2 edition of Neural network based variable structure control for nonlinear discrete systems found in the catalog.
Neural network based variable structure control for nonlinear discrete systems
G. P. Liu
by University of Sheffield, Dept. of Automatic Control and Systems Engineering in Sheffield
Written in English
|Statement||G.P. Liu, V. Kadirkamanathan and S.A. Billings.|
|Series||Research report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.626, Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.626.|
|Contributions||Kadirkamanathan, V., Billings, S. A.|
J. Sarangapani et al., "Neural Network-Based Control of Nonlinear Discrete-Time Systems in Non-Strict Form," Proceedings of the 44th IEEE Conference on Decision and Control and the European Control Conference (, Seville, Spain), Institute of Electrical and Electronics Engineers (IEEE), Jan Cited by: 2. suited for neural network applications. Neural networksh aveb eent het opic of a number of special issues [Z], , and these are good sources of recent developments in other areas. In , , collections of neural network papers with emphasis on control ap- plications have appeared. Control Technology The use of neural networks in control sys-.
Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints Abstract: In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. An NN approach to robust MPC for constrained nonlinear systems with unmodeled dynamics is introduced in. A nonlinear NN-based MPC controller, for use in processes with an integrating response exhibiting long dead time, is designed and successfully applied to the temperature control of Author: Alex Alexandridis, Marios Stogiannos, Nikolaos Papaioannou, Elias N. Zois, Haralambos Sarimveis.
The neural network predictive controller that is implemented in the Deep Learning Toolbox™ software uses a neural network model of a nonlinear plant to predict future plant performance. The controller then calculates the control input that will optimize plant performance over a specified future time horizon. A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural by:
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Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous by: This paper presents a neural network based variable structure controller design procedure for unknown nonlinear discrete systems.
A neural network based affine nonlinear predictor is introduced so that the control algorithm is simple and easy to implement. Two cases are. Neural Network Control of Nonlinear Discrete-Time Systems - CRC Press Book Intelligent systems are a hallmark of modern feedback control systems.
But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured. Dynamic neural networks have the ability to approximate multi-input multi-output general nonlinear systems and have the differential equation structure.
This book presents a hybrid control strategy integrating dynamic neural networks and feedback linearisation into a predictive control : Jiamei Deng. Nonlinear Identification and Con-trol: A Neural Network Approach by G.P.
Liu, Springer, New York,pp., EuroISBN Re-viewed by Victor M. Becerra, Univer-sity of Reading, U.K. The field of neural networks is vast, with many different known network ar-chitectures and training algorithms.
This monograph deals with. Conclusion. In this paper, a neural-network-based control scheme for a class of nonlinear systems with actuator faults has been developed. A novel controller, which contains the fault compensator, has been designed, and the corresponding stability analysis has been also provided via the Lyapunov by: 9.
The book is a collection of contributions concerning the theories, applications and perspectives of Variable Structure Systems (VSS). Variable Structure Systems have been a major control design method.
devoted to nonlinear control theory. A neural network-based variable structure control design procedure is described. An aﬃne nonlinear pre-dictor is used. Besides, a discrete sliding mode control technique is introduced which oﬀers system stability and robustness.
Finally, a Cited by: 1. Abstract. In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) The neural identifier which is used to estimate the unknown nonlinear system, applies new learning by: 2.
This paper deals with the adaptive tracking problem for discrete-time induction motor model in presence of bounded disturbances. In this paper, a high order neural network structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques.
An adaptive sliding mode control design method is proposed for discrete nonlinear systems where explicit knowledge of the system dynamics is not available. Three-layer feedforward neural networks are used as function approximators for the unknown by: Neural network-based variable structure control for nonlinear discrete systems.
Research Report no.Technical Report, Department of Automatic Control and Systems Engineering, The Cited by: In this paper, adaptive neural network (NN) control is investigated for a class of single-input single-output (SISO) discrete-time unknown non-linear systems with general relative degree in the.
In Lee and Lee , Mu et al. , a data-based learning algorithm was proposed to derive an improved control policy for discrete-time nonlinear systems using ADP with an identified process. The control design method, which combined the backstepping design technique and the dynamic surface control approach, is applied to convert triangular structure systems to affine nonlinear systems.
"For contributions to variable structure systems theory and its applications in mechatronics" Salvatore Monaco "For contributions to nonlinear digital systems theory and control" Joseph Osullivan "For contributions to information-theoretic imaging with applications to medical tomographic systems and radar imaging" Stephen.
F.L. Lewis, S. Jagannathan, and A. Yesildirek, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor and Francis, London, Engineering Ethics History of Feedback Control Review Material Review of State Variable Systems Simulation of DT Systems and Filters.
Abstract: In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated.
By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed Cited by: 4. A neural network which has the ability to learn sophisticated nonlinear relationships provides an ideal means of modelling complicated nonlinear systems.
This paper addresses the issues related to the identification of nonlinear discrete-time dynamic systems using neural by: The BTTNC consists of two components: (1) a Neural Emulator Network to represent the structure to be controlled; and (2) a Neural Action Network to determine the control action on the structure.
The artificial neural-network controller is a newly developed technique for the purposes of control and has many attributes, such as massive. Motivated by the need for systematic neural control strategies for nonlinear systems, Stable Adaptive Neural Network Control offers an in-depth study of stable adaptive control designs using Author: Jinkun Liu.DYNAMIC NEURAL NETWORK-BASED ROBUST CONTROL METHODS FOR UNCERTAIN NONLINEAR SYSTEMS By Huyen T.
Dinh August Chair: Warren E. Dixon Major: Mechanical Engineering Neural networks (NNs) have proven to be effective tools for identiﬁcation, estimation and control of complex uncertain nonlinear systems.
As a natural extension of feedforward NNs. Chen, Z., Jagannathan, S.: Generalized Hamilton–Jacobi–Bellman formulation-based neural network control of affine nonlinear discrete-time systems. IEEE Trans. Neural Netw. 19(1), 90– () CrossRef Google Scholar.