Journal ArticleDOI
An adaptive sliding-mode controller for discrete nonlinear systems
D. Munoz,Daniel Sbarbaro +1 more
Reads0
Chats0
TLDR
A sliding-mode controller for a class of nonlinear discrete-time systems using a modified switching function that produces a low-chattering control signal and an adaptive term is added to the original sliding- mode algorithm.Abstract:
This paper presents a sliding-mode controller for a class of nonlinear discrete-time systems. The proposed controller uses a modified switching function that produces a low-chattering control signal. In order to improve the controller performance, an adaptive term is added to the original sliding-mode algorithm. This new feature uses an artificial neural network for online identification of the modeling error. Simulations and experimental results illustrate the main characteristics and performance of this approach,.read more
Citations
More filters
Journal ArticleDOI
New methodologies for adaptive sliding mode control
TL;DR: The goal is to obtain a robust sliding mode adaptive-gain control law with respect to uncertainties and perturbations without the knowledge of uncertainties/perturbations bound (only the boundness feature is known).
Journal ArticleDOI
Sliding-Mode Control With Soft Computing: A Survey
Xinghuo Yu,Okyay Kaynak +1 more
TL;DR: The state of the art of recent developments in SMC systems with SC is provided, examining key technical research issues and future perspectives.
Journal ArticleDOI
Adaptive sliding mode control with application to super-twist algorithm: Equivalent control method
TL;DR: In this paper an adaptation methodology is developed for searching the minimum possible value of control based on evaluations of the, so-called, equivalent control by a low-pass filter based on direct measurements of the first-order low- pass filter.
Journal ArticleDOI
Adaptive Sliding-Mode Control for NonlinearSystems With Uncertain Parameters
TL;DR: This correspondence proposes a systematic adaptive sliding- mode controller design for the robust control of nonlinear systems with uncertain parameters and proves system robustness, as well as stability, is proven by using the Lyapunov theory.
Journal ArticleDOI
Dynamic operation and control of microgrid hybrid power systems
Ting-Chia Ou,Chih-Ming Hong +1 more
TL;DR: In this article, the authors examined dynamic operation and control strategies for a microgrid hybrid wind-PV (photovoltaic) and fuel cell-based power supply system, which consists of the PV power, wind power, FC power, SVC (static var compensator) and an intelligent power controller.
References
More filters
Journal ArticleDOI
Variable structure control: a survey
John Y. Hung,W. Gao,J.C. Hung +2 more
TL;DR: A tutorial account of variable structure control with sliding mode is presented, introducing in a concise manner the fundamental theory, main results, and practical applications of this powerful control system design approach.
Journal ArticleDOI
Neural networks for control systems: a survey
TL;DR: In this paper, the authors focus on the promise of artificial neural networks in the realm of modelling, identification and control of nonlinear systems and explore the links between the fields of control science and neural networks.
Journal ArticleDOI
Discrete-time variable structure control systems
Weibing Gao,Yufu Wang,A. Homaifa +2 more
TL;DR: This paper presents a treatment of discrete variable structure control systems, and a recently introduced "reaching law approach" is conveniently used to develop the control law for robust control.
Journal ArticleDOI
Sliding mode control of a discrete system
TL;DR: In this paper, a stable discrete sliding mode control insensitive to the choice of sampling interval and not yielding chattering is presented, which is based on a discrete Lyapunov function and a sufficient condition of the control gain to make the system stable is given.
Journal ArticleDOI
Adaptive control of a class of nonlinear discrete-time systems using neural networks
Fu-Chuang Chen,Hassan K. Khalil +1 more
TL;DR: The result says that, for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity.