Parameter self-tuning method based on ensemble learning for siso compact model-free controller

Abstract

The invention discloses a parameter self-tuning method of a SISO compact format model-free controller based on integrated learning. The integrated learning algorithm includes three individual algorithms of PSO algorithm, BP neural network and cyclic neural network. Taking the system error as the input of the integrated learning algorithm, three individual algorithms are firstly used to tune the parameters of the SISO compact model-free controller online and output three sets of temporary tuning parameters, and input the results into the controller to calculate the control of the controlled object. Input, calculate three groups of temporary system errors and use the softmax function to calculate the weight ratio of individual algorithms, weight the weight ratio and the temporary tuning parameters to be summed as the final SISO compact model-free controller parameters to be tuned, and realize parameter self-tuning. The SISO compact format model-free controller proposed by the present invention is based on the parameter self-tuning method of integrated learning, combines the advantages of different individual algorithms, enhances the generalization of the algorithm, overcomes the problem of online tuning of controller parameters, and has a good control effect on the SISO system.

Description

technical field [0001] The invention belongs to the field of automatic control, and in particular relates to a parameter self-tuning method of an SISO compact format model-free controller based on integrated learning. Background technique [0002] SISO (Single Input and Single Output) systems are widely used in reactors, precision Distillation towers, machines, equipment, devices, production lines, workshops, factories and other controlled objects. With the continuous improvement of the level of science and technology, industrial devices are becoming larger and more complex, making the production process more and more strongly nonlinear and time-varying. It is often difficult to achieve the ideal control effect for complex controlled objects with linear and time-varying characteristics. Model-free controller is a new type of data-driven control model, which has a good control effect on unknown nonlinear time-varying systems, so it has a good application prospect. [0003]...

AI Technical Summary

Problems solved by technology

[0008] The object of the present invention is to provide a parameter self-tuning method based on ensemble learning for a SISO compact format model-free controller, so as to solve the parameter online self-tuning problem of the SISO compact format model-free controller

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