CDC2000 Numerical Design and Analysis Techniques for Nonlinear Systems

Numerical Design and Analysis Techniques for Nonlinear 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	Set Stabilization Of Sampled-Data Nonlinear Differential Inclusions Via Their Approximate Discrete-Time Models Authors: Dragan Nesić, Andrew R. Teel, Volume: 1, Page 2112 Paper number 1201 Abstract: We present conditions under which a family of controllers that semiglobally-practically asymptotically (SPA) stabilizes a set for a family of discrete-time approximations of a nonlinear differential inclusion also SPA stabilizes that set for the inclusion's family of exact discrete-time models, for sufficiently small sampling periods. The result has the following important features: it does not require any regularity assumptions on the designed controllers; it is applicable to dynamic control laws; and it is stated for stability with respect to sets that are not necessarily compact. CD001201.PDF (From Author) TOP Attractors Under Perturbation And Discretization Authors: Lars Grüne, Volume: 1, Page 2118 Paper number 1202 Abstract: Using control theoretic techniques we give a necessary and sufficient condition for the convergence of attractors in one step discretizations of ordinary differential equations and obtain estimates for the resulting discretization error. The necessary and sufficient condition is based on a robustness property for an associated perturbed system, which is closely related to but slightly weaker than the input-to-state stability property well known in control theory. CD001202.PDF (From Author) TOP An Algorithm For The Optimal Control Of The Driving Of Trains Authors: Rüdiger Franke, Peter Terwiesch, Markus Meyer, Volume: 1, Page 2123 Paper number 1203 Abstract: We discuss an algorithm that optimizes the driving style of a train. The objective is to minimize the electrical energy used for traction subject to constraints such as travel time, speed limits, available traction power, etc. The optimization is based on a nonlinear point-mass model of the train, which includes the equations of motion and which considers the setpoint-dependent efficiency of the propulsion system. Although nonlinear, the equations of motion are formulated in a way that allows their piecewise analytical solution, thus greatly increasing computational efficiency. A discrete dynamic programming algorithm is developed for the deterministic and efficient numerical solution of the nonlinear optimal control problem. Both simulation results and practical measurements indicate energy savings between 10 and 30%, depending on operating conditions. The resulting optimal driving style is qualitatively different from previous solutions obtained with more simplified train models as reported in the literature. The algorithm forms a suitable basis for a nonlinear model-predictive controller operating in hard real time. CD001203.PDF (From Author) TOP Computing Control Lyapunov Functions via a Zubov Type Algorithm Authors: Lars Grüne, Fabian Wirth, Volume: 1, Page 2129 Paper number 1204 Abstract: In this paper we present a scheme for the determination of control Lyapunov functions which can be used as a basis for numerical computations. Under the assumption of local asymptotic nullcontrollability we define the domain of asymptotic nullcontrollability. On this set a control Lyapunov function is defined via an optimal control problem. It is then shown that this function can be characterized as the unique viscosity solution of a partial differential equation which can be interpreted as a generalization of Zubov's equation. CD001204.PDF (From Author) TOP Computation of Control Sets Using Subdivision and Continuation Techniques Authors: Dietmar Szolnoki, Volume: 1, Page 2135 Paper number 1205 Abstract: A key notion for the analysis of the global behavior of control systems are control sets. Control sets are subsets of the state space where approximate controllability holds: from every point in a control set one can steer arbitrarily close to any other point in the control set. In general it is not possible to find explicit formulas for control sets and their domains of attraction. Therefore numerical methods are a natural part of a systematic analysis. We will present a method for the computation of control sets, which is based on subdivision and continuation techniques. CD001205.PDF (From Author) TOP Computation Of Almost Invariant Sets For Perturbed Systems Authors: Fritz Colonius, Wolfgang Kliemann, Volume: 1, Page 2141 Paper number 1206 Abstract: Using the relation between the supports of invariant Markov measures and invariant control sets we discuss the characterization of almost invariant sets for Markov diffusion systems CD001206.PDF (From Author) TOP