Results 1 
7 of
7
Adaptive inverse control of linear and nonlinear systems using dynamic neural networks
 IEEE Trans. Neural Networks
"... Abstract—In this paper, we see adaptive control as a threepart adaptivefiltering problem. First, the dynamical system we wish to control is modeled using adaptive systemidentification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, we see adaptive control as a threepart adaptivefiltering problem. First, the dynamical system we wish to control is modeled using adaptive systemidentification techniques. Second, the dynamic response of the system is controlled using an adaptive feedforward controller. No direct feedback is used, except that the system output is monitored and used by an adaptive algorithm to adjust the parameters of the controller. Third, disturbance canceling is performed using an additional adaptive filter. The canceler does not affect system dynamics, but feeds back plant disturbance in a way that minimizes output disturbance power. The techniques work to control minimumphase or nonminimumphase, linear or nonlinear, singleinput–singleoutput (SISO) or multipleinput–multipleouput (MIMO), stable or stabilized systems. Constraints may additionally be placed on control effort for a practical implementation. Simulation examples are presented to demonstrate that the proposed methods work very well. Index Terms—Adaptive inverse control, disturbance canceling, disturbance rejection, feedforward control, system identification. I.
Backlash Compensation In Nonlinear Systems Using Dynamic Inversion By Neural Networks
, 1999
"... A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator which yields a stable closedloop system. 1 INTRODUCTION A general class of industrial motion control systems has the structure of a nonlinear dynamical system preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. This includes xypositioning tables [19], robot manipulators [14], overhead crane mechanisms, and more. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the nonanalytic nature of the actuator nonlinearities and the fact that their exact nonlinear functions are unknown, such systems...
Backlash Compensation with Filtered Prediction in Discrete Time Nonlinear Systems by Dynamic Inversion Using Neural Networks
 Journal of Control
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash no ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) or linearinthe parameters (LIP) assumptions are needed. 1 Introduction Many physical components of control systems have nonsmooth nonlinea...
Backlash Compensation in Discrete Time Nonlinear Systems Using Dynamic Inversion by Neural Networks
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure f ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
(Show Context)
A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme becomes adaptive, guaranteeing bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) assumption is needed. 1 Introduction Robotic systems often have nonlinearities in the actuator such as deadzone, backlash, saturation, etc. This includes xypositioning tables, robot manipulators, ove...
Neural Net Backlash Compensation With Hebbian Tuning By Dynamic Inversion
"... Neural network compensation scheme is presented for the class of nonlinear systems with backlash nonlinearity. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. Instead of a derivative, which cannot be implement ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Neural network compensation scheme is presented for the class of nonlinear systems with backlash nonlinearity. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. Instead of a derivative, which cannot be implemented, a filtered derivative is used. Full rigorous stability proofs are given using filtered derivative. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A modified Hebbian algorithm is presented for NN tuning which yields a stable closedloop system. Using this method yields a relatively simple adaptation structure and offers computational advantages over gradient descent based algorithms. 1 Introduction Recently, in seminal work several rigorously derived ad...
Intelligent Compensation of Actuator Nonlinearities
"... Abstract − Inaccuracy of mechanical components and nature of physical laws make actuators nonlinear devices. Actuator nonlinearities can be static like friction, deadzone, saturation, and/or dynamic like backlash and hysteresis. In this paper, we present recent developments in four basic problems in ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract − Inaccuracy of mechanical components and nature of physical laws make actuators nonlinear devices. Actuator nonlinearities can be static like friction, deadzone, saturation, and/or dynamic like backlash and hysteresis. In this paper, we present recent developments in four basic problems in actuator control – friction, deadzone, backlash, and saturation. We apply intelligent control techniques for compensation of those nonlinearities for a class of nonlinear systems. Static and dynamic compensators using neural networks are considered. Different control structures are developed for common actuator nonlinearities. We present tuning algorithms for those intelligent compensation techniques that guarantee small tracking error and bounded internal states. Simulation results show that the proposed compensation schemes are efficient way of improving the tracking performance of nonlinear systems with backlash. I.
Backlash Compensation in Nonlinear Systems by Dynamic Inversion Using Neural Networks: Continuous and Discrete Time Approaches
, 2000
"... Two different dynamic inversion compensation schemes for control of nonlinear system with input backlash are presented; one in continuous time and one in discrete time. Both schemes use the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward pa ..."
Abstract
 Add to MetaCart
(Show Context)
Two different dynamic inversion compensation schemes for control of nonlinear system with input backlash are presented; one in continuous time and one in discrete time. Both schemes use the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible dynamical system. Tuning algorithms are given for the NN weights so that the backlash compensation schemes guarantee bounded tracking and backlash errors, and also bounded parameter estimates. This paper presents a mathematical approach based on rigorous proofs that guarantees both stability and performance. Simulation examples are given to verify closedloop performance. 1 INTRODUCTION Many physical components of control systems have the nonsmooth nonlinear characteristic known as backlash. Backlash is the difference between toothspace and tooth width in mechanical system and ...