CDC2000 Optimisation Approaches and Methods

Optimisation Approaches and Methods
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	Granular Optimization: An Approach to Function Optimization Authors: Yu-Chi Ho, Jonathan T. Lee, Volume: 1, Page 103 Paper number 1700 Abstract: Finding a function that minimizes a functional is a common problem, e.g., determining the feedback control law for a system. However, it remains to be a challenge due to the large and structureless search space. In this paper, we present a search algorithm, granular optimization, to deal with this type of problems under some mild constraints. The algorithm is tested on two different problems. One of them is the well-known Witsenhausen counterexample. On the counterexample, the result from our automated algorithm comes close to the currently known best solution, which involves much human intervention. This shows the potential usefulness of the algorithm in more general problems. CD001700.PDF (From Author) TOP Lyapunov Methods in Nonsmooth Optimization, Part I: Quasi-Newton Algorithms for Lipschitz, Regular Functions Authors: Andrew R. Teel, Volume: 1, Page 112 Paper number 1556 Abstract: A recent converse Lyapunov theorem for differential inclusions is used to generate a large class of algorithms for nonsmooth optimization. Particular attention is given to quasi-Newton algorithms for the minimization of locally Lipschitz, regular functions. The main contribution is to show that when such functions have compact sublevel sets, they generically admit smooth functions that decrease along every direction in the generalized gradient at every point that is not a stationary point. This generalizes a well-known result for convex functions which states that the Euclidean distance to the minimizer is a smooth descent function. We also show that linear transformations on the generalized gradient also admit smooth descent functions. This fact enables a large class of quasi-Newton algorithms for nonsmooth optimization. CD001556.PDF (From Author) CD001556.PDF (Scanned) TOP Lyapunov Methods in Nonsmooth Optimization, Part II: Persistently Exciting Finite Differences Authors: Andrew R. Teel, Volume: 1, Page 118 Paper number 1987 Abstract: A recent converse Lyapunov theorem for differential inclusions is used to generate a class of finite difference algorithms for nonsmooth optimization. The algorithms rely on a proof of asymptotic stability for differential inclusions that contain persistently exciting signals and the ability to approximate these differential inclusions with finite differences. The notion of persistency of excitation that is used here generalizes that which is typically used in the identification and adaptive control literature. CD001987.PDF (From Author) TOP Stochastic Optimization of Controlled Partially Observable Markov Decision Processes Authors: Peter L. Bartlett, Jonathan Baxter, Volume: 1, Page 124 Paper number 1788 Abstract: We introduce an on-line algorithm for finding local maxima of the average reward in a Partially Observable Markov Decision Process (POMDP) controlled by a parameterized policy. Optimization is over the parameters of the policy. The algorithm's chief advantages are that it requires only a single sample path of the POMDP, it uses only one free parameter (beta), which has a natural interpretation in terms of a bias-variance trade-off, and it requires no knowledge of the underlying state. In addition, the algorithm can be applied to infinite state, control and observation spaces. We prove almost-sure convergence of our algorithm, and show how the correct setting of (beta) is related to the mixing time of the Markov chain induced by the POMDP. CD001788.PDF (From Author) TOP Optimization of a Sensor-Fault-Detection-Filter via Genetic Algorithms Authors: Stefan M. Jakubek, Hanns P. Jörgl, Volume: 1, Page 130 Paper number 1647 Abstract: In this work the principle of observer-based sensor fault detection and isolation is improved by the use of genetic optimization algorithms. Residual signals are generated by taking linear combinations of the observation errors such that asymptotic decoupling can be achieved. While the residual-generator itself is easy to implement its design in view of fault-isolation turns out to be a complex problem. It is demonstrated how the observer-eigenstructure can be optimized for transient decoupling of the residuals using genetic optimization algorithms. In order to illustrate its applicability, the method is applied to an industrial turbo-charged combustion engine power plant. CD001647.PDF (From Author) TOP A High-Order Local Maximum Principle For Abnormal Extremals - Examples Authors: Urszula A. Ledzewicz, Heinz M. Schättler, Volume: 1, Page 136 Paper number 17 Abstract: We illustrate a generalized local Maximum Principle published earlier which gives necessary conditions for optimality of abnormal trajectories in optimal control problems. In this theorem the multiplier associated with the objective is non-zero. CD000017.PDF (From Author) TOP Extremum Seeking Loops with Assumed Functions Authors: Jason L. Speyer, Ravi N. Banavar, David F. Chichka, Ihnseok Rhee, Volume: 1, Page 142 Paper number 2088 Abstract: Extremum seeking (also peak-seeking) controllers are designed to operate at an unknown set-point that extremizes the value of a performance function. This performance function is approximated by an assumed function with a finite number of parameters. These parameters, which are estimated on-line, are assumed to change slowly compared to the plant and compensator dynamics. Philosophically, the approach of assuming a function is in contrast with traditional approaches that use time scale separation between gradient computation and function minimization and the system dynamics. To analyze our current scheme, quadratic functions or exponentials of quadratic functions are assumed as approximations to the performance function. This allows the peak-seeking control loop to be reduced to a linear system. For this loop, compensators can be designed and robust performance and stability analysis of the loop due to parameter uncertainty in the assumed performance functions can be obtained. CD002088.PDF (From Author) TOP