CDC2000 Learning and Neuro-Control

Learning and Neuro-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	Robust Adaptive Nonlinear Control Using Single Hidden Layer Neural Networks Authors: Flavio Nardi, Anthony J. Calise, Volume: 1, Page 3825 Paper number 1165 Abstract: In this paper we develop an adaptive dynamic inverting controller with guaranteed closed loop stability for partially or completely unknown nonlinear non affine dynamic systems. We assume full state feedback and no zero dynamics. A single hidden layer neural network is used to approximate the inverse map, and a stable adaptive scheme is used to update on-line the neural network weights. Stability is guaranteed by introducing a robust adaptive bound. The performance of the adaptive scheme is demonstrated in a tracking task controller design and simulation for the nonlinear Van der Pol oscillator. CD001165.PDF (From Author) TOP Adaptive Control On Manifolds With RBF Neural Networks Authors: Valeri A. Terekhov, Ivan Yu. Tyukin, Danil V. Prokhorov, Volume: 1, Page 3831 Paper number 1935 Abstract: We propose a new method of adaptive control on manifolds for non-linear plants in the full-state feedback case using radial basis function (RBF) neural networks. We introduce a procedure for synthesis of adaptation algorithms based on associated performance criteria. We analyze applicability of the algorithms developed for a quadratic performance criterion. CD001935.PDF (From Author) TOP A Composite Energy Function Based Sub-Optimal Learning Control Approach for Nonlinear Systems with Time-Varying Parametric Uncertainties Authors: Ying Tan, Jian-Xin Xu, Volume: 1, Page 3837 Paper number 1417 Abstract: In this paper, a novel composite energy function (CEF) is introduced to provide a general framework for incorporating system information along both time and learning repetition horizon. Based on the CEF, learning control is integrated with nonlinear sub-optimal control to enhance control performance for a class of nonlinear system with time-varying parametric uncertainties. Sub-optimal control strategy based on control Lyapunov function (CLF) and Sontag's formula provides a sub-optimal performance as well as stability along time horizon for the nominal part of the nonlinear dynamic system. Learning mechanism tries to learn unknown time-varying parametric uncertainties so as to eliminate uncertain effects. The proposed control scheme achieves asymptotical convergence along learning repetition horizon. At the same time, the boundedness and pointwise convergence of the tracking error along time horizon are also ensured. CD001417.PDF (From Author) TOP Global Stability Of Periodic Orbits In Relay Feedback Systems Authors: Subbarao Varigonda, Tryphon T. Georgiou, Volume: 1, Page 3843 Paper number 2139 Abstract: In this paper, we provide a sufficient condition for the global stability of a periodic orbit using the contraction mapping theorem. The condition is obtained by identifying an invariant set of the system dynamics in which the Poincare map is continuous and contractive. An upper bound on the norm of the derivative of the map is obtained by exploiting its geometric structure. CD002139.PDF (From Author) TOP Passivity Properties of Neuro Identifier Authors: Wen Yu, Xiaoou Li, Volume: 1, Page 3848 Paper number 2107 Abstract: In this paper the passivity approach is applied to access several stability properties of neuro identifier. A dynamic neural network is used for nonlinear system no-line identification. By using a simple gradient learning law, the conditions for passivity, stability, asymptotic stable and input-to-state stability are established. We get a very interesting result: gradient algorithm is robust with respect to all kinds of bounded uncertainties for neuro identifier. CD002107.PDF (From Author) TOP