Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties

Robotica ◽  
2013 ◽  
Vol 32 (3) ◽  
pp. 433-446 ◽  
Author(s):  
Mohammad Reza Soltanpour ◽  
Mohammad Hassan Khooban ◽  
Mahmoodreza Soltani

SUMMARYThis paper proposes a simple fuzzy sliding mode control to achieve the best trajectory tracking for the robot manipulator. In the core of the proposed method, by applying the feedback linearization technique, the known dynamics of the robot's manipulator is removed; then, in order to overcome the remaining uncertainties, a classic sliding mode control is designed. Afterward, by applying the TS fuzzy model, the classic sliding mode controller is converted to fuzzy sliding mode controller with very simple rule base. The mathematical analysis shows that the robot manipulator with the new proposed control in tracking the robot manipulator in presence of uncertainties has the globally asymptotic stability. Finally, to show the performance of the proposed method, the controller is simulated on a robot manipulator with two degrees of freedom as case study of the research. Simulation results demonstrate the superiority of the proposed control scheme in presence of the structured and unstructured uncertainties.

2008 ◽  
Vol 13 (1) ◽  
pp. 124-128 ◽  
Author(s):  
Ha Quang Thinh Ngo ◽  
Jin-Ho Shin ◽  
Won-Ho Kim

2000 ◽  
Author(s):  
J. Choi ◽  
C. W. de Silva ◽  
V. J. Modi ◽  
A. K. Misra

Abstract This paper focuses a robust and knowledge-based control approach for multi-link robot manipulator systems. Based on the concepts of sliding-mode control and fuzzy logic control (FLC), a fuzzy sliding-mode controller has been developed in previous work. This controller possesses good robustness properties of sliding-mode control and the flexibility and ‘intelligent’ capabilities of knowledge-based control through the use of fuzzy logic. This paper presents experimental studies with fuzzy sliding-mode control as well as conventional sliding-mode control. The results show that the tracking error is guaranteed to converge to a specification in the presence of uncertainties. The performance of the fuzzy sliding-mode controller is found to be somewhat better than that of the conventional sliding-mode controller.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ji-Hwan Hwang ◽  
Young-Chang Kang ◽  
Jong-Wook Park ◽  
Dong W. Kim

In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC) for robot manipulator is proposed. The proposed AIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization (FL) control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law and interval type-2 fuzzy system, sliding mode controller is designed, respectively. The tuning algorithms are derived in the sense of Lyapunov stability theorem. Two-link rigid robot manipulator with nonlinearity is used to test and the simulation results are presented to show the effectiveness of the proposed method that can control unknown system well.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ibrahim M. Mehedi ◽  
Heidir S. M. Shah ◽  
Ubaid M. Al-Saggaf ◽  
Rachid Mansouri ◽  
Maamar Bettayeb

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient’s lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.


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