Particle filter and RBF identification-based neural network PID control parameter self-setting method

Abstract

The invention discloses a particle filter and radial basis function (RBF) identification-based neural network proportion integration differentiation (PID) control parameter self-setting method used for a control system, the object model of which is unknown and the interference of which is on-linear and non-Gaussian noise. The method comprises the following steps of: connecting the output of a PID controller and the system output to the input of an RBF neural network identification structure respectively, and connecting a particle filter part between the system output and the RBF neural network identification structure; and filtering the system output by using particle filter to obtain particle filter output, training the RBF neural network by using the difference value of the particle filter output and the RBF neural network output as a target function to obtain the RBF neural network output, then calculating Jacobian information of the system, finally training a neuron by using the deviation signal between the system reference input and the system output as a target function, guiding the neuron by using the Jacobian information, and adjusting the PID controller by a learning algorithm. At the same time of keeping the characteristics of high PID control robustness, good reliability and the like, the method can further improve the dynamic response performance and the interference resistance of the control system.

Description

technical field [0001] The invention specifically relates to a method for self-tuning of neural network PID control parameters, which belongs to the field of control systems and is used for control systems with unknown object models and nonlinear and non-Gaussian noise interference. Background technique [0002] In the actual industrial production process, the control object often has nonlinear, time-varying uncertainty and the interference of various nonlinear and non-Gaussian noises in the control process. It is difficult to establish an accurate mathematical model for the control object, and the parameter self-tuning method is complicated. Therefore, Conventional PID controllers are often difficult to achieve good control results. [0003] In order to improve the control accuracy and the robustness of the system, former Soviet scholars proposed a compound control system on the basis of the principle of invariance of the control system. The advantage of the composite cont...

AI Technical Summary

Problems solved by technology

[0009] The beneficial effects of the present invention are: the present invention obtains accurate Jacobian information through particle filter and RBF system identification, that is, the sensitivity information of the output of the control object to the control input, and solves the problem of unknown Jacobian information

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