Sliding Mode Control of a 2D Torsional MEMS Micromirror with Sidewall Electrodes

Author(s):  
Hui Chen ◽  
Manu Pallapa ◽  
Weijie Sun ◽  
Zhendong Sun ◽  
John T. W. Yeow

This paper presents a sliding mode control scheme to improve the positioning performance of a 2-Degree-of-freedom (DOF) torsional MEMS micromirror with sidewall electrodes. The stability of closed-loop system is proved by Lyapunov stability theorem under the existence of bounded parameter uncertainties and external disturbances. Furthermore, the performance of the closed-loop system is illustrated by experimental and simulation results which verify that the feasibility and effectiveness of the proposed scheme. The results demonstrated that the torsional MEMS micromirror with the proposed sliding mode controller has a good transient response and tracking performance.

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Asier Ibeas ◽  
Manuel de la Sen ◽  
Santiago Alonso-Quesada

This paper is aimed at designing a robust vaccination strategy capable of eradicating an infectious disease from a population regardless of the potential uncertainty in the parameters defining the disease. For this purpose, a control theoretic approach based on a sliding-mode control law is used. Initially, the controller is designed assuming certain knowledge of an upper-bound of the uncertainty signal. Afterwards, this condition is removed while an adaptive sliding control system is designed. The closed-loop properties are proved mathematically in the nonadaptive and adaptive cases. Furthermore, the usual sign function appearing in the sliding-mode control is substituted by the saturation function in order to prevent chattering. In addition, the properties achieved by the closed-loop system under this variation are also stated and proved analytically. The closed-loop system is able to attain the control objective regardless of the parametric uncertainties of the model and the lack ofa prioriknowledge on the system.


2013 ◽  
Vol 427-429 ◽  
pp. 1101-1104
Author(s):  
Yan Qiu Che ◽  
Ting Ting Yang ◽  
Xiao Qin Li ◽  
Rui Xue Li

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.


2020 ◽  
pp. 107754632093202
Author(s):  
Hamid Reza Shafei ◽  
Mohsen Bahrami ◽  
Heidar Ali Talebi

This study uses a comprehensive control approach to deal with the trajectory tracking problem of a two-flexible-link manipulator subjected to model uncertainties. Because the control inputs of two-flexible-link manipulators are less than their state variables, the proposed controller should be able to tackle the stated challenge. Practically speaking, there is only a single control signal for each joint, which can be used to suppress link deflections and control joint trajectories. To achieve this objective, a novel optimal robust control scheme, with an updated gain under the adaptive law, has been developed in this work for the first time. In this regard, a nonsingular terminal sliding mode control approach is used as the robust controller and a control Lyapunov function is used as the optimal control law, to benefit from the advantages of both methods. To systematically deal with system uncertainties, an adaptive law is used to update the gain of nonsingular terminal sliding mode control. The advantage of this approach over the existing methods is that it not only can robustly and stably control an uncertain nonlinear system against external disturbances but also can optimally solve a quadratic cost function (e.g. minimization of control effort). The Lyapunov stability theory has been applied to verify the stability of the proposed approach. Moreover, to show the superiority of this method, the computer simulation results of the proposed method have been compared with those of an adaptive sliding mode control scheme. This comparison shows that the presented approach is capable of optimizing the control inputs while achieving the stability of the examined two-flexible-link manipulator in the presence of model uncertainties and external disturbances.


Author(s):  
Huaizhen Wang ◽  
Lijin Fang ◽  
Junyi Wang ◽  
Tangzhong Song ◽  
Hesong Shen

Robust and precise control of robot systems are still challenging problems due to the existence of uncertainties and backlash hysteresis. To deal with the problems, an adaptive neural sliding mode control with prescribed performance is proposed for robotic manipulators. A finite-time nonsingular terminal sliding mode control combined with a new prescribed performance function (PPF) is developed to guarantee the transient and steady-state performance of the closed-loop system. Based on the sliding mode variable, an adaptive law is presented to effectively estimate the bound of system uncertainties where the prior knowledge of uncertainties is not needed. To approximate nonlinear function and unknown dynamics, the Gaussian radial basis function neural networks(RBFNNs) is introduced to compensate the lumped nonlinearities. All signals of the closed-loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. Finally, comparative simulations are conducted to illustrate superiority and reliability of the proposed control strategy.


