System error-based parameter self-setting method of MISO full-format model-free controller

Abstract

The invention discloses a system error-based parameter self-setting method of an MISO full-format model-free controller. According to the invention, a system error set is used as input of a BP neuralnetwork, and the BP neural network performs forward calculation and outputs a penalty factor, a step factor and other MISO full-format no-model controller to-be-set parameters through an output layer.The calculation is performed by adopting a control algorithm of the MISO full-format model-free controller, and then a control input vector for a controlled object is obtained through calculation. The value of a system error function is minimized as a target, and the gradient descent method is adopted. The gradient information sets of all to-be-set parameters are respectively set according to thecontrol input and the reverse propagation calculation of the system error is performed. The hidden-layer weight coefficient of the BP neural network is updated on line in real time, and then the hidden-layer weight coefficient is outputted for realizing the parameter self-setting of the controller based on the system error. According to the invention, the self-setting method of the MISO full-format model-free controller is proposed based on the system error. The problem of the parameter online setting of the controller can be effectively solved. The good control effect of the MISO system is achieved.

Description

technical field [0001] The invention belongs to the field of automatic control, in particular to a parameter self-tuning method of a MISO full-format model-free controller based on system errors. 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 a good application prospect. ...

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 the 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 controllers time-consuming and laborious, but also sometimes seriously affects the control effect of MISO full-format model-free controllers, restricting the development of MISO full-format model-free controllers. Promote application

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