ROBUST NEURO-FUZZY SLIDING MODE CONTROLLER FOR A FLEXIBLE ROBOT MANIPULATOR

2015 ◽  
Vol 30 (1) ◽  
Author(s):  
Khedoudja Kherraz ◽  
Mustapha Hamerlain ◽  
Nouara Achour
Keyword(s):  
Sliding Mode ◽  
Robot Manipulator ◽  
Sliding Mode Controller ◽  
Flexible Robot ◽  
Neuro Fuzzy ◽  
Fuzzy Sliding Mode

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
Keyword(s):  
Sliding Mode Control ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rule Base ◽  
Sliding Mode Controller ◽  
Fuzzy Sliding Mode Control ◽  
Ts Fuzzy Model ◽  
Mode Control ◽  
Fuzzy Sliding Mode

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.

Adaptive Neuro-Fuzzy Sliding Mode Controller

2018 ◽  
Vol 7 (2) ◽  
pp. 34-54 ◽  
Author(s):  
Sana Bouzaida ◽  
Anis Sakly
Keyword(s):  
Sliding Mode ◽  
Benchmark Problems ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Adaptive Sliding Mode Controller ◽  
Neuro Fuzzy ◽  
Fuzzy Sliding Mode ◽  
Fuzzy Network ◽  
Online Tuning

A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.

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