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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 ..."
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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...
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...
Performance Analysis of Adaptive Neural Network Frequency Controller for Thermal Power Systems
 Proceedings of the 9th WSEAS International Conference on Automatic Control, Modeling & Simulation
"... Abstract: An adaptive neural network control scheme for thermal power system is described. No offline training is required for the proposed neural network controller. The online tuning algorithm and neural network architecture are described. The performance of the controller is illustrated via sim ..."
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Cited by 1 (0 self)
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Abstract: An adaptive neural network control scheme for thermal power system is described. No offline training is required for the proposed neural network controller. The online tuning algorithm and neural network architecture are described. The performance of the controller is illustrated via simulation for different changes in process parameters. Performance of neural network controller is compared with conventional proportionalintegral control scheme for frequency control in thermal power systems. KeyWords:power system, neural network, adaptive control, frequency control 1
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...