Parameter self-setting method of MISO full format model-free controller based on deviation information

Abstract

The invention discloses a parameter self-setting method of a MISO full format model-free controller based on deviation information. The parameter self-setting method includes the steps of using deviation information as the input of a BP neural network, the BP neural network performing forward calculation and outputting a penalty factor, a step factor and other to-be-set parameters of an MISO fullformat no-model controller through an output layer, calculating to obtain a control input vector for a controlled object by adopting a control algorithm of the MISO full format no-model controller, conducting system error reverse propagation calculation for gradient information sets of all the to-be-set parameters by using a gradient descent method in combination with control output with minimization of the value of a system error function as a target, on-line updating the hidden layer weight coefficient of the BP neural network in real time, outputting the layer weight coefficient, and realizing the self-setting of parameters based on the deviation information. The parameter self-setting method can effectively overcome the online setting difficulty of the parameters of a controller, and agood control effect on an MISO system 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 MISO full-format model-free controller based on partial derivative information. Background technique [0002] The control problem of MISO (Multiple Input and Single Output) system has always been one of the major challenges in the field of automation control. [0003] Existing implementations of MISO controllers include MISO full-format model-free controllers. MISO full-format model-free controller is a new type of data-driven control method that does not rely on any mathematical model information of the controlled object, but only relies on the input and output data measured by the MISO controlled object in real time for controller analysis and design, and The realization is simple, the calculation burden is small and the robustness is strong, and the unknown nonlinear time-varying MISO system can also be well controlled, and has ...

AI Technical Summary

Problems solved by technology

[0006] However, the MISO full-format model-free controller needs to rely on empirical knowledge to pre-set the penalty factor λ and the step size factor ρ before it is actually put into use. 1 ,…,ρ Ly+Lu and other parameters, the penalty factor λ and step size factor ρ have not yet been realized in the actual commissioning process 1 ,…,ρ Ly+Lu Online self-tuning of other parameters

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

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