scholarly journals Composite Learning Sliding Mode Control of Flexible-Link Manipulator

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Bin Xu ◽  
Pengchao Zhang

This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.

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.


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.


2017 ◽  
Vol 31 (14) ◽  
pp. 1750160 ◽  
Author(s):  
Shuai Song ◽  
Xiaona Song ◽  
Ines Tejado Balsera

This paper investigates the mixed [Formula: see text] and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks with time delays. Our aim is to design a controller such that, though the unavoidable phenomena of time delay and external disturbances is fully considered, the resulting closed-loop system is stable with a mixed [Formula: see text] and passive performance level. By combining sliding mode control and adaptive control methods, a novel adaptive sliding mode control strategy is designed for the synchronization of time-delayed FO dynamic networks. Via the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequalities. Based on the conditions, a desired controller which can guarantee the stability of the closed-loop system and also ensure a mixed [Formula: see text] and passive performance level is designed. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.


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.


2015 ◽  
Vol 740 ◽  
pp. 278-282
Author(s):  
Zhao Lan He ◽  
Zong Ze Liu ◽  
Xian Xian Tang

This Paper deals with the sliding mode control of a class of uncertain Markov switched systems. By using linear transformation, the system is transformed into standard type. A sufficient condition of the existence of a sliding mode dynamics is derived, and an explicit parameterization of desired sliding surface is also given. A sliding mode controller is then designed to guarantee exponential stability of the overall switched closed-loop system. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approaches.


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.


2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Runze Ding ◽  
Lingfei Xiao

For a class of nonlinear harmonic gear drive systems with mismatched uncertainties, a novel robust control method is presented on the basis of quadratic integral sliding mode surface, and the closed-loop system has satisfying performance and strong robustness against mismatched uncertainties and nonlinear disturbances. Considering time-varying nonlinear torques and parameters variations which are caused by nonlinear frictions and backlash, a nonlinear harmonic gear drive system mathematic model is established and the effect of nonlinear parts is compensated during control system design. It is proven that the quadratic integral sliding mode surface can be reached in finite time and the closed-loop system is asymptotic stable robustly. The simulation studies are carried out in comparison with traditional linear sliding mode control and integral sliding mode control, verifying the effectiveness of the proposed method.


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