CDC2000 Stochastic Control and Tuning Methodologies

Stochastic Control and Tuning Methodologies
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	Integrating Identification with Robust Control: a Mixed H_2/H_(infinity) Approach Authors: Liuping Wang, Graham C. Goodwin, Volume: 1, Page 3341 Paper number 1247 Abstract: In previous work we have proposed an H_2 design procedure aimed at bringing robust control and system identification closer together by using statistical confidence bounds. The key idea was to change the nominal design so as to reduce the overall variability from an a-priori specified desired performance. However, due to the choice of cost function, the original design method did not necessarily produce a robust control system with guaranteed stability. This paper addresses the latter problem. First a control validation procedure is proposed based on an estimated error bound; then the stability problem is formulated as a mixed H_2/H_(infinity) problem. The solution to this problem is obtained in the frequency domain using a classical algorithm due to Lawson (1961). An alternative algorithm is also described which uses standard quadratic programming methods. Finally a simulation example is presented which shows how the robust design method can be used in conjunction with standard identification procedures. CD001247.PDF (From Author) TOP Automatic Tuning for Classical Step-Response Specifications Using Iterative Feedback Tuning Authors: Magnus AAkerblad, Anders Hansson, Bo Wahlberg, Volume: 1, Page 3347 Paper number 9039 Abstract: The objective of this contribution is to study how to tune PID controllers with respect to classical step-response specifications using iterative feedback tuning. Typically the closed-loop response is improved considerably using only six to nine closed-loop experiments. CD009039.PDF (From Author) TOP A Robustness Result for Stochastic Control Authors: Gino Favero, Wolfgang J. Runggaldier, Volume: 1, Page 3349 Paper number 1024 Abstract: The solution of a stochastic control problem depends on the underlying model, i.e., on the probability measure induced by the model. The real world model may not be known precisely, and so one solves the problem for a hypothetical model that induces a measure generally different but close to the real one. We investigate two ways to derive a bound on the suboptimality of the hypothetical optimal control when it is used in the real problem. Both bounds are in terms of the Radon-Nikodym derivative of the real world measure with respect to the hypothetical one. CD001024.PDF (From Author) TOP A Learning Algorithm for Markov Decision Process with Adaptive State Aggregation Authors: John S. Baras, Vivek S. Borkar, Volume: 1, Page 3351 Paper number 1716 Abstract: We propose a simulation-based algorithm for learning good policies for a Markov decision process with unknown transition law, with aggregated stated. The state aggregation itself can be adapted on a slower time scale by an auxiliary learning algorithm. Rigorous justifications are provided for both algorithms. CD001716.PDF (From Author) CD001716.PDF (Scanned) TOP An Asymptotic Expansion in A 3-Dimensional Degenerated Control Problem With Finite Horizon Authors: Charles Marchetti, Volume: 1, Page 3357 Paper number 73 Abstract: We Study a degenerate nonlinear optimal stochastic control problem with finite horizon ([2]), [8] and [9]). Using the Hamilton-Jacobi-Bellman equation, we find an asymptotic expansion for this solution of this problem CD000073.PDF (From Author) TOP Covariance Analysis, Positivity and the Yakubovich-Kalman-Popov Lemma Authors: Rolf Johansson, Anders Robertsson, Volume: 1, Page 3363 Paper number 1280 Abstract: This paper presents theory and algorithms for covariance analysis and stochastic realization without any minimality condition imposed. Also without any minimality conditions, we show that several properties of covariance factorization and positive realness hold. The results are significant for validation in system identification of state-space models from finite input-output sequences. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem. The case considered includes the problem of rank-deficient residual covariance matrices, a case which is encountered in applications with mixed stochastic-deterministic input-output properties as well as for cases where outputs are linearly dependent, thus extending previous results in covariance analysis. CD001280.PDF (From Author) TOP Control for Bounded Pseudo ARMAX Stochastic Systems via Linear B-Spline Approximations Authors: Hong Wang, Volume: 1, Page 3369 Paper number 1449 Abstract: Following the recently developed algorithms for the control of the shape of the output probability density function for general dynamic stochastic systems (Wang, 1998; 1999 and 2000), this paper presents the modelling and control algorithms for pseudo ARMAX systems, where different from all the existing ARMAX systems the considered system is subjected to any arbitrary bounded random input and the purpose of the control input design is to make the output probability density function of the system output as close as possible to a given distribution function. At first, the relationship between the input noise distribution and the output distribution is established via linear B-spline approximations. This is then followed by the description on the control algorithm design and discussions on control of unstable systems and nonlinear ARMAX systems. CD001449.PDF (From Author) TOP