CDC2000 Robustness and Optimisation

Robustness and Optimisation
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 Avoiding Vertexization of Robustness Problems: The Approximate Feasibility Concept Authors: B. Ross Barmish, Pavel S. Shcherbakov, Volume: 1, Page 1031 Paper number 1967 Abstract: For a large class of robustness problems with uncertain parameter vector q confined to a box Q, there are many papers providing results along the following lines: The desired performance specification is robustly specified for all q in Q if and only if it is satisfied at each vertex q_i of Q. Since the number of vertices of G explodes combinatorically with the dimension of q, the computation associated with the implementation of such results is often intractable. The main point of this paper is to introduce a new approach to such problems. To this end, the definition of approximate feasibility is introduced, and the theory which follows from this definition is vertex-free. CD001967.PDF (From Author) TOP Probabilistic Robust Design with Linear Quadratic Regulators Authors: Boris T. Polyak, R. Tempo, Volume: 1, Page 1037 Paper number 1212 Abstract: In this paper, we study robust design of uncertain systems in a probabilistic setting by means of Linear Quadratic Regulators. We consider systems affected by random bounded nonlinear uncertainty so that classical optimization methods based on Linear Matrix Inequalities cannot be used without conservatism. The approach followed here is a blend of randomization techniques for the uncertainty together with convex optimization for the controller parameters. In particular, we propose an iterative algorithm for designing a controller which is based upon subgradient iterations. At each step of the sequence, we first generate a random sample and then we make a subgradient step for a convex constraint defined by the LQR problem. The main result of the paper is to prove that this iterative algorithm provides a controller which quadratically stabilizes the uncertain system with probability one in a finite number of steps. In addition, at a fixed step, we compute a lower bound of the probability that a quadratically stabilizing controller is found. CD001212.PDF (From Author) TOP Global Uniqueness Tests for H-Infinity Optima Authors: J. William Helton, Marshall A. Whittlesey, Volume: 1, Page 1043 Paper number 1720 Abstract: Optimization of sup norm type performance functions over the space of H-infinity functions is central to the subject of H-infinity design. Problems with a large amount of plant uncertainty are often highly non-convex and therefore may have many solutions. In this article, even for highly non-convex problems, we give a test one can perform, once a local optimum f has been computed, to see if it is a global optimum. The uniqueness phenomena we discovered uses H-infinity properties (stability properties) heavily and are considerably stronger than what occurs in other types of general optimization. One of the least intuitive properties of SISO control is that a (local) optimum for a carefully set up H-infinity problem even with large amounts of plant uncertainty is unique. Such problems can be quite non -convex so the fact is surprising. While the result is false in general for MIMO control, in this note we are describing MIMO situations where uniqueness holds. The setting in this paper is simultaneous (Pareto) optimization of several competing performances and we obtain uniqueness results for its solutions. CD001720.PDF (From Author) TOP Generalized Design of Mixed H-Infinity/Deadbeat Suboptimal Controllers for SISO Continuous-Time Servo Systems Authors: Satoru Tanaka, Katsuhisa Furuta, Volume: 1, Page 1049 Paper number 9906 Abstract: This paper is concerned with a mixed H-infinity/deadbeat suboptimal control problem for SISO continuous servo systems. By considering the deadbeat tracking, time domain performance may improve. Simultaneously, to improve frequency domain performance of the closed loop system, H-infinity norm constraint is introduced. This problem gives the deadbeat tracking control with H-infinity norm constraint and has been studied by Nobuyama et al. and Tsumura et al. individually. However, there is a structural difference between their designs. This paper proposes a new controller design of the mixed H-infinity/deadbeat suboptimal control problem. The controller is more general since the structure of the controller covers those of the previous two designs. CD009906.PDF (From Author) TOP Risk-Sensitive and Robust Control of Discrete Time Hybrid Systems Authors: Thordur Runolfsson, Volume: 1, Page 1055 Paper number 1912 Abstract: In this paper we study systems that are subject to sudden structural changes due to either changes in the operational mode of the system or due to failure. We consider linear dynamical that depend on a modal variable which is either modeled as a finite state Markov chain or generated by an automaton that is subject to an external disturbance. In the Markov chain case the objective of the control is to minimize a risk sensitive cost functional. The risk sensitive cost functional measures the risk sensitivity of the system to transitions caused by the random modal variable. In the case when a disturbed automaton describes the modal variable, the objective of the control is to make the system as robust to changes in the external disturbance as possible. Optimality conditions for both problems are derived and it is shown that the disturbance rejection problem is closely related to a certain risk sensitive control problem for the hybrid system. CD001912.PDF (From Author) TOP A Disk ``Optimal'' Robust Pole Assignment Using Genetic Algorithms Authors: Giuseppe Franzé, Pietro Maria Muraca, Volume: 1, Page 1061 Paper number 1084 Abstract: This paper presents a method for determining the maximum allowable perturbation, for m.i.m.o. linear-invariant uncertain system, such that the closed-loop poles are assigned in a specifie disk by static output feedback; the uncertainty being norm-2 bounded. A sufficient condition for d-stabilizability is derived. Hence, in order to solve the related optimization problem a genetic-like algorithm is performed. An illustrative example shows the effectiveness of the proposed procedure. CD001084.PDF (From Author) TOP