Faculty Publications
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Publications by NITK Faculty
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Item GA based adaptive controller for 2DOF robot manipulator(IFAC Secretariat, 2014) Vijay, M.; Jena, D.In this paper different types of robotic manipulator controllers are developed to acquire dynamic properties and improve the global stability. Here two control schemes for two degrees of freedom (2DOF) robot manipulator are discussed. The controllers are developed using Fminsearch and Genetic Algorithm (GA). The main objectives of these controllers are to provide stability, good disturbance rejection and small tracking error. The Proportional Derivative (PD) and Proportional Integral Derivative (PID) conventional controllers are developed for three different control strategies (IATE, ISE and ISTE) using Fminsearch and GA. The performances of these controllers are compared with various torque disturbances in terms of path tracking and disturbance rejection. © 2014 IFAC.Item Optimal GA based SMC with adaptive PID sliding surface for robot manipulator(Institute of Electrical and Electronics Engineers Inc., 2015) Vijay, M.; Jena, D.Different types of robotic manipulator controllers are developed to acquire dynamic properties and improve the global stability. In this paper a control strategy for robotic manipulator based on the coupling of the Artificial Neuro Fuzzy Inference System (ANFIS) with sliding mode control (SMC) approach has been presented. Initially, the Proportional Integral Derivative (PID) controller has developed for three different control strategies (IATE, ISE and ISTE) using GA. SMC 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. Finally, we have trained an ANFIS network, which can generate the adaptive PID control signal to the SMC of robot manipulator. 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 ANFIS adaptive SMC controller can achieve favorable tracking performance and it is robust with regard to disturbances in input torque. © 2014 IEEE.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 Backstepping terminal sliding mode control of robot manipulator using radial basis functional neural networks(Elsevier Ltd, 2018) Vijay, M.; Jena, D.This paper examines an observer-based backstepping terminal sliding mode controller (BTSMC) for 3 degrees of freedom overhead transmission line de-icing robot manipulator (OTDIRM). The control law for tracking of the OTDIRM is formulated by the combination of BTSMC and neural network (NN) based approximation. For the precise trajectory tracking performance and enhanced disturbance rejection, NN-based adaptive observer backstepping terminal sliding mode control (NNAOBTSMC) is developed. To obviate local minima problem, the weights of both NN observer and NN approximator are adjusted off-line using particle swarm optimization. The radial basis function neural network-based observer is used to estimate tracking position and velocity vectors of the OTDIRM. The stability of the proposed control methods is verified with the Lyapunov stability theorem. Finally, the robustness of the proposed NNAOBTSMC is checked against input disturbances and uncertainties. © 2017 Elsevier Ltd