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Adaptive dynamic surface control method based on RBF neural network compensation

A technology of dynamic surface control and neural network, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the stability of primary permanent magnet linear motor parameter time-varying control system, etc., to ensure robustness Effect

Pending Publication Date: 2021-05-28
SHENYANG INST OF ENG
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of the stability of the control system under the influence of time-varying parameters of primary permanent magnet linear motors and unmodeled load disturbances, and to provide an adaptive dynamic surface control method based on RBF neural network compensation

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  • Adaptive dynamic surface control method based on RBF neural network compensation
  • Adaptive dynamic surface control method based on RBF neural network compensation
  • Adaptive dynamic surface control method based on RBF neural network compensation

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[0013] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. The specific embodiments described here are only used to explain the present invention, not to limit the invention.

[0014] see Figure 1-Figure 2 , this embodiment specifically provides an adaptive dynamic surface control method based on RBF neural network compensation, and the specific steps of the control method are as follows.

[0015] First, the primary permanent magnet linear motor three-phase winding current i a i b i c After Clark coordinate transformation and Park transformation, the current signal i in the two-phase rotating dq coordinate system is obtained d i q , combined with the displacement and velocity of the motor, the state space equation of the primary permanent magnet linear motor in the dq coordinate system is obtained.

[0016] The state space equ...

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Abstract

The invention discloses a self-adaptive dynamic surface control method based on RBF neural network compensation, and the method comprises the steps: firstly guaranteeing the convergence and robustness of a proposed control scheme for the external time-varying disturbance of a primary permanent magnet linear motor through employing a backstepping method according to the Lyapunov stability theory; moreover, solving the problem of differential expansion in backstepping control by introducing an instruction filter, and finally,compensating unmodeled load disturbance suffered by the primary permanent magnet linear motor through an RBF neural network, so that the displacement control of a closed-loop signal of the primary permanent magnet linear motor can be achieved, and meanwhile, the robustness of the provided control method for parameter uncertainty is ensured.

Description

technical field [0001] The invention relates to the technical field of motor control, in particular to an adaptive dynamic surface control method based on RBF neural network compensation. Background technique [0002] Compared with the traditional permanent magnet linear motor, the primary permanent magnet linear motor has the advantages of large electromagnetic thrust and low cost. Therefore, it has received more and more attention in the field of rail transit. The direct thrust control adopts stator magnetic field orientation, calculates the flux linkage and thrust of the motor in the stator coordinate system, and limits the amplitude of thrust fluctuation and flux linkage within a certain range through hysteresis comparison between the regulator and the given value. Compared with the traditional vector control, the direct thrust control cancels the complex coordinate transformation process in the process, the algorithm is simple, and the dynamic response speed is fast, w...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042Y02T10/72
Inventor 王秀平王一名曲春雨王楠姜胜龙葛子荻姚舜禹刘万明王长华王雅慧刘帅郭子琦胡永壮
Owner SHENYANG INST OF ENG
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