Nagarajan Sukavanam

Bio: Nagarajan Sukavanam is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Controllability & Nonlinear system. The author has an hindex of 19, co-authored 118 publications receiving 1311 citations.

Neural network based hybrid force/position control for robot manipulators

Abstract: This paper presents a neural network based adaptive control scheme for hybrid force/position control for rigid robot manipulators. Firstly the robot dynamics is decomposed into force, position and redundant joint subspaces. Based on this decomposition, a neural network based controller is proposed that achieves the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment as well as regulate robot tip position in cartesian space. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. Finally numerical simulation studies are carried out for a two link rigid robot manipulator.

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

Abstract: In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.

Approximate Controllability of Fractional Order Semilinear Delay Systems

Abstract: In this paper, the approximate controllability for a class of semilinear delay control systems of fractional order is proved under the natural assumption that the linear system is approximately controllable. The existence and uniqueness of the mild solution is also proved under suitable assumptions. An example is given to illustrate our main results.

Regularization Of Inverse Problems

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