2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Liang Ding ◽  
Haibo Gao ◽  
Kerui Xia ◽  
Zhen Liu ◽  
Jianguo Tao ◽  
...  

The hybrid joints of manipulators can be switched to either active (actuated) or passive (underactuated) mode as needed. Consider the property of hybrid joints, the system switches stochastically between active and passive systems, and the dynamics of the jump system cannot stay on each trajectory errors region of subsystems forever; therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this paper, we consider stochastic stability and sliding mode control for mobile manipulators using stochastic jumps switching joints. Adaptive parameter techniques are adopted to cope with the effect of Markovian switching and nonlinear dynamics uncertainty and follow the desired trajectory for wheeled mobile manipulators. The resulting closed-loop system is bounded in probability and the effect due to the external disturbance on the tracking errors can be attenuated to any preassigned level. It has been shown that the adaptive control problem for the Markovian jump nonlinear systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. Finally, a numerical example is given to show the potential of the proposed techniques.


Author(s):  
Parham Ghorbanian ◽  
Sergey G. Nersesov ◽  
Hashem Ashrafiuon

In this paper, a general framework that provides sufficient conditions for asymptotic stabilization of underactuated nonlinear systems using an optimal sliding mode control in the presence of system uncertainties is presented. A performance objective is used to optimally select the parameters of the sliding mode control surfaces subject to state and input constraints. It is shown that the closed-loop system trajectories reach the optimal sliding surfaces in finite time and a constructive methodology to determine exponential stability of the closed-loop system on the sliding surfaces is developed which ensures asymptotic stability of the overall closed-loop system. The framework further provides the basis to determine an estimate of the domain of attraction for the closed-loop system with uncertainties. The results developed in this work are experimentally validated using a linear inverted pendulum testbed which show a good match between the actual domain of attraction of the upward equilibrium state and its analytical estimate.


Author(s):  
Huaizhen Wang ◽  
Lijin Fang ◽  
Menghui Hu ◽  
Tangzhong Song ◽  
Jiqian Xu

In this paper, a novel adaptive funnel fast nonsingular terminal sliding mode control for robotic manipulators with dynamic uncertainties is proposed. A modified funnel variable is utilized to transform the tracking error fall within funnel boundary, which improves the transient and steady-state tracking performance of robotic manipulators. Based on the transformed error, a novel funnel fast nonsingular terminal sliding mode surface is developed and a sliding mode control law is designed to stabilize the closed-loop system and achieve high tracking precision. An adaptive update law combined with the sliding mode surface is designed to deal with uncertainties and external disturbances where their upper bounds are unknown in practical cases. The stability and finite time convergence of the closed-loop system are proved by Lyapunov stability theorem. Simulation results and discussions are presented to demonstrate the effectiveness and high-precision tracking control for robotic manipulators.


2020 ◽  
Vol 31 (1) ◽  
pp. 68-76

We constitute a control system for overhead crane with simultaneous motion of trolley and payload hoist to destinations and suppression of payload swing. Controller core made by sliding mode control (SMC) assures the robustness. This control structure is inflexible since using fixed gains. For overcoming this weakness, we integrate variable fractional-order derivative into SMC that leads to an adaptive system with adjustable parameters. We use Mittag–Leffler stability, an enhanced version of Lyapunov theory, to analyze the convergence of closed-loop system. Applying the controller to a practical crane shows the efficiency of proposed control approach. The controller works well and keeps the output responses consistent despite the large variation of crane parameters.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ruimin Zhang ◽  
Qiaoyu Chen ◽  
Haigang Guo

This paper presents an adaptive nonsingular terminal sliding mode control approach for the attitude control of a hypersonic vehicle with parameter uncertainties and external disturbances based on Chebyshev neural networks (CNNs). First, a new nonsingular terminal sliding surface is proposed for a general uncertain nonlinear system. Then, a nonsingular sliding mode control is designed to achieve finite-time tracking control. Furthermore, to relax the requirement for the upper bound of the lumped uncertainty including parameter uncertainties and external disturbances, a CNN is used to estimate the lumped uncertainty. The network weights are updated by the adaptive law derived from the Lyapunov theorem. Meanwhile, a low-pass filter-based modification is added into the adaptive law to achieve fast and low-frequency adaptation when using high-gain learning rates. Finally, the proposed approach is applied to the attitude control of the hypersonic vehicle and simulation results illustrate its effectiveness.


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