Parameter self-tuning method of MISO partial format model-free controller based on system errors

Abstract

The invention discloses a parameter self-tuning method of a MISO partial format model-free controller based on system errors. The method includes: a system error set is adopted as input of a BP neuralnetwork, the BP neural network performs forward calculation and outputs to-be-tuned parameters of the MISO partial format model-free controller, such as a penalty factor and a step factor, through anoutput layer, a control input vector of a controlled object is obtained through calculation by employing a control algorithm of the MISO partial format model-free controller, the minimization of a value of a system error function is regarded as the target, reverse propagation calculation of the system errors is performed aiming at a gradient information set of each to-be-tuned parameter with thecombination of control input by employing a gradient descent method, weight coefficients of a hidden layer and the output layer of the BP neural network are updated in real time in an online manner, and parameter self-tuning of the controller based on the system errors is realized. According to the parameter self-tuning method of the MISO partial format model-free controller based on the system errors, the online tuning problem of the parameters of the controller can be effectively overcome, and a 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 partial 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 partial scheme model-free controllers. MISO 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 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...

AI Technical Summary

Problems solved by technology

[0006] However, the MISO 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 means not only makes the debugging process of the MISO partial format model-free controller time-consuming and laborious, but also sometimes seriously affects the control effect of the MISO partial format model-free controller, restricting the performance of the MISO partial format model-free controller. Promote application

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