Duhem model and inverse compensation controller for trajectory tracking in piezo-actuated micropositioning stage based on neural network

Zhou, Miaolei; Yang, Xiaoning; Zhang, Chen; Pan, Wei; Yu, Yewei; Song, Mingxu; He, Yinhao; Gao, Wei

doi:10.1016/j.sna.2024.115685

Sensors and Actuators A: Physical

2024

DOI: 10.1016/j.sna.2024.115685

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Duhem model and inverse compensation controller for trajectory tracking in piezo-actuated micropositioning stage based on neural network

Miaolei Zhou,

Xiaoning Yang,

Chen Zhang

et al.

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Cited by 2 publications

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Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

Son,

Van Kien

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper introduces a novel Direct Adaptive Neural Control (DANC) approach to enhance the precision of piezoelectric actuator motion control significantly. By effectively addressing the complexities of hysteresis, DANC simplifies system design while delivering superior performance. A Radial Basis Function Neural Network (RBFNN) is employed to handle system uncertainties robustly, and a switching control mechanism guarantees stability, rigorously verified through Lyapunov analysis. Experimental results using a Thorlabs PZS001 actuator convincingly demonstrate substantial performance improvements over conventional PID control, the feedforward-based neural network and feedback control (FF-FB control).

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Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

Son,

Van Kien

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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Force-displacement hysteresis characteristics of PAM based on improved generalised Prandtl-Ishlinskii model

Zhang,

Liu,

Cai

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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As an advanced actuator that simulates the characteristics of biological muscle, the internal structure and working principle of pneumatic artificial muscle imply significant complexity and highly nonlinear characteristics, which are particularly prominent in the theoretical modelling process, and pose many challenging problems for researchers. The classical Prandtl-Ishlinskii model fails to provide ideal mathematical descriptions and accurate models when dealing with the nonlinear characteristics of pneumatic artificial muscle, resulting in the construction of models for such nonlinear systems that still require further research. Based on the above, this paper proposes a generalised Prandtl-Ishlinskii model based on the improved force-displacement hysteresis characteristics of pneumatic artificial muscle, which employs an improved particle swarm optimisation to identify the unknown parameters of the model, and applies the identified parameters to construct the force-displacement hysteresis curves of pneumatic artificial muscle. Comparing the model fitting results with the experimental data, it is found that the maximum error of the improved generalised Prandtl-Ishlinskii model is reduced by more than 50% and the average error is reduced by more than 75% compared with the classical Prandtl-Ishlinskii model when the internal pressure of the pneumatic artificial muscle is 0.35, 0.4 and 0.45 MPa. The improved generalised Prandtl-Ishlinskii model can better characterise the asymmetric hysteresis property, which improves the model accuracy of the force-displacement coupling of pneumatic artificial muscle, and provides a certain theoretical basis for the later realisation of the precise control of the force-displacement coupling of pneumatic artificial muscle.

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Duhem model and inverse compensation controller for trajectory tracking in piezo-actuated micropositioning stage based on neural network

Cited by 2 publications

References 34 publications

Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

Force-displacement hysteresis characteristics of PAM based on improved generalised Prandtl-Ishlinskii model

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