CDC2000 Model Predictive Control

Model Predictive Control
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	Stabilizing Receding Horizon H-Infinity Controls for Linear Continuous Time-Varying Systems Authors: Ki Baek Kim, Tae-Woong Yoon, Volume: 1, Page 148 Paper number 1637 Abstract: In this paper, new matrix inequality conditions on the terminal weighting matrices are proposed for linear continuous time-varying systems. Under these conditions, nonincreasing and nondecreasing monotonicities of the saddle point value of a dynamic game are shown to be guaranteed. It is proved that the proposed terminal inequality conditions ensure the closed-loop stability of the receding horizon H-infinity control (RHHC). The stabilizing RHHC guarantees an H-infinity norm bound of the close-loop system. The proposed terminal inequality conditions for the monotonicity of the saddle point value and the closed-loop stability include most well-known existing terminal conditions as special cases. The results for time-invariant systems are obtained correspondingly from those in the time-varying case. CD001637.PDF (From Author) TOP Global Analytical Model Predictive Control with Input Constraints Authors: María M. Seron, José A. De Doná, Graham C. Goodwin, Volume: 1, Page 154 Paper number 1817 Abstract: We derive a closed-form global analytical solution for Model Predictive Control (MPC) of linear, discrete-time systems, subject to a quadratic performance index and hard magnitude constraints at the system input. The solution is shown to be a partition of the state space in regions for which an analytic expression is given for the corresponding control law. Both the regions and the control law are characterised in terms of the parameters of the open-loop optimal control problem that underlies MPC. The result exploits the geometric properties of quadratic programming. CD001817.PDF (From Author) TOP Linear Predictive Pole-Placement Control: Practical Issues Authors: Peter J. Gawthrop, Volume: 1, Page 160 Paper number 1632 Abstract: Some of the theoretical properties of predictive-pole-placement control (a form of model-based predictive control) are given a practical interpretation and corresponding design rules suggested. CD001632.PDF (From Author) TOP Robust Finite Horizon Model Predictive Control Without Terminal Constraints Authors: Camile Rowe, Jan M. Maciejowski, Volume: 1, Page 166 Paper number 1615 Abstract: In this paper a procedure for obtaining the parameters of a finite horizon model predictive controller to make it equivalent to an H infinity normalised left coprime factorisation (NLCF) controller in the unconstrained case will be considered. The procedure will be based on a Linear Matrix Inequalities (LMI) approach to the solution of the discrete-time H-infinity control problem and will be solved by first considering the solution when the state is available for measurement. CD001615.PDF (From Author) TOP Improved Stabilising Conditions For Model Predictive Control Authors: David Q. Mayne, José A. De Doná, Graham C. Goodwin, Volume: 1, Page 172 Paper number 1166 Abstract: It is known that stability of a model predictive control system is ensured if the terminal conditions of the optimal control problem solved online satisfy certain criteria. The usual requirement is that the terminal cost function is a control Lyapunov function defined on the terminal constraint set. Conventionally the terminal cost function is chosen, when the system being controlled is linear, to be the value function for the infinite horizon unconstrained optimal control problem and the terminal constraint set is chosen to be the output admissible set for the closed-loop system using the optimal unconstrained controller u=-Kx. The purpose of this paper is to relax these terminal conditions thereby facilitating online solution of the optimal control problem. Using some recent results, we present alternative conditions that employ, as the terminal cost, the infinite horizon cost resulting from a nonlinear controller u=-sat(Kx) and, as the terminal constraint set, the set in which this controller is optimal for the infinite horizon constrained optimal control problem. It is shown that this solution provides a considerably larger terminal constraint set. CD001166.PDF (From Author) TOP Robustification of Model Predictive Control Authors: Daniel E. Quevedo, Mario E. Salgado, Volume: 1, Page 178 Paper number 1011 Abstract: A general procedure leading to an enhancement of robustness of existing Model Predictive Control techniques is proposed. This procedure, which considers additive modeling errors, is illustrated for the case of Cautious Stable Predictive Control. The basic idea is the augmentation of the cost function with an additional term related to a description of the nominal model uncertainty, leading either to a minimization or to a min-max optimization problem, depending on the class of error description being used. CD001011.PDF (From Author) TOP