Variation particle swarm optimized BP neural network proportion integration differentiation (PID) control algorithm

Abstract

The present invention discloses a variation particle swarm optimized BP neural network PID control algorithm. The algorithm comprises a step 1 of initializing a BP neural network; a step 2 of initializing a variation particle swarm optimization algorithm; a step 3 of determining an input signal of the BP neural network; a step 4 of calculating a PID control system output adjustable parameter; a step 5 of updating a particle individual optimal value and a global optimum value; a step 6 of carrying out an variation operation; a step 7 of determining whether to end iteration; and a step 8 of determining the PID control system output adjustable parameter. The variation particle swarm optimized BP neural network PID control algorithm of the present invention does not need to establish an accurate mathematical model, can automatically identify a controlled process parameter and an automatic setting control parameter and adapt to the parameter change of a controlled process, and also overcomes the defects that a reverse propagation error correction method of the conventional BP neural network is slow in execution speed, and is caught in the local minimum very easily, and possesses a stronger robustness and a more excellent control effect.

Description

technical field [0001] The invention belongs to the technical field of intelligent control, in particular to a BP neural network PID control algorithm optimized by variant particle swarm optimization. Background technique [0002] PID control is proportional-integral-derivative control. It is a control strategy based on classical control theory. It is the most widely used in industrial process control, with the longest history and the strongest vitality. Among them, more than 90% of the control systems are PID control systems. It adopts a method based on mathematical models. Because of its simple algorithm, good robustness, high reliability, and good control effect, it is widely used in industrial control processes. For traditional PID control systems, before it is put into operation, it must first Set three parameters: proportional coefficient k p , integral coefficient k i , differential coefficient k d , in order to get the best control effect, if the control system p...

AI Technical Summary

Problems solved by technology

However, in the process of its practical application, the BP neural network PID control system designed by BP neural network has low learning efficiency, slow convergence speed, weak global search ability and easy to fall into local optimum due to the backpropagation learning algorithm. The effect is not ideal, which limits the application of neural network in PID controller

[0004] Paper: Xu Shengnan et al. Neural Network Adaptive Control Algorithm Based on Particle Swarm Optimization [J]. China Mechanical Engineering, 2012, 23(22), 2732-2738. Disclosed a neural network adaptive control algorithm based on particle swarm optimization , overcome the defects that the neural network is easy to fall into local minimum and slow convergence

Although the neural network adaptive control algorithm of particle swarm optimization is simple and easy to operate, and overcomes the BP algorithm to a certain extent, it is easy to fall into the local optimal value, but in the later stage of algorithm optimization, there are also situations such as reduced optimization speed and premature maturity. , leading to its weak global search ability, therefore, its control effect is not good and leads to relatively large errors in individual regions

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