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8th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services

作者： Antic, Dragan Milojkovic, Marko Jovanovic, Zoran Univ Nis Fac Elect Engn Aleksandra Medvedeva 14 Nish 18000 Serbia

ISBN: (纸本)9788685195549

This paper considers a new method for nonlinear systems control based on genetic algorithms applied on fuzzy-sliding mode. This new control method achieves good results in terms of robustness, dynamics of control, and controlled signals. It also solves the chattering problem. Illustration and verification of this new method is done by simulation of inverted pendulum system.

关键词： nonlinear system control fuzzy logic sliding mode genetic algorithm

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Sliding mode control for uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones

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IEEE TRANSACTIONS ON systemS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第1期34卷 374-380页

作者： Hsu, KC Wang, WY Lin, PZ Fu Jen Catholic Univ Dept Elect Engn Taipei 24205 Taiwan

In this paper, we investigate a novel robust control approach for a class of uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones. Sliding mode control (SMC) is suggested to design stabilizing, controllers for these uncertain nonlinear systems. The controllers guarantee the global reaching condition of the sliding mode in these systems. They can work effectively for systems either with or without sector nonlinearities and deadzones in the inputs. Moreover, the controllers ensure that the system trajectories globally exponentially converge to the sliding mode. Illustrative examples are demonstrated to verify the effectiveness of the proposed sliding mode controller.

关键词： deadzone control nonlinear system control sector nonlineatities sliding mode control

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Sliding mode closed-loop control of FES: controlling the shank movement

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2004年第2期51卷 263-272页

作者： Jezernik, S Wassink, RGV Keller, T ETH Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland Univ Hosp Balgrist Spinal Cord Injury Ctr ParaCare CH-8008 Zurich Switzerland

Functional electrical stimulation (FES) enables restoration of movement in individuals with spinal cord injury. FES-based devices use electric current pulses to stimulate and excite the intact peripheral nerves. They produce muscle contractions, generate joint torques, and thus, joint movements. Since the underlying neuromuscular-skeletal system is highly nonlinear and time-varying, feedback control is necessary for accurate control of the generated movement. However, classical feedback/closed-loop control algorithms have so far failed to provide satisfactory performance and were not able to guarantee stability of the closed-loop system. Because of this, only open-loop controlled FES devices are in clinical use in spite of their limitations. The purpose of the reported research was to design a novel closed-loop FES controller that achieves good tracking performance and guarantees closed-loop stability. Such a controller was designed based on a mathematical neuromuscular-skeletal model and is founded on a sliding mode control theory. The controller was used to control shank movement and was tested in computer simulations as well as in actual experiments on healthy and spinal cord injured subjects. It demonstrated good robustness, stability, and tracking performance properties.

关键词： FES modulation nonlinear system control rehabilitation engineering spinal cord injury

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Stable adaptive fuzzy controller with time-varying dead-zone

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FUZZY SETS AND systemS 2001年第1期121卷 161-168页

作者： Koo, KM Kangnung Natl Univ Dept Elect Engn Kangwondo 210702 South Korea

This paper proposes an adaptive fuzzy control scheme for a class of continuous-time nonlinear dynamic systems for which explicit linear parameterizations of the uncertainties are either unknown or impossible. To improve robustness under the approximation errors and disturbances, the proposed scheme includes a dead-zone in adaptation laws which varies its size adaptively. The assumption of known bounds on the approximation errors and disturbances is not required since those are estimated using adaptation laws. The overall adaptive scheme is proven to guarantee global uniform ultimate boundedness in the Lyapunov sense. (C) 2001 Elsevier Science B.V.. All rights reserved.

关键词： control theory adaptive fuzzy system nonlinear system control direct adaptive tracking control time-varying dead-zone

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An adaptive neural regulator of minimum variance 5

An adaptive neural regulator of minimum variance

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5th Seminar on Neural Network Applications in Electrical Engineering

作者： Ratkovic, N Stankovic, S Univ Belgrade Fac Mech Engn YU-11001 Belgrade Yugoslavia

ISBN: (纸本)0780355121

This paper presents an extension of the Minimum Variance control Strategy (MV) to nonlinear models. The synthesis of the optimal adaptive controller of reduced complexity is presented - the regulator consists of P. PI or PID.. and perhaps of nonlinearity. Parameters of this regulator (P. PI and PID gains with nonlinearity parameters) are tuned to minimize a quadratic criterion function. The process model is assumed to be known or estimated via neural network of appropriate structure, The process model has linear autoregressive part and nonlinear function of the exogenous input. Parameters adaptation is performed for several reference types and different regulator structures.

关键词： neural networks nonlinear system control adaptive control

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Planning with a functional neural-network architecture

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1999年第1期10卷 115-127页

作者： Panagiotopoulos, DA Newcomb, RW Singh, SK Technol & Educ Inst Thessaloniki Automat Dept Thessaloniki Greece Univ Maryland Dept Elect Engn College Pk MD 20742 USA

This article introduces the concept of planning in an interactive environment between two systems: the challenger and the responder, The responder's task is to produce behavior that relates to the challenger's behavior through some response function. In this setup, we concentrate planning on the responder's actions and use the produced plan in order to control the responder. In general, the responder is assumed to be a nonlinear system whose input-output (I/O) map may be expressed by a Volterra series. The planner uses an estimate of the challenger's future output sequence, the response function, and a model of the responder's I/O relation implemented through a functional artificial neural network (FANN) architecture, in order to produce the input sequence that will be applied to the responder in the future, in parallel-time with the challenger's corresponding output sequence. The responder accepts input from the planner, which may be combined with feedback information, in order to produce an output sequence that relates to the challenger's output sequence according to the response function. The importance of planning for the generation of smooth behavior is discussed, and the effectiveness of the planner's implementation using neural network technology is demonstrated with an example.

