Adaptive nonsingular terminal sliding mode controller for micro/nanopositioning systems driven by linear piezoelectric ceramic motors

Safa, Alireza; Abdolmalaki, Reza Yazdanpanah; Shafiee, Saeed; Sadeghi, Behzad

doi:10.1016/j.isatra.2018.03.027

Cited by 31 publications

(16 citation statements)

References 41 publications

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“…Indeed, a low speed is necessary for microscopy stages, robotic arms, medical operations, and so on. To enable high precision control of the LUSM, various control strategies have been investigated [9]- [14], such as model predictive control, fuzzy neural networks, nonlinear PID and sliding mode.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

et al. 2020

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The control performance of a butterfly-shaped linear ultrasonic motor is not good when the target speed is less than 1 mm/s by traditional methods. To improve low-speed controlling characteristics of linear ultrasonic motor, a closed-loop control strategy by using both the step control and the fuzzy PID control was proposed. The controller was constructed with the function of providing a closed-loop control of the speed by adjusting the driving voltage amplitude in stepping driving mode. The corresponding step controlling parameters were determined by experiments. Comprehensive experiments on the developed control strategy were conducted under different target speeds. There was a maximum of 24.5% speed error at the target speed of 10 μm/s, meanwhile, the coefficient of variation and the response time were 16.3% and 0.11 s, respectively. With the triangular or sinusoidal waves speed tracking curves at amplitude of 1 mm/s, the maximum speed tracking error were 0.1 mm/s and 0.15 mm/s respectively. Thus, a good speed tracking performance was obtained. These results support the functional ability and potential of the proposed method as a low-speed precision control method for linear ultrasonic motors. INDEX TERMS linear ultrasonic motor, step control, fuzzy PID control, low speed NOMENCLATURE Coefficient of variation Maximum speed error of the platform Speed error of the platform Change of speed error of the platform Proportional coefficient of the PID controller Initial value of proportional coefficient Change of proportional coefficient Integral coefficient of the PID controller Initial value of integral coefficient Change of integral coefficient Differential coefficient of the PID controller Initial value of differential coefficient Change of differential coefficient Number of driving waves of the driving signal Set speed of the platform Coefficient of determination Driving period of the driving signal Average speed of the platform Actual position of the platform Actual speed of the platform Standard deviation of the platform speed

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Section: Introductionmentioning

confidence: 99%

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

et al. 2020

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“…However, when the system is in a specific subspace of the state space, the controller's output signal will appear infinite. In [18], adaptive nonsingular terminal sliding mode control method is adopted. Adaptive control is used to estimate the upper bound of system uncertainty and convert the discontinuous signal function into the time derivative of the control input.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor

Zhao

2019

IEEE Access

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For the problem that the position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is easily affected by uncertain factors such as parameters change, load disturbance and friction and so on, an adaptive neural network nonsingular fast terminal sliding mode control (ANNNFTSMC) method is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the nonsingular fast terminal sliding mode control (NFTSMC) can avoid the singularity problem and make the state of the system converge to the equilibrium point quickly, so as to improve the response speed of the system. Secondly, in order to minimize the influence of disturbance and dynamic uncertainty, the dynamic model of PMLSM servo system is estimated by RBF neural network, and the uncertain upper bound of PMLSM servo system is estimated in real time combined with adaptive control, which weakens the chattering phenomenon and enhances the robustness of the system. It is proved theoretically that the control scheme can make the system achieve fast convergence and good tracking. Finally, the system experiments show that the proposed control scheme has the advantages of high tracking accuracy, good robustness, fast response speed and small position error. INDEX TERMS Permanent magnet linear synchronous motor, nonsingular fast terminal sliding mode control, adaptive, RBF neural network.

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“…Thus, the chattering phenomenon is solved. 26,27 As mentioned before, the SOTSMC is mainly studied with the backstepping method, and the research of combining the complementary and global TSMC is less mentioned. The tracking of the desired height has not been studied for the FSHV.…”

Section: Introductionmentioning

confidence: 99%

Adaptive compound second-order terminal sliding mode control for the longitudinal attitude control of the fully submerged hydrofoil vessel

Liu

Niu

Zhang

et al. 2019

Advances in Mechanical Engineering

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Due to the ever-existing environmental disturbance of ocean wave and nonlinear of the system, it is difficult to obtain a satisfying control performance for the longitudinal attitude and desired height tracking of the fully submerged hydrofoil vessel. To solve such problems, an adaptive compound second-order terminal sliding mode controller is proposed. First, a combination of the complementary sliding mode surface and second-order terminal sliding mode control is introduced. Therefore, the closed system is uniformly ultimately bounded, and the steady-state errors converge to a small neighborhood of equilibrium point. Second, the chattering problem in an actual controller is solved by eliminating the sign function contained in the controller after integration without influencing the stability of the closed-loop system. Besides, a revised adaptive radial basis function neural network is introduced to estimate the derivative of the unknown environmental disturbances without the prior information of the disturbance. Finally, the stability of the system is proved by the Lyapunov stability theory. Numerical experimental results demonstrate that the proposed method possesses fast tracking ability and can decrease the stabilization error and the tracking error simultaneously.

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Adaptive nonsingular terminal sliding mode controller for micro/nanopositioning systems driven by linear piezoelectric ceramic motors

Cited by 31 publications

References 41 publications

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

Improvement of Low-Speed Precision Control of a Butterfly-Shaped Linear Ultrasonic Motor

Adaptive Neural Network Nonsingular Fast Terminal Sliding Mode Control for Permanent Magnet Linear Synchronous Motor

Adaptive compound second-order terminal sliding mode control for the longitudinal attitude control of the fully submerged hydrofoil vessel

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