Faculty Publications
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Item Optimal backstepping sliding mode control for robot manipulator(Institute of Electrical and Electronics Engineers Inc., 2015) Vijay, M.; Jena, D.In this paper a control strategy for robotic manipulator based on the coupling of the Backstepping sliding mode control (BSMC) and equivalent control approach has been presented. Initially, the Proportional Integral Derivative (PID) controller has developed for three different control strategies (IATE, ISE and ISTE) using GA. BSMC has developed for best optimal criterion by using GA. The main objectives of these controller are to provide stability, good disturbance rejection and small tracking error. The stability of the system is guaranteed by the checking of the Lyapunov stability theorem. Numerical simulations using the dynamic model of 2 DOF planner rigid robot manipulator with input torque disturbance shows the effectiveness in trajectory tracking problem and disturbance rejection. The simulation results of these controllers are compared with various torque disturbances in terms of path tracking and disturbance rejection. The proposed Backstepping SMC controller can achieve favourable tracking performance and it is robust with regard to disturbances in input torque and uncertainties in parameters. © 2015 IEEE.Item PSO based backstepping sliding mode controller and observer for robot manipulators(Institute of Electrical and Electronics Engineers Inc., 2018) Vijay, M.; Jena, D.This paper examines the structure of an observer-based modified backstepping boundary sliding mode controller (BBSMC) for 2 degrees of freedom robot manipulator. A radial basis functional NN (RBFNN) is combined with BBSMC and adjustment law is derived for the effective control of a robot manipulator. The RBFNN-based observer is used to estimate the tracking position and velocity vector, at the same time RBFNN-based identifier provides an auxiliary torque to the robot manipulator. For both the observer and identifier, a PSO based off-line tuning method is adapted to tune the weights of RBFNN network. The stability of developed controller is checked by utilizing Lyapunov stability function. Finally, the performance of the proposed NNAOBBSMC is validated experimentally. © 2018 IEEE.