Adaptive Sliding Mode Control Method for Z-Axis Vibrating Gyroscope Using Prescribed Performance Approach

Lu, Cheng; Hua, Liu; Zhang, Xinsong; Wang, Huiming; Guo, Yunxiang

doi:10.3390/app10144779

Cited by 6 publications

(3 citation statements)

References 29 publications

(51 reference statements)

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“…Thus, the extension to attack the same problem without the Lipschitz hypothesis seems to be nontrivial and needs to be replaced by other assumptions that allow to apply the proposed decomposition. In this respect, the adoption of metrics different from the Euclidean one [38][39][40] as well as of non-quadratic Lyapunov functions [41,42] may be the target of future investigations.…”

Section: Discussionmentioning

confidence: 99%

State Observation for Lipschitz Nonlinear Dynamical Systems Based on Lyapunov Functions and Functionals

2020

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State observers for systems having Lipschitz nonlinearities are considered for what concerns the stability of the estimation error by means of a decomposition of the dynamics of the error into the cascade of two systems. First, conditions are established in order to guarantee the asymptotic stability of the estimation error in a noise-free setting. Second, under the effect of system and measurement disturbances regarded as unknown inputs affecting the dynamics of the error, the proposed observers provide an estimation error that is input-to-state stable with respect to these disturbances. Lyapunov functions and functionals are adopted to prove such results. Third, simulations are shown to confirm the theoretical achievements and the effectiveness of the stability conditions we have established.

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Section: Discussionmentioning

confidence: 99%

State Observation for Lipschitz Nonlinear Dynamical Systems Based on Lyapunov Functions and Functionals

2020

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“…To address this problem, in [23,24], system uncertainties are estimated by NNRBF through the SMC approach. Regarding the prominent features of SMC, the combination of this method with PPC has been recently developed for different systems [25][26][27] which drives the control system towards a robust performance with desired transient and the steady-state responses. In addition, fast system response is highly demanding in various robotics applications.…”

Section: Introductionmentioning

confidence: 99%

Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method

et al. 2022

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This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications.

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“…The first kind of controller method is based on the Riccati difference equation [5], adaptive control [6][7][8][9][10][11], fuzzy and linear matrix inequality (LMI)-based control method [12,13] to stabilize the matched/unmatched uncertain systems. However, the theoretical assumption is not practical, because the unmatched terms are arbitrary signals, so it may not present a zero steady state value.…”

Section: Introductionmentioning

confidence: 99%

Perturbation Observer-Based Robust Control Using a Multiple Sliding Surfaces for Nonlinear Systems with Influences of Matched and Unmatched Uncertainties

Thanh¹,

Xuan-Mung²,

Nguyen³

et al. 2020

Mathematics

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This paper presents a lumped perturbation observer-based robust control method using an extended multiple sliding surface for a system with matched and unmatched uncertainties. The fundamental methodology is to apply the multiple surfaces to approximate the unknown lumped perturbations simultaneously influencing on a nonlinear single input–single output (SISO) system. Subsequently, a robust controller, based on the proposed multi-surface and the approximated values, is designed to highly improve the control performance of the system. A general stability of the lumped perturbation observer and closed-loop control system is obtained through the Lyapunov theory. Results of a numerical simulation of an illustrative example demonstrate the soundness of the proposed algorithm.

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Adaptive Sliding Mode Control Method for Z-Axis Vibrating Gyroscope Using Prescribed Performance Approach

Cited by 6 publications

References 29 publications

State Observation for Lipschitz Nonlinear Dynamical Systems Based on Lyapunov Functions and Functionals

State Observation for Lipschitz Nonlinear Dynamical Systems Based on Lyapunov Functions and Functionals

Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method

Perturbation Observer-Based Robust Control Using a Multiple Sliding Surfaces for Nonlinear Systems with Influences of Matched and Unmatched Uncertainties

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