CDC2000 Adaptive Control: Linear Systems

Adaptive Control: Linear Systems
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	Adaptive/Self-Tuning PID Control by Frequency Loop-Shaping Authors: Elena Grassi, Kostas S. Tsakalis, Sachi Dash, Sujit V. Gaikwad, Gunter Stein, Volume: 1, Page 1099 Paper number 9157 Abstract: This paper addresses issues arising in the on-line adaptation of PID controller parameters. With frequency loop-shaping principles as the underlying controller design approach, the PID parameter adaptation can be performed directly by minimizing a suitable estimation error with standard least-squares algorithms. The paper also presents an adaptive algorithm that approximates the minimization of the H-infinity norm of the error operator. A numerical example is used to illustrate the implementation of the algorithm. CD009157.PDF (From Author) TOP A New Design Method of Plug-in Adaptive Controller via Root Locus Technique Authors: Hiroyuki Miyamoto, Hiromitsu Ohmori, Akira Sano, Volume: 1, Page 1102 Paper number 9111 Abstract: This paper presents a new design method of Plug-in Adaptive Controller (P-in AC) that can reject periodic disturbance in adaptive manner at selected frequencies independently. Our proposed controller for rejecting disturbance is designed by evaluating the movement of the poles on imaginary axis, and does not need full information about the error system which derives adaptive law. So the design method becomes simpler than our conventional method. CD009111.PDF (From Author) TOP Continuous-Time Adaptive Observer for Linear System with Unknown Time Delay Authors: Michiru Sugimoto, Hiromitsu Ohmori, Akira Sano, Volume: 1, Page 1104 Paper number 9905 Abstract: Although many objectives involving the time-delay system exist, the time-delay systems are often dealt as a finite dimensional system by using Pade approximation, etc. This paper presents a design of a continuous-time adaptive observer for the linear system with the unknown time delay without using such a approximation. To our best knowledge, no research result of an adaptive observer for the unknown time-delay system designed without the approximations is proposed until now. CD009905.PDF (From Author) TOP A Self-Optimizing Adaptive LQG Control Scheme For Input-Output Systems Authors: Maria Prandini, Marco C. Campi, Volume: 1, Page 1110 Paper number 1730 Abstract: In this paper, we consider the optimal control problem of an unknown linear system in input-output form based on the linear quadratic Gaussian (LQG) control design method. A self-tuning LQG control scheme is proposed which is shown to be stable and self-optimizing. Optimality is achieved by using a new identification algorithm which incorporates a cost-biasing term favoring the parameters with smaller LQG optimal cost and a second term aiming at moderating the time-variability of the estimate. CD001730.PDF (From Author) TOP Automatic Tuning Of The Modified Smith Predictor Controllers Authors: Somanath Majhi, Derek P. Atherton, Volume: 1, Page 1116 Paper number 1325 Abstract: A simple relay feedback automatic tuning method is proposed for the modified Smith predictor, to provide a controller for stable, unstable and integrating processes with long dead time. A single asymmetrical relay feedback test is used to obtain a reduced order process model in terms of a second order dynamics plus dead time model. Very simple but straightforward tuning formulae are derived for the controllers which have a simple relationship with the plant model parameters. Thus the plant model blocks in the Smith predictor structure, as well as all the controllers are designed from a single asymmetrical relay test. Excellent performance of the auto-tuned Smith predictor has been substantiated by simulations. CD001325.PDF (From Author) TOP Adaptive Pole-Placement Control of MIMO Stochastic Systems Authors: Wen-Shyong Yu, Hung-Ming Huang, Volume: 1, Page 1121 Paper number 89 Abstract: In this paper, an adaptive pole-placement control algorithm using delayed normalized least mean squares (DNLMS) estimation with inverse logarithm step size is proposed for controlling the multi-input multi-output (MIMO) stochastic systems. The DNLMS estimation is used to identify the plant parameters and then a pole-placement controller is designed and adaptively adjusted using the estimates. Based on the assumptions of a mixing input condition and the satisfaction of a certain law of large numbers, the estimation with inverse logarithm step size has almost sure convergence. Further, by using the perturbation scheme, the control algorithm facilitates the establishment of the adaptive pole-placement control and prevents the closed-loop control system from occurring unstable pole-zero cancellation. An analysis shows that the proposed control algorithm guarantees parameter estimation convergence and system stability in the mean squares sense, with the output of the system approaching zero if there are no uncertainties and disturbances and converging to a neighborhood of zero if they exist. A series of simulations for controlling a mobile robot system are given to illustrate the effectiveness of the proposed scheme. The results show that the proposed control scheme is fairly robust for systems with uncertainties as well as has satisfactory performance characteristics. CD000089.PDF (From Author) TOP