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Self-adaptive robust control method for compensating dead zone inversion error based on neural network

An adaptive robust, neural network technology, applied in the field of adaptive robust control based on neural network compensation of dead zone inversion error, can solve the problem of non-linear dead zone of motor servo system, and achieve the goal of overcoming the influence of control accuracy. Effect

Active Publication Date: 2020-01-10
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0004] The present invention proposes an adaptive robust control method based on neural network compensation for dead zone inversion errors, which solves the problem of dead zone nonlinearity in motor servo systems

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  • Self-adaptive robust control method for compensating dead zone inversion error based on neural network
  • Self-adaptive robust control method for compensating dead zone inversion error based on neural network
  • Self-adaptive robust control method for compensating dead zone inversion error based on neural network

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[0107] To verify the effectiveness of the ARCNN controller proposed in this paper, we will compare the tracking performance of four other general-purpose controllers with the ARCNN controller under two operating conditions, i.e., high-frequency and low-frequency tracking modes.

[0108] A total of five different controllers are listed below:

[0109] 1) PID: This is the well-known traditional three-loop proportional-integral-derivative controller. Based on the position loop, we choose k in the simulation p =-900,k i =-6000,k d =0, representing proportional gain, integral gain and differential gain respectively.

[0110] 2) FBL: This is a feedback linearization controller, and the control parameters are selected as k 1 =0.05,k 2 = 0.005.

[0111] 3) FBLNN: This is a feedback linearization controller with a neural network, where the network is also used to compensate dead zone errors. We choose the control parameters as k 1 =0.05,k 2 =0.005, Γ 2 = 0.1.

[0112] 4)...

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Abstract

The invention discloses a self-adaptive robust control method for compensating a dead zone inversion error based on a neural network and belongs to the field of electromechanical servo control. The invention is based on an adaptive robust control method, for the problem of dead zone nonlinearity widely existing in a motor servo system, a smooth and continuous mathematical model is used to provideapproximate inverse transformation of a dead zone required for feedback linearization, a single-hidden-layer neural network capable of performing online learning is designed to compensate an inversionerror from approximate inversion, in addition, a parameter adaptive law for processing parameter uncertainty is derived, a nonlinear robust feedback term is designed to suppress the influence of imperfect modeling, a compensation error or other interferences, the Lyapunov theorem is used for proving the stability of a proposed control algorithm, and a wide comparison simulation result shows thata proposed adaptive robust controller for compensating the dead zone inversion error based on the neural network has better control performance.

Description

technical field [0001] The invention relates to the technical field of motor servo control, in particular to an adaptive robust control method based on neural network compensation for dead zone inversion errors. Background technique [0002] DC motors have the advantages of fast response, high transmission efficiency, convenient maintenance, and convenient energy acquisition, so they are widely used in industry. With the needs of industrial development, high-precision motion control has become the main development direction of modern DC motors. In the motor servo system, due to changes in working conditions, external disturbances and modeling errors, when designing a controller, there will be a lot of model uncertainty, especially uncertain nonlinearity (such as parameter uncertainty, non-linear Linear friction and external disturbances, etc.), it will seriously deteriorate the control performance that can be achieved, resulting in low control accuracy, limit cycle oscillati...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 胡健曹书鹏
Owner NANJING UNIV OF SCI & TECH
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