CDC2000 Neural / Fuzzy Stability and Control

Neural / Fuzzy Stability and Control
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	On the Persistent Excitation Conditions of Adaptive Fuzzy Systems in Nonlinear Identifications Authors: Feng Wan, Li-Xin Wang, Volume: 1, Page 858 Paper number 1208 Abstract: This paper addresses the persistent excitation conditions of adaptive fuzzy systems in the identifications of nonlinear functions and nonlinear dynamical systems. The adaptive fuzzy system is constructed as a standard fuzzy system and the parameters in the fuzzy system are tuned on-line by the orthogonal projection algorithm. We first give the conditions under which the parameters in the fuzzy system can be uniquely determined, and then propose methods to design input signals with the persistent excitation property for adaptive fuzzy systems in the identifications of nonlinear moving average and auto-regressive moving average systems. CD001208.PDF (From Author) TOP Global Inverse Modeling For Nonlinear Non-Affine System Control By Wavelet Network Authors: Ying Tan, Jian-Xin Xu, Volume: 1, Page 864 Paper number 9804 Abstract: This paper presents a control scheme which learns the inverse mapping of a dynamic system by an orthonormal wavelet network. To compensate the modeling error caused by the model parameterization, feedback is added. The inverse mapping of dynamic system proposed here is defined as a mapping between the output trajectory and input trajectory. Training samples are chosen such that they can cover input trajectory space uniformly both in the amplitude domain and frequency domain . Here amplitude domain depends on the actuator while the frequency domain depends on sampling period of control system. For trajectory training, there are a lot of sample data(not sample trajectory) which enhance the complexity of modeling problem. Hence data compression is used by wavelet threshold which is a method frequently used in signal processing. The performance of proposed algorithm is illustrated by compute simulation experiment. CD009804.PDF (From Author) TOP Global Asymptotic Stability of a Class of Dynamic Neural Systems with Asymmetric Connection Weights Authors: Youshen Xia, Jun Wang, Volume: 1, Page 870 Paper number 1023 Abstract: Recently, a class of dynamic neural systems were presented and analyzed due to their good performance in optimization computation and low complexity for implementation. The global asymptotic stability of dynamic neural systems with symmetric weights was well studied. In this paper, we investigate the global asymptotical stability of a dynamic neural system with asymmetric weights. Since asymmetric weight cases are more general than symmetric ones, the new results are significant in both theory and applications. CD001023.PDF (From Author) TOP A NN Controller and Tracking Error Bound for Robotic Manipulators Authors: Jinyu Li, Danwei Wang, Volume: 1, Page 872 Paper number 1539 Abstract: In this paper, a robust neural network control scheme is proposed for robot tracking tasks. The neural network is trained on-line and the weight tuning algorithm has a small dead zone to overcome bounded disturbances. Under this proposed control scheme, it is shown that tracking error bound is completely determined by neural network approximation error bound, disturbance bound, as well as a control design parameter. The tracking error bound does not depend on the weight estimation errors. A two-link manipulator is used to illustrate the performance of the control scheme. CD001539.PDF (From Author) TOP Global Asymptotic Stability of Discrete-Time Recurrent Neural Networks Authors: Sanqing Hu, Jun Wang, Volume: 1, Page 877 Paper number 1183 Abstract: This paper presents new analytical results on the global asymptotic stability for the equilibrium states of a general class of discrete-time recurrent neural networks (DTRNNs) described by using a set of nonlinear difference equations. We at first provide several conditions for the global asymptotic stability of such DTRNNs. Because these conditions are not easy to be verified for a general DTRNN, to be more testable, we then present many sufficient conditions for the global asymptotic stability of DTRNNs. The resulting criteria include diagonal stability, global asymptotic stability by bounded constraint, and nondiagonal stability. These stability conditions are less restrictive than the existing ones in literature. CD001183.PDF (From Author) TOP Stability Analysis of Decentralized Adaptive Fuzzy Logic Control for Robot Arm Tracking Authors: Ming Liu, Volume: 1, Page 883 Paper number 1200 Abstract: The global ultimate stability of a decentralized adaptive fuzzy controller for trajectory tracking of robot manipulators is presented. Employing a PD control and a cubic feedback to ensure the global stability for robot tracking, an adaptive fuzzy logic scheme is incorporated to reduce the effects of interconnections, frictions, gravity force and other uncertainties. It shows that with a very limited knowledge on the sizes of interconnection terms, the controller guarantees the global ultimate boundedness of tracking errors. As the overall controller is based on a decentralized controller structure, it results in very simple fuzzy rules which can be implemented in most robot systems without hardware alternation. The simulation results are included for verification. CD001200.PDF (From Author) TOP Output Trajectory Tracking Using Dynamic Neural Networks Authors: Alexander S. Poznyak, Edgar N. Sanchez, Orlando Palma, Wen Yu, Volume: 1, Page 889 Paper number 1945 Abstract: This paper concerns the development of output trajectory tracking by means of dynamic neural networks for a class of unknown nonlinear systems. In order to obtain this tracking, first we develop a robust asymptotic neuro-observer. This Luenberger type observer includes two important terms: a first one, which assure the boundness of the weights and a second one, which introduces a time-delay term in order to approximate the derivatives of the measurable states. The Lyapunov-Krasovskii technique is used to proof the robust asymptotic stability ''on average'' of the neuro observer as well as boundness of the observation error. Then the trajectory tracking error is analyzed on the basis of a local optimal controller. Work is in progress to test the applicability of the approach to a nonlinear system such as a robotic manipulator. CD001945.PDF (From Author) TOP