly to adjust as the linear attenuation, according to the specific circumstances of the convergence of particle inertia weight for the second amendment. Alternative use of improved particle swarm optimization neural network BP algorithm weight value. On this basis, improved particle swarm optimization based on the magnetic bearing neural network PID control scheme is proposed.Simulation results show that the improved particle swarm optimization design of magnetic bearing PID neural network control scheme not only has excellent magnetic rotor dynamic performance and steady-state performance, but also the control system with good anti-jamming capability.