N. Sukavanam

Approximate Controllability of Fractional Order Semilinear Delay Systems

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.

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

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.

Neural network-based nonlinear tracking control of kinematically redundant robot manipulators

In this paper, neural network-based nonlinear dynamical control of kinematically redundant robot manipulators is considered. The neural network-based controller achieves end-effector trajectory tracking as well as subtask tracking effectively. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. The whole system is shown to be stable in the sense of Lyapunov. Numerical simulation studies are carried out for a 3R planar robot manipulator to show the effectiveness of the control scheme.

Backstepping based Trajectory Tracking Control of a Four Wheeled Mobile Robot

For a four wheeled mobile robot a trajectory tracking concept is developed based on its kinematics. A trajectory is a time–indexed path in the plane consisting of position and orientation. The mobile robot is modeled as a non holonomic system subject to pure rolling, no slip constraints. To facilitate the controller design the kinematic equation can be converted into chained form using some change of co-ordinates. From the kinematic model of the robot a backstepping based tracking controller is derived. Simulation results demonstrate such trajectory tracking strategy for the kinematics indeed gives rise to an effective methodology to follow the desired trajectory asymptotically.

Approximate controllability of second order semilinear stochastic system with nonlocal conditions

In this paper, the approximate controllability of second order semilinear stochastic system involving nonlocal conditions is studied. By using Sadovskiis Fixed Point theorem with stochastic analysis theory, we derive a new set of sufficient conditions for the approximate controllability of second order semilinear stochastic system with nonlocal conditions under the assumption that the corresponding linear system is approximately controllable. Finally, an application to a second order semilinear stochastic system with nonlocal initial condition is provided to illustrate the obtained theory.

Trajectory control of a two DOF rigid-flexible space robot by a virtual space vehicle

Model based control schemes use inverse dynamics of the robot arm to produce the main torque component necessary for trajectory tracking. For a model-based controller one is required to know the model parameters accurately. This is a very difficult job especially if the manipulator is flexible. This paper presents a control scheme for trajectory control of the tip of a two arm rigid-flexible space robot, with the help of a virtual space vehicle. The flexible link is modeled as an Euler-Bernoulli beam. The developed controller uses the inertial parameters of the base of the space robot only. Bond graph modeling is used to model the dynamics of the system and to devise the control strategy. The efficacy of the controller is shown through simulated and animation results.

Simulation and stability analysis of neural network based control scheme for switched linear systems

This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented.

Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

The aim of this paper is to design a robust adaptive neural network-based hybrid position/force control scheme for robot manipulators in the presence of model uncertainties and external disturbance. The feedforward neural network employed to learn a highly nonlinear function requires no preliminary learning. The control purposes are to achieve the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment and to regulate robot tip position in cartesian space. An adaptive compensator is also developed to eliminate the effect of disturbance term of neural network approximation error and external disturbance or unmodeled dynamics etc. A key feature of this compensator is that the prior information of the disturbance bound is not required. Finally, a comparative simulation study with a model-based robust control scheme for a two-link robot manipulator is presented.

Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator

In this paper an ANFIS-PD+I (AFSPD+I) based hybrid force/position controller has been proposed which works effectively with unspecified robot dynamics in the presence of external disturbances. A constraint is put to limit the movement of manipulator in XY Cartesian coordinates. The validity of the proposed controller has been tested using a 6-degree of freedom PUMA robot manipulator. The performance comparison have been done with the fuzzy proportional derivative plus integral, fuzzy proportional integral derivative and conventional proportional integral derivative controllers subjected to the same data set with proposed controller. The projected AFSPD+I controller adhered to the desired path closer and smoother than the other mentioned controllers.

Neural network based control scheme for redundant robot manipulators subject to multiple self-motion criteria

Abstract The aim of this paper is to design a neural network based adaptive control scheme for redundant manipulators in the presence of model uncertainties and external disturbances together with multiple self-motion criteria. With reference to the paper (N. Kumar, V. Panwar, N. Sukavanam, S.P. Sharma, J.H. Borm, Neural network based nonlinear tracking control of kinematically redundant robot manipulators, Mathematical and Computer Modelling 53 (2011) 1889–1901), an adaptive mechanism is also developed to estimate the uncertain bound of external disturbances and neural network approximation error etc. without requirement of known bounds. By Lyapunov method asymptotic error convergence can be guaranteed for both task-space and sub-task tracking errors. A comparative simulation study with a robust controller and the proposed controller are included for a 3 link planar robot manipulator.