Diagonal recurrent neural network control strategy based on Q learning algorithm

A recursive neural network and learning algorithm technology, applied in the field of diagonal recursive neural network control strategy, can solve the problem of PID neural network initialization for a long time, to improve dynamic characteristics and robustness, speed up iteration speed, and enhance anti-interference ability Effect

Active Publication Date: 2020-09-15
CHANGCHUN UNIV OF TECH
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The disadvantage of this method is that it takes a lo

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  • Diagonal recurrent neural network control strategy based on Q learning algorithm
  • Diagonal recurrent neural network control strategy based on Q learning algorithm

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[0051] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be noted that the technical features and combinations of technical features described in the following embodiments should not be considered as isolated, and they can be combined with each other to achieve better technical effects.

[0052] Such as figure 1 As shown, the present invention proposes a diagonal recurrent neural network control strategy based on the Q learning algorithm. The specific architecture includes a Q learning algorithm optimized DRNN module and a brushless DC motor. The specific control method is as follows: sampling to obtain the brushless DC motor input Speed ​​Y d (k) and output speed y(k), calculate speed error e(k)=Y d (k)-y(k), according to the speed error e(k), for e(k), e(k)-e(k-1), e(k)-2e(k-1)+e(k -2) Perform normalization processing as the input x of Q-DRNN 1 ,x 2 ,x 3 . At this time, the action a(k) of ...

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Abstract

The invention designs a diagonal recurrent neural network (DRNN) control strategy (Q-DRNN) based on a Q learning algorithm. The Q-DRNN organically combines the strong search capability of Q learning with the advantages of the built-in recursive ring structure, the dynamic mapping capability, the adaptive time-varying characteristic and the like of the DRNN, and is used for improving the working stability of a brushless direct current motor (BLDCM). In the Q-DRNN, the DRNN iterates an output variable through a unique recursive ring in a hidden layer, and optimizes the key weight of the output variable so as to accelerate the iteration speed; and meanwhile, the improved Q learning is introduced to correct the weight term factor of the DRNN, so that the DRNN has self-learning and online correction capabilities, the anti-interference capability and robustness of the system are enhanced, and the brushless direct current motor achieves a better control effect.

Description

technical field [0001] The invention belongs to the field of brushless direct current motor control methods, and in particular relates to a diagonal recursive neural network control strategy based on a Q learning algorithm. Background technique [0002] Due to its simple structure, large output and high efficiency, brushless DC motors have been widely used in the fields of national defense, aerospace, robotics, industrial process control, precision machine tools, automotive electronics, household appliances and office automation. The brushless DC motor plays an important role in the modern motor speed control system. Therefore, it is of great practical significance and application prospect to study the speed control strategy of the brushless DC motor with fast response speed, strong adjustment ability and high control precision. [0003] PID control is one of the earliest linear control strategies and has a long history. It remains the most commonly used control algorithm i...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/04
Inventor 王宏志王婷婷胡黄水韩优佳
Owner CHANGCHUN UNIV OF TECH
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