Adaptive neural sliding mode control with prescribed performance of robotic manipulators subject to backlash hysteresis

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.

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.


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.


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.


2016 ◽  
Vol 39 (8) ◽  
pp. 1195-1204 ◽  
Author(s):  
Huiming Wang ◽  
Shihua Li ◽  
Qixun Lan ◽  
Zhenhua Zhao ◽  
Xingpeng Zhou

In this paper, we discuss the speed regulation problem of permanent magnet synchronous motor (PMSM) servo systems. Firstly, a continuous terminal sliding mode control (CTSMC) method is introduced for speed loops to eliminate the chattering phenomenon while still ensuring a strong disturbance rejection ability for the closed-loop system. However, in the presence of strong disturbances, the CTSMC law still needs to select high gain which may result in large steady-state speed fluctuations for the PMSM control system. To this end, an extended state observer (ESO)-based continuous terminal sliding mode control method is proposed. The ESO is employed to estimate system disturbances and the estimation is employed by the speed controller as a feed-forward compensation for disturbances. Compared to the conventional sliding mode control method, the proposed composite sliding control method obtains a faster convergence and better tracking performance. Also, by feed-forward compensating system disturbances and tuning down the gain of the CTSMC law, the fluctuation of steady-state speed of the closed-loop system is reduced while the disturbance rejection capability of the PMSM system is still maintained. Simulation and experimental results are provided to demonstrate the superior properties of the proposed control method.


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.


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