Parameter self-tuning method based on partial derivative information for siso full-format model-free controller

Abstract

The invention discloses a parameter self-tuning method of an SISO full-format model free controller based on partial derivative information. The partial derivative information is used as an input of aBP neural network, the BP neural network performs forward calculation and outputs a penalty factor, a step length factor and other parameters to be set of a controller through an output layer, a control algorithm of the controller is used to calculate and obtain control input for a controlled object, the gradient information of the control input for each parameter to be set is calculated, with aminimum value of a system error function as a target, a gradient descent method is used, combined with gradient information, the system error backpropagation calculation is carried out, a hidden layerweight coefficient and an output layer weight coefficient of the BP neural network are updated in real time in an online mode, and the gradient information is stored as the partial derivative information to be an input of the BP neural network of a next time. The invention provides the parameter self-tuning method of an SISO full-format model free controller based on partial derivative information, the tuning problem of the controller parameters can be effectively overcome, and a good control effect is achieved.

Description

technical field [0001] The invention belongs to the field of automatic control, and in particular relates to a parameter self-tuning method of a SISO full-format model-free controller based on partial derivative information. Background technique [0002] Model-free controller is a new type of data-driven control method, which does not rely on any mathematical model information of the controlled object, but only relies on the real-time measured input and output data of the controlled object for controller analysis and design, and realizes concise, computational The burden is small and the robustness is strong, and the unknown nonlinear time-varying system can also be well controlled, and has a good application prospect. [0003] There are many ways to realize the model-free controller, among which the SISO (Single Input and Single Output, single input and single output) full-format model-free controller is one of the main ways to realize the model-free controller. The theore...

AI Technical Summary

Problems solved by technology

The lack of effective parameter setting methods not only makes the debugging process of the SISO full-format model-free controller time-consuming and laborious, but also sometimes seriously affects the control effect of the SISO full-format model-free controller, restricting the development of the SISO full-format model-free controller. Promote application

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