Proceedings ArticleDOI
Adaptive bounding techniques for stable neural control systems
Marios M. Polycarpou,Petros Ioannou +1 more
- Vol. 3, pp 2442-2447
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TLDR
An adaptive bounding design is used to show that the overall neural control system guarantees semi-global uniform ultimate boundedness within a neighborhood of zero tracking error.Abstract:
This paper considers the design of stable adaptive neural controllers for uncertain nonlinear dynamical systems with unknown nonlinearities. The Lyapunov synthesis approach is used to develop state-feedback adaptive control schemes based on a general class of nonlinearly parametrized neural network models. The key assumptions are that the system uncertainty satisfies a "strict-feedback" condition and that the network reconstruction error and higher-order terms (with respect to the parameter estimates) satisfy certain bounding conditions. An adaptive bounding design is used to show that the overall neural control system guarantees semi-global uniform ultimate boundedness within a neighborhood of zero tracking error.read more
Citations
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Neural network-based control design: an LMI approach
S. Limanond,J. Si +1 more
TL;DR: A neural-network-based control design for a discrete-time nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin and the stability of the closed-loop is guaranteed.
Proceedings ArticleDOI
Nonlinear adaptive control using neural networks and multiple models
Lingji Chen,Kumpati S. Narendra +1 more
TL;DR: The principal contribution described here is concerned with combining linear and nonlinear models to improve the performance of essentially nonlinear dynamical systems even while assuring their stability.
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Robust adaptive output feedback control of nonlinear systems without persistence of excitation
B. Aloliwi,Hassan K. Khalil +1 more
TL;DR: This paper proves tracking error convergence without persistence of excitation, and shows that the adaptive controller is robust with respect to sufficiently small bounded disturbance, and adds a robustifying control component to show that the controllers is robust for a wide class of, not-necessarily-small, bounded disturbance.
Proceedings ArticleDOI
Neural network-based control design: an LMI approach
S. Limanond,J. Si +1 more
TL;DR: In this paper, a neural network-based control design for a discrete-time nonlinear system is presented, where the activation functions are of the sigmoid type symmetric to the origin.
Journal ArticleDOI
Learning control of mobile robots using a multiprocessor system
TL;DR: In this paper, a real-time multiprocessor system is proposed for the solution of the tracking problem of mobile robots operating in a real context with environmental disturbances and parameter uncertainties, which utilizes multiple models of the robot for its identification in an adaptive and learning control framework Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the net non-linear approximation capabilities for modeling the kinematic behavior of the vehicle and for reducing unmodeled contributions to tracking errors.
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Proceedings ArticleDOI
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Multilayer neural-net robot controller with guaranteed tracking performance
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