Self-adaptive control method for reinforcement learning of brush DC motor

A brushed DC motor, self-adaptive control technology, applied in the field of motors, can solve problems such as dead zone, crawling and low-speed instability

Active Publication Date: 2018-08-24
JILIN UNIV
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Problems solved by technology

[0010] The purpose of the present invention is to solve the problem of dead zone and crawling in the speed control process of brushed DC motors through online identification of cogging torque, nonlinear friction model paramet

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  • Self-adaptive control method for reinforcement learning of brush DC motor
  • Self-adaptive control method for reinforcement learning of brush DC motor
  • Self-adaptive control method for reinforcement learning of brush DC motor

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Embodiment Construction

[0123] Through continuous research and practice, the inventors of the present invention found that the appropriate adaptive rate can estimate the friction torque and cogging torque parameters online, so as to achieve the purpose of accurately compensating the motor friction torque and cogging torque; The unknown disturbance estimation and compensation strategy can suppress the impact of unknown disturbances on the smoothness of the motor's steady-state operation, and this method can bring better speed tracking performance to the motor. Based on the mathematical model of the brushed DC motor, the invention designs an adaptive control method of the brushed DC motor based on reinforcement learning.

[0124] The steps of the present invention are:

[0125] (1) Establish the mathematical model of the brushed DC motor

[0126] figure 1 It is the schematic diagram of the brushed DC motor circuit. It can be seen that the equivalent circuit of the brushed DC motor is the series conne...

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Abstract

A self-adaptive control method for reinforcement learning of a brush DC motor belongs to the technical field of motors. The invention aims to provide a non-linear control method for a rotational speedof the brush DC motor, the problems of dead regions, creeping and low-speed instability of the brush DC motor in the rotational speed control process are solved by parameter robustness self-adaptiveon-line identification cogging torque, a non-linear friction force model parameter and a reinforcement learning-based known disturbance estimation compensation strategy. The self-adaptive control method comprises the steps of building a mathematic model of the brush DC motor; performing the robustness self-adaptive control method of the brush DC motor. Differential flatness design-based feedforward and feedback two-degree of freedom control structure is employed. Compared with a traditional dual-ring PI control method, the method has the advantages that introduced feedforward control can act on a controlled object at the moment of a reference input rather than deviation occurring, a non-linear compensation signal is introduced into feedforward, the influence of disturbance on low-speed control of the motor can be prevented, and the rotational speed tracking accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of motors. Background technique [0002] Brushed DC motor is an important industrial basic component, which has the advantages of large torque coefficient, strong overload capacity and high reliability, and is widely used in automobiles, robots, aerospace and other fields. With the rapid development of modern science and technology, especially the great progress of power electronics, digital control technology and modern control theory, favorable conditions have been created for the development of high-precision speed control of brushed DC motors. The high-precision speed control of brushed DC motors is subject to received more and more attention. The requirements for the control performance of brushed DC motors in many fields are constantly improving, and the difficulty of developing high-precision motor speed control methods has therefore become higher. [0003] Friction torque and cogging torque are two...

Claims

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Application Information

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IPC IPC(8): H02P21/00H02P21/18
CPCH02P21/0017H02P21/18
Inventor 胡云峰李娜张森陈虹史少云
Owner JILIN UNIV
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