Parameter self-tuning method based on system error for MIMO partial format model-free controller

Abstract

The invention discloses a parameter self-tuning method based on system error for an MIMO partial format model-free controller. A system error set is used as the input of a BP neutral network. The BP neutral network performs forward calculation, and the to-be-tuned parameters of an MIMO partial format model-free controller, such as penalty factor and step length factor, are output through an outputlayer. A control input vector of a controlled object is calculated by using a control algorithm of the MIMO partial format model-free controller. With the minimum value of a system error function asthe objective, system error back propagation calculation is carried out on the gradient information set of each to-be-tuned parameter through a gradient descent method according to the control input.The hidden layer weight coefficient and the output layer weight coefficient of the BP neutral network are updated online and in real time. Therefore, self-tuning of the parameters of the controller based on system error is realized. The parameter self-tuning method based on system error for an MIMO partial format model-free controller presented by the invention can overcome the difficulty of online controller parameter tuning, and has a good control effect on an 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 partial format model-free controller based on system errors. 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 partial scheme model-free controllers. The MIMO partial 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 controlled, and h...

AI Technical Summary

Problems solved by technology

[0006] However, the MIMO partial scheme 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 L and other parameters, the penalty factor λ and step size factor ρ have not yet been realized in the actual commissioning process 1 ,…,ρ L Online self-tuning of other parameters

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

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