Partial deviation information based parameter self-setting method of MIMO tight-format model-free controller

Abstract

The invention discloses a partial deviation information based parameter self-setting method of an MIMO tight-format model-free controller. Partial deviation information serves as input of a BP neuralnetwork, the BP neural network carries out forward calculation and outputs to-be-set parameters, including an output-layer output punishing factor and a step factor, of the MIMO tight-format model-free controller, a control algorithm of the MIMO tight-format model-free controller is used to calculate a control input vector aimed at a controlled object, reverse spreading of system errors is calculated by taking minimizing a value of a system error function as the target, employing a gradient decrease method and controlling input aimed at a gradient information set of parameters to be set, a hidden-layer weight coefficient and an output-layer weight coefficient of the BP neural network are updated online in real time, and parameters of the controller are self-set on the basis of the partialdeviation information. The method can be used to overcome difficulty in online parameter setting of the controller, and has a good control effect for the MIMO 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 a MIMO compact format model-free controller based on partial derivative information. Background technique [0002] The control problem of MIMO (Multiple Input and Multiple Output) system has always been one of the major challenges in the field of automation control. [0003] Existing implementations of MIMO controllers include MIMO compact-form model-free controllers. MIMO compact format 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 input and output data measured by the MIMO controlled object in real time for controller analysis and design, and The implementation is simple, the calculation burden is small and the robustness is strong, and the unknown nonlinear time-varying MIMO system can also be well contro...

AI Technical Summary

Problems solved by technology

[0006] However, the MIMO compact format model-free controller needs to rely on empirical knowledge to pre-set the values ​​of the penalty factor λ and the step size factor ρ before it is actually put into use. On-line self-tuning of parameters such as factor ρ

The lack of effective parameter tuning methods not only makes the debugging process of the MIMO compact model-free controller time-consuming and laborious, but also sometimes seriously affects the control effect of the MIMO compact model-free controller, restricting the performance of the MIMO compact model-free controller. Promote application

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