Self-adaptive parameter setting method and device of active disturbance rejection controller

An active disturbance rejection controller and adaptive parameter technology, applied in the field of reinforcement learning, can solve problems such as poor decision-making ability of parameter setting methods, and achieve the effects of realizing intelligence, improving control performance, and improving robustness

Pending Publication Date: 2022-01-11
NANKAI UNIV
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Problems solved by technology

[0006] In view of this, the purpose of the present invention is to overcome the deficiencies of the prior art and provide a method and device for adaptive parameter tuning of an ADRC controller to solve the problem of poor decision-making ability of the prior art controller parameter tuning method

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  • Self-adaptive parameter setting method and device of active disturbance rejection controller
  • Self-adaptive parameter setting method and device of active disturbance rejection controller
  • Self-adaptive parameter setting method and device of active disturbance rejection controller

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Embodiment Construction

[0060] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other implementations obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0061] A specific ADRC adaptive parameter tuning method and device provided in the embodiment of the present application will be described below with reference to the accompanying drawings.

[0062] Such as figure 1 As shown, this application provides a DQN algorithm-based linear ADRC parameter tuning method, which utilizes the DQN algorithm to optimize the parameter ω in the linear ADRC controller o ,ω c and b 0 .

[0063] Such as figure 2 As shown, the ...

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Abstract

The invention relates to a self-adaptive parameter setting method and device for an active-disturbance-rejection controller. The method comprises the steps: setting uo initial parameters of a linear active-disturbance-rejection controller according to the order of a controlled system; initializing an environment and network parameters in the DQN; according to the initial parameters and the network parameters, adopting an epsilon-greedy strategy to carry out experience accumulation, and storing experience samples in a memory playback unit; training the network by using the experience samples in the memory playback unit to obtain a decision network; and selecting controller parameters by using the trained decision network. The parameter adaptive optimization of the active disturbance rejection controller is realized, the control performance of the controller is improved, the controller obtained through the technical scheme provided by the invention can adapt to control under different working conditions, and the robustness of the controller is improved. Intelligentization of the controller is achieved, and decisions can be made according to the system state on the premise of not depending on model information.

Description

technical field [0001] The invention belongs to the technical field of reinforcement learning, and in particular relates to a method and a device for setting adaptive parameters of an ADRC controller. Background technique [0002] Linear Active Disturbance Rejection Controller is suitable for any situation from knowing nothing about the object model to fully grasping the object model, and has been widely used in recent years. The selection of controller parameters will directly affect the control performance of the controller, so parameter tuning is a part that cannot be ignored in the process of controller design. [0003] In related technologies, controller parameter tuning methods can be divided into two types according to parameter characteristics. One is based on heuristic algorithms, such as particle swarm algorithm or genetic algorithm, through which a set of relative parameters of the controller under certain working conditions can be obtained. optimal parameters. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/08
CPCG06F30/27G06N3/08
Inventor 陶金郑月敏孙青林
Owner NANKAI UNIV
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