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Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization

A neural network, fuzzy immune technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as unsatisfactory work effect

Inactive Publication Date: 2010-06-30
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

[0004] The present invention aims at the problem of unsatisfactory working effect when the PID of the cascade control system controls the complex process of nonlinearity, time-varying, coupling and uncertain parameters and structure, and proposes a RBF neural network tuning PID and fuzzy immune control method , the organic combination of RBF neural network, immune regulation mechanism and conventional PID control system has good control effect and high robustness

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  • Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization
  • Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization
  • Method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization

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Embodiment Construction

[0037] With the development of intelligent control theories such as expert system, fuzzy control, and neural network, a variety of intelligent PID control strategies combining intelligent control and PID control have been formed. Because the neural network has the ability of self-organization, self-learning and self-adaptation, the control based on the neural network has become the most important way in the intelligent control. RBF (Radial Basis Function, radial basis function, hereinafter referred to as RBF) neural network has the ability to approximate any nonlinear mapping, and the network structure is simple, the connection weight of its output is linearly related to the output, and the global optimal The linear optimization algorithm has become a research hotspot.

[0038] The RBF neural network is a three-layer feed-forward network with a single hidden layer. Because it simulates the neural network structure of the local adjustment and mutual coverage of the receiving f...

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Abstract

The invention relates to a method for controlling radial basis function (RBF) neural network tuned proportion integration differentiation (PID) and fuzzy immunization. A main loop adopts RBF neural network tuned PID control, and inputs of an RBF neural network comprise an error e and an output measured value yout; kp, ki and kd are obtained by calculation; after the variables are input to the PID control for carrying out calculation, a variable u1 is output; a subloop adopts fuzzy immunization control, the fuzzy immunization control adopts an error e2 output by the PID control and the variation rate as inputs, and an output is a nonlinear function f(x), and the fuzzy immunization control is used for controlling the subloop by immune calculation; and the control is applied to a serial control system so that the system hardly has overshoot in the process of transition and is more stable.

Description

technical field [0001] The invention relates to an intelligent control technology, in particular to an RBF neural network setting PID and fuzzy immune control method for a cascade control system. Background technique [0002] PID (Proportion Integration Differentiation, proportional-integral-differential) controller has been the earliest practical controller for more than 50 years, and it is still the most widely used industrial controller. The PID controller is easy to understand and does not require precise system models and other prerequisites in use, so it has become the most widely used controller. However, PID does not work well when controlling nonlinear, time-varying, coupling, and complex processes with uncertain parameters and structures. Most importantly, if the PID controller cannot control a complex process, no matter how much you tune the parameters, it is useless. [0003] The cascade control system is composed of two controllers, the main controller and the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02
Inventor 薛阳严振杰叶建华钱虹杨旭红
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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