Output Feedback Adaptive Motion Control and Its Experimental Verification for Time-Delay Nonlinear Systems With Asymmetric Hysteresis

Zhang, Xiuyu; Chen, Xinkai; Zhu, Guoqiang; Su, Chun‐Yi

doi:10.1109/tie.2019.2938460

Cited by 62 publications

(24 citation statements)

References 42 publications

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“…A piezoelectric actuator (PEA) is a transducer that converts electrical energy into a mechanical displacement or stress based on a piezoelectric effect, or vice versa [ 1 , 2 ]. Compared with traditional electromechanical systems, the PEA actuated nanopositioning system has primary advantages [ 3 , 4 , 5 ] including the large mass ratio, sub-nanometer resolution, high positioning accuracy, and fast response characteristic; hence, it is extensively applied in atomic force microscope [ 6 ], ultra-precision mechanical control [ 7 ], biological manipulation [ 8 ], and other related fields.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

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Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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“…Piezoelectric actuators are widely assembled in micro-position and nanoscale systems due to their ability to satisfy ultra-high precision requirement. However, the unavoidable disadvantage existing in piezoelectric actuators is the hysteresis nonlinearity (Chen et al, 2016; Zhang et al, 2019a). The hysteresis is non-differentiable and multi-valued.…”

Section: Introductionmentioning

confidence: 99%

Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Zhao

Shen

et al. 2020

Journal of Intelligent Material Systems and Structures

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Rate-dependent hysteresis nonlinearity in piezoelectric actuators severely limits micro- and nanoscale system performance. It is necessary to establish a dynamic model to describe the full behavior of rate-dependent hysteresis. In this article, the Elman neural network–based hysteresis model is developed for piezoelectric actuators. An improved dynamic hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into one-to-one mapping on a newly constructed expanded input space. Then, Elman neural network incorporated with the improved dynamic hysteretic operator is utilized to approximate the behavior of rate-dependent hysteresis. The combination of Elman neural network and the improved dynamic hysteretic operator can dually embody the dynamic property and is capable of fully extracting the characteristics of rate-dependent hysteresis. The experimental results are presented to illustrate the potential of the proposed modeling technique.

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“…Dynamic surface control introduces the first-order low-pass filter into backstepping control, and filters the virtual control law to avoid the expansion of its terms [23]- [25]. Therefore, DSC method have been utilized in control multi-machine power system [26]- [30]. In [26], NNs and tracking error transformed functions are used in the design of an adaptive DSC scheme for the SIMS.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

et al. 2020

Self Cite

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A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

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Output Feedback Adaptive Motion Control and Its Experimental Verification for Time-Delay Nonlinear Systems With Asymmetric Hysteresis

Cited by 62 publications

References 42 publications

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

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