System-error-based parameter self-setting method of SISO partial-format model-free controller
A parameter self-tuning and system error technology, applied in general control systems, control/regulation systems, adaptive control, etc., can solve the problem of time-consuming and laborious debugging process of SISO partial model-free controllers, and restrict SISO partial model-free control. The promotion and application of the device, the lack of effective tuning means, etc., to achieve the effect of good control effect
Active Publication Date: 2018-04-20
ZHEJIANG UNIV
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The lack of effective parameter tuning methods not only makes the debugging process of the SISO partial format model-free controller time-consuming and laborious, but also sometimes seriously affects the control effect of the SISO partial format model-free controller, restricting the development of the SISO partial format model-free controller. Promote application
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[0041] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0042] figure 1 The principle block diagram of the present invention is given. Determine the control input linearization length constant L of the SISO partial model-free controller, L is an integer greater than 1; the parameters of the SISO partial model-free controller include the penalty factor λ and the step factor ρ 1 ,…,ρ L ; Determine the SISO partial format model-free controller to be tuned parameters, the SISO partial format model-free controller to be tuned parameters, part or all of the SISO partial format model-free controller parameters, including penalty factor λ and step size factor ρ 1 ,…,ρ L any one or combination of any of the figure 1 Among them, the parameters to be tuned for the SISO partial scheme model-free controller are the penalty factor λ and the step factor ρ 1 ,…,ρ L ; Determine the number of input layer nodes...
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The invention discloses a system-error-based parameter self-setting method of a single-input-and-single-output (SISO) partial-format model-free controller. A system error and a function group are usedas inputs of a BP neural network. The BP neural network carries out forward calculation and outputs to-be-set parameters like a penalty factor and a step-size factor of an SISO partial-format model-free controller through an output layer. Calculation is carried out by using a control algorithm of the SISO partial-format model-free controller to obtain a control input for a controlled object; andwith minimization of a value of a system error function as an objective, system error back-propagation calculation is carried out on gradient information of all to-be-set parameters by combining the control input based on a gradient descent method. A hidden layer weight coefficient and an output layer weight coefficient of the BP neural network are updated in real time in an online manner and system-error-based self setting of the parameters of the controller is realized. With the system-error-based parameter self-setting method, the setting problem of the parameters of the controller can be solved; and the good control effect is realized.
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 SISO partial format model-free controller based on system errors. Background technique [0002] 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 real-time measured input and output data of the controlled object for controller analysis and design, and realizes concise, computational The burden is small and the robustness is strong, and the unknown nonlinear time-varying system can also be well controlled, and has a good application prospect. [0003] There are many implementation methods of model-free controllers, among which SISO (Single Input and Single Output, single input and single output) partial format model-free controller is one of the main implementation methods of model-free controllers. The theoret...
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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 卢建刚李雪园
Owner ZHEJIANG UNIV