Reinforcement learning based optimal tracking control method for unknown servo system

A servo system, tracking control technology, applied in control systems, adaptive control, general control systems, etc., can solve problems such as high energy consumption

Inactive Publication Date: 2019-06-28
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of high energy consumption in the process of servo system tracking a

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  • Reinforcement learning based optimal tracking control method for unknown servo system
  • Reinforcement learning based optimal tracking control method for unknown servo system
  • Reinforcement learning based optimal tracking control method for unknown servo system

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Embodiment Construction

[0062] Known conditions: The motor servo system is used in each joint of the electric intelligent robot. The joints rotate according to the predetermined target trajectory, and the controller needs to be designed. Traditional PID controllers and sliding mode controllers will cause problems such as overshooting and vibration during the joint rotation process, and large energy consumption. In order to make the joint rotation process stable and minimize energy consumption, thereby prolonging the battery life, the present invention minimizes the performance index including tracking error and input by solving the optimal control, so as to achieve the optimal accumulated error and the minimum energy consumption during the joint operation process. Purpose.

[0063] A reinforcement learning optimal tracking control method for an unknown servo system, comprising the following steps:

[0064] Step 1. According to the mechanism modeling method, according to the structure and physical l...

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Abstract

The invention mainly relates to a design method of a reinforcement learning based optimal tracking controller for a model unknown servo system. The design method of the reinforcement learning based optimal tracking controller for the model unknown servo system is introduced mainly on the basis of a simplified reinforcement learning evaluation-execution structure with a high-order neural network approach method, and the optimal tracking control solution speed of a motor is increased. As for the model unknown servo system, firstly, homeostatic control is solved with a multilayer neutral networkintelligent identification system model; performance indexes are given, and a high-order neutral network approach optimal performance index function is applied; an HJB (Hamilton-Jacobi-Bellman) equation is established according to an approximate performance index function and the identification system model, and the optimal feedback control of the servo system is solved. The optimal tracking control is calculated according to the solved homeostatic control and optimal feedback control, so that tracking error accumulation values and system energy consumption are minimized simultaneously while load rotation angle and rotation speed rapidly track given signals.

Description

technical field [0001] The invention relates to a reinforcement learning optimal tracking control method for an unknown servo system, belonging to the technical field of intelligent control. Background technique [0002] Nowadays, the control method for servo system is mainly PID control. In order to achieve better control effects, control methods such as adaptive control, sliding mode control, and active disturbance rejection control are used to control the servo system. These control methods not only require the dynamics of the servo system to be known, but also consume a lot of energy during the process of the servo system tracking a given signal, that is, the tracking performance cannot be optimized. Contents of the invention [0003] The purpose of the present invention is to solve the problem of high energy consumption in the process of servo system tracking a given signal in the existing method, and to provide a reinforcement learning optimal tracking control metho...

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Application Information

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IPC IPC(8): G05B13/04G05B13/02H02P23/00
Inventor 任雪梅吕永峰李慧超李林伟
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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