关键词： functional neural networks interacting systems nonlinear system control path tracking planning Volterra functionals

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nonlinear-system control-BASED ON MULTIRESOLUTION RADIAL-BASIS COMPETITIVE AND COOPERATIVE NETWORKS

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NEUROCOMPUTING 1995年第2期9卷 187-206页

作者： LEE, S LEE, JM CALTECH JET PROP LABPASADENACA 91109 KANGWEON NATL UNIV DEPT CONTROL & INSTRUMENTAT ENGCHUNCHON 200701SOUTH KOREA

Most mapping neural networks to date can learn only one-to-one and many-to-one mapping. However, the inverse control of certain nonlinear systems requires to handle one-to-many inverse mapping, which most conventional mapping neural networks are unable to cope with. This paper presents a novel Multi-resolution Radial-basis Competitive and Cooperative Network (MRCCN) capable of dealing with one-to-many inverse mapping for control. MRCCN self-organizes a collection of local clusters of various locations, shapes and sizes to represent an arbitrary many-to-many mapping under uniform mapping accuracy. MRCCN is able to retrieve multiple outputs based on the cooperative decision of local clusters relevant to the given input. The highlight of this paper is the demonstration that MRCCN is able to control those nonlinear systems that can not be handled by conventional mapping networks due to the one-to-many mapping involved. Simulation results are shown.

关键词： RADIAL-BASIS NETWORKS nonlinear system control COMPETITIVE AND COOPERATIVE NETWORKS CLUSTERING MANY-TO-MANY MAPPING

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Time delay control of nonlinear systems with neural network modeling

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MECHATRONICS 1997年第7期7卷 613-640页

作者： Lee, JW Oh, JH Korea Adv Inst Sci & Technol Taejon 305701 South Korea

Time Delay control (TDC) that uses a multilayer neural network as a nonlinear plant modeler is proposed in this paper. In the proposed controller structure, TDC is used to compensate for the changes of the plant and/or uncertainties including neural network modeling errors. The neural network adapts to learn the uncertainties and the changes in the system, which are eventually embedded into the neural network. In this way, the proposed method exhibits short-term adaptability through TDC and long-term adaptability through neural network adaptation. Because the method uses a neural network as a modeler, it can be effective for the control of nonlinear systems which are hard to model in an analytic way;it also has the ability to cancel out unmodeled dynamics. It is proved that neural network learning error and control error is uniformly bounded. Computer experiments reveal that the proposed algorithm is effective in controlling nonlinear systems. (C) 1997 Elsevier Science Ltd.

关键词： multilayer neural network backpropagation time delay control nonlinear system control Jacobian of neural network

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Inversion control of nonlinear systems with neural network modelling

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IEE PROCEEDINGS-control THEORY AND APPLICATIONS 1997年第5期144卷 481-487页

作者： Lee, JW Oh, JH Kia Motors Dept Res & Dev Prod & Engn Div Gwang Myoung Shi 423701 Gyoung Gi Do South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Taejon 305701 South Korea

A method of controlling certain types of nonlinear dynamical systems whose dynamics can be modelled by a multilayer neural network is proposed. The control algorithm assumes that the plant equations are not known but the dimension of the system is known, The control input is derived by inversion of a forward neural network via the Newton Raphson method. During inversion of the multilayer neural network some optimal control senses are resolved. To suppress the control error due to the modelling error of the forward neural network, the inversion controller with a conventional feedback controller is proposed, which provides a better performance than a pure inversion controller. The proposed algorithm shows various advantages, and computer experiments on a bioreactor prove the effectiveness of this algorithm.

关键词： nonlinear system control multilayer neural network inversion of neural network Newton-Raphson method

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Nuclear Reactor control using Diagonal Recurrent Neural Networks

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IFAC Proceedings Volumes 1993年第2期26卷 797-800页

作者： C.-C. Ku K.Y. Lee R.M. Edwards Department of Electrical and Computer Engineering The Pennsylvania State University University Park PA 16802 USA Department of Nuclear Engineering The Pennsylvania State University University Park PA 16802 USA

A new approach for wide-range optimal reactor temperature control using Diagonal Recurrent Neural Networks (DRNN) with adaptive learning rate scheme is presented. The draw-back of the usual feedforward neural network (FNN) is that it is a static mapping and requires a large number of neurons. The usual fixed learning rate based on empirical trial and error scheme is slow and does not guarantee convergence. The DRNN is for dynamic mapping and requires much less number of neurons and weights, and thus converges faster than FNN. Rapid convergence of this DRNN-based control system is demonstrated when applied to improve reactor temperature performance.

关键词： Artificial neural nets nuclear reactor control nonlinear system control

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