Microwave drying PID (proportion integration differentiation) control method based on increment improved BP (back propagation) neural network

Abstract

The invention discloses a microwave drying PID (proportion integration differentiation) control method based on an increment improved BP (back propagation) neural network, selecting a three-layer BP neural network as a protomodel and combining incremental learning, an L-M (Levenberg-Marquardt) optimization algorithm, a BP neural network and PID control to realize online adjustment of PID control parameters. The method comprises the following steps: firstly carrying out offline system identification on a neural network by adopting the actual production data in a microwave drying process as training data, comparing the output value of the network with the measured value until the mean square error of network training meets the requirement, determining weight and threshold for each layer of the network, and carrying out online dynamic adjustment on the PID control parameters by taking parameters of a controlled object which are measured in the actual production process as the input of the neural network, wherein the output of the neural network is namely parameters of a PID controller Kp, Ki and Kd.

Description

technical field [0001] The invention relates to a method for dynamically and real-time setting the parameters of a microwave drying process PID controller based on an incrementally improved BP neural network, and belongs to the technical field of metallurgical engineering computer neural network control. Background technique [0002] The known PID controller has the advantages of simple structure, strong robustness to model errors and easy operation, etc., and is widely used in industrial process control fields such as metallurgy, chemical industry, electric power, light industry and machinery. With the development of industry, the complexity of the controlled object is increasing, especially for the complex system with large lag, time-varying and nonlinear, the known PID control technology can no longer meet the requirements of precise target control. With its arbitrary approximation ability and self-learning ability of any nonlinear function, BP neural network can understa...

AI Technical Summary

Problems solved by technology

The well-known BP algorithm is based on the gradient descent method, and corrects the network weight by calculating the gradient of the objective function to the network weight and the threshold. In the training process, there are problems of slow convergence speed and local minimum; and for complex problems, in the training process will be trapped in a local minimum point, so that it cannot converge

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