Proceedings ArticleDOI
Stability analysis of neurocontrol systems using a describing function
A. Delgado
- Vol. 3, pp 2126-2130
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TLDR
The describing function of the neural network is used to determine the bounds for the network weights in order to predict the limit cycles and stable system response.Abstract:
This paper presents the stability analysis of closed loop systems with a linear plant and a neural network as controller (neurocontroller). The describing function of the neural network is used to determine the bounds for the network weights in order to predict the limit cycles and stable system response.read more
Citations
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Stabilization of thermal neurocontrollers
TL;DR: A modified backpropagation training method is developed here to simultaneously minimize the target error and increase the dynamic stability of the system.
Book ChapterDOI
Robust stability analysis of a fuzzy vehicle lateral control system using describing function method
TL;DR: The fuzzy controller can be linearized by utilizing the describing function method with experiments and the stability analysis of the vehicle lateral control system with the variations of velocity and friction is carried out by the use of parameter plane method.
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Describing function analysis of uncertain fuzzy vehicle control systems
TL;DR: This work shows that the limit cycles caused by a static fuzzy controller can be easily suppressed if the system parameters are chosen carefully.
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Limit cycle prediction of a neurocontrol vehicle system based on gain-phase margin analysis
TL;DR: A systematic procedure to address the limit cycle prediction of a neural vehicle control system with adjustable parameters using the classical method of parameter plane and some useful frequency domain methods is proposed.
Proceedings Article
Parameter plane analysis of fuzzy vehicle steering control systems
TL;DR: The main purpose of this paper is to analyze the robust stability for a fuzzy vehicle steering control system and a systematic procedure is proposed to solve this problem.
References
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Journal ArticleDOI
A systematic classification of neural-network-based control
TL;DR: This work breaks the available schemes down to their essential functional features and organizes the latter into a multi-level classification, revealing that similar schemes often get placed in different categories, fundamentally different features often get lumped into a single category and proposed new schemes are often merely permutations and combinations of the well-established fundamental features.