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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)
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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 ..."
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Cited by 4 (1 self)
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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...
Design and Implementation of Industrial Neural Network Controller Using Backstepping
, 2001
"... In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closedloop system. The NN backstepping co ..."
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Cited by 4 (0 self)
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In this paper a novel neural network (NN) backstepping controller is modified for application to an industrial motor drive system. A control system structure and NN tuning algorithms are presented that are shown to guarantee stability and performance of the closedloop system. The NN backstepping controller is implemented on an actual motor drive system using a twoPC control system developed at UTA. The implementation results show that the NN backstepping controller is highly effective in controlling the industrial motor drive system. It is also shown that the NN controller gives better results on actual systems than a standard backstepping controller developed assuming full knowledge of the dynamics. Moreover, the NN controller does not require the linearintheparameters assumption or the computation of regression matrices required by standard backstepping.
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)
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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 ..."
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Cited by 2 (0 self)
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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...
On Adaptive Critic Architectures In Feedback Control
, 1999
"... Two feedback control systems are designed that employ the adaptive critic architecture, which consists of two neural networks, one of which (the critic) tunes the other. The first application is a deadzone compensator, where it is shown that the adaptive critic structure is a natural consequence of ..."
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Two feedback control systems are designed that employ the adaptive critic architecture, which consists of two neural networks, one of which (the critic) tunes the other. The first application is a deadzone compensator, where it is shown that the adaptive critic structure is a natural consequence of the mathematical problem of inversion of an unknown function. In this situation the adaptive critic appears in the feedforward loop. The second application is the supervisory loop adaptive critic, where it is shown that the critic neural network requires additional dynamics that effectively give it a memory capability. 1 INTRODUCTION The uses of neural networks (NN) in openloop applications such as signal processing or system identification are significantly different than their applications in closedloop feedback control applications. In the latter situation, it is necessary to take into account the interaction between the dynamics of the controlled system and that of the NN weight tuning...