Model-free robust finite-time force tracking control for piezoelectric actuators using time-delay estimation with adaptive fuzzy compensator

Kang, Shengzheng; Wu, Hongtao; Yang, Xiaolong; Li, Yao; Wang, Yaoyao

doi:10.1177/0142331219869708

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…Using the hyperbolic tangent function instead of symbolic functions can weaken the jitter, we use the switching control law as: (16) The time derivative of is:…”

Section: Design Of Rbfnn-based Sliding Mode Controllermentioning

confidence: 99%

“…Dehghani, et al [15] used a visual inspection method to calibrate a Hexa parallel robot. In previous work [16], a continuous nonsingular fast terminal sliding mode (CNFTSM) was developed for control of Stewart platforms in combination with perturbation estimation to obtain a chattering-free robust controller in the presence of time-varying external disturbances. However, many parameters need to be identified such as initial link lengths of Stewart and initial coordinates of hinge points, which increase the difficulty of calibration.…”

Section: Six-axis Parallel Platform Kinematics Analysismentioning

confidence: 99%

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Trajectory tracking based on neural network sliding mode controller

Yu¹

2023

Preprint

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In this paper, we aim to improve the tracking performance of the manipulator joint system under the presence of the uncertainties, such as modelling error, friction, and external disturbance. Firstly, the nonsingular fast terminal sliding mode control is developed to guarantees a finite-time convergence and to solve the singular issue of the terminal sliding mode control. Secondly, in view of the established system model, an adaptive sliding mode controller (SMC) based on radial basis function neural network (RBFNN) and sliding mode variable structure control theory is designed for the tracking of the bi-joint manipulator and six-degree of freedom parallel robot. Finally, the results show that our method improves the robustness of the adaptive RBFNN controller further, weakens the chattering phenomenon, reduces error, and has an excellent control performance.

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“…Using the hyperbolic tangent function instead of symbolic functions can weaken the jitter, we use the switching control law as: (16) The time derivative of is:…”

Section: Design Of Rbfnn-based Sliding Mode Controllermentioning

confidence: 99%

Section: Six-axis Parallel Platform Kinematics Analysismentioning

confidence: 99%

Trajectory tracking based on neural network sliding mode controller

Yu¹

2023

Preprint

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“…This kind of models are based entirely on data-driven methodologies and have rather simple shapes. Fuzzy models [ 34 ], autoregressive-moving-average (ARMA) [ 35 ], machine learning based models [ 36 ], and neural network-based models [ 37 , 38 ] are a few examples. The latter type of modeling will be the one implemented in this research.…”

Section: Introductionmentioning

confidence: 99%

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Uralde

Artetxe

Barambones

et al. 2023

Sensors

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Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change.

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“…To overcome the above shortcoming and improve the robustness of the system, many estimation techniques have been reported to estimate system uncertainties online, such as disturbance observer (DO), 24 fuzzy logic systems (FLS), [25][26][27] perturbation estimation (PE), 28,29 and function estimation technique (FET). 30,31 In the design process of the DO, the bandwidth and order of the low-pass filter play a decisive role in the stability of the system, so it needs careful design and is difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties

Kang

Bai

et al. 2023

Measurement and Control

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Piezoelectric micropositioning systems (PMSs) have been widely utilized in the high-precision manipulation applications, but are also subjected to undesired nonlinearities, like hysteresis, and parameter uncertainties. To solve this problem, this paper proposes a new robust sliding mode control scheme for a class of nonlinear PMSs with time-varying uncertainties. Different from the conventional sliding mode control (SMC), the proposed controller further combines the Fourier series-based function estimation technique, fuzzy logic system and adaptive learning algorithm to realize online estimation and compensation of system time-varying uncertainties without their boundary information. The adaptive laws of Fourier coefficients and fuzzy adjustable parameters are obtained via the Lyapunov stability theory. Compared with the existing SMC methods, the proposed control effectively eliminates the control chattering problem, and guarantees the convergence of the tracking error in finite time in the presence of time-varying uncertainties. Theoretical analysis and numerical simulation results show that the proposed control strategy can meet the high-speed, high-precision robust tracking performance requirements of PMSs for micro/nano-manipulation applications.

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Model-free robust finite-time force tracking control for piezoelectric actuators using time-delay estimation with adaptive fuzzy compensator

Cited by 15 publications

References 37 publications

Trajectory tracking based on neural network sliding mode controller

Trajectory tracking based on neural network sliding mode controller

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Robust high-precision tracking control for a class of nonlinear piezoelectric micropositioning systems with time-varying uncertainties

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