Network Control Of Robot Manipulators Research Articles

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

AbstractThis paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

Robust neural network control of robotic manipulators via switching strategy

In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical implementation is constructed. The proposed control scheme can guarantee that the resulting closed-loop switched system is asymptotically Lyapunov stable and the tracking error performance of the control system is well reached. Finally, a simulation example of two-link robotic manipulators is shown to illustrate the eectiveness of the proposed control method.

CMAC neural network control of robot manipulators

A comprehensive treatment of the cerebellar model articulation controller (CMAC) neural network (NN) for the control of robot manipulators is presented. The structure and localized learning properties of CMAC NN is exploited to design efficient controllers for nonlinear systems belonging to a given useful class. Continuous- and discrete-time implementation of these controllers is systematically examined. Novel weight update schemes are derived and the closed-loop stability of the controller and the system is rigorously proved. These weight update schemes are shown to be nonstandard modifications of adaptive techniques prevalent in the literature. Finally, the validity of these techniques is demonstrated through numerical studies. ©1997 by John Wiley & Sons, Inc.

Adaptive neural network control of robot manipulators in task space

In this paper, the adaptive neural network control of robot manipulators in the task space is considered. The controller is developed based on a neural network modeling technique which neither requires the evaluation of inverse dynamical model nor the time-consuming training process. It is shown that, if Gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotically tracking is achieved. The controller thus obtained does not require the inverse of the Jacobian matrix. In addition, robust control can be easily incorporated to suppress the neural network modeling errors and the bounded disturbances. Numerical simulations are provided to show the effectiveness of the approach.

Neural network control of robot manipulators

The author describes neural network controllers for robot manipulators in a variety of applications, including position control, force control, parallel-link mechanisms, and digital neural network control. These model-free controllers offer a powerful and robust alternative to adaptive control.

A study of neural network control of robot manipulators

SummaryThe basic robot control technique is the model based computer-torque control which is known to suffer performance degradation due to model uncertainties. Adding a neural network (NN) controller in the control system is one effective way to compensate for the ill effects of these uncertainties. In this paper a systematic study of NN controller for a robot manipulator under a unified computed-torque control framework is presented. Both feedforward and feedback NN control schemes are studied and compared using a common back-propagation training algorithm. Effects on system performance for different choices of NN input types, hidden neurons, weight update rates, and initial weight values are also investigated. Extensive simulation studies for trajectory tracking are carried out and compared with other established robot control schemes.