CDC2000 Nonlinear Model Predictive Control

Nonlinear 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	Relaxing The Optimality Condition In Receding Horizon Control Authors: Ali Jadbabaie, John Hauser, Volume: 1, Page 4945 Paper number 1781 Abstract: Receding horizon control is based on iteratively solving an open-loop finite horizon optimization problem. Despite its success in a variety of industrial applications, theoretical issues such as stability were not completely addressed until recently. It was shown in [JYHcdc99] that by utilizing a suitable Control Lyapunov Function (CLF) as terminal cost, the stability of the receding horizon scheme can be guaranteed and the region of attraction of the receding horizon controller can be estimated. The key point in this approach, which made it different from others, was removal of additional stability constraints, hence making the optimizations much easier to solve. A requirement implied in the previous results was being able to solve the optimizations globally . In this paper, that assumption is removed and it is shown that the optimality can be replaced by an improvement property. Specifically, instead of requiring the trajectories to be optimal, it is required that a certain amount of decrease in the cost is obtained at each receding horizon iteration. It is further shown that there always exist a sequence of controls which guarantee the necessary decrease in the cost. A numerical example using the inverted pendulum is presented to illustrate this point. CD001781.PDF (From Author) TOP Invariant Sets For Constrained Nonlinear Discrete-Time Systems With Application To Feasibility In Model Predictive Control Authors: Eric C. Kerrigan, Jan M. Maciejowski, Volume: 1, Page 4951 Paper number 1264 Abstract: An understanding of invariant set theory is essential in the design of controllers for constrained systems, since state and control constraints can be satisfied if and only if the initial state belongs to a positively invariant set for the closed-loop system. The paper briefly reviews some concepts in invariant set theory and shows that the various sets can be computed using a single recursive algorithm. The ideas presented in the first part of the paper are applied to the fundamental design goal of guaranteeing feasibility in predictive control. New necessary and sufficient conditions based on the control horizon, prediction horizon and terminal constraint set are given in order to guarantee that the predictive control problem will be feasible for all time, given any feasible initial state. CD001264.PDF (From Author) TOP Performance Analysis of Piecewise Linear Systems and Model Predictive Control Systems Authors: Alberto Bemporad, Fabio D. Torrisi, Manfred Morari, Volume: 1, Page 4957 Paper number 1678 Abstract: In their recent paper (Bemporad et al., 2000), the authors provided a tool for obtaining the explicit solution of constrained model predictive control (MPC) problems by showing that the control law is a continuous piecewise affine (PWA) function of the state vector. Therefore, the feedback interconnection between the MPC controller and a linear system, or a PWA system (e.g., a PWA approximation of a nonlinear system), is a PWA system. For discrete-time PWA and hybrid systems, we presented an algorithm for verification/reachability analysis in (Bemporad, Torrisi, Morari, 2000). In this paper, we formulate the performance analysis problem of closed-loop PWA systems (including MPC feedback loops where the prediction model and the plant model could be different) as a reachability analysis problem, and use our algorithm to obtain a tool for characterizing (i) the set of states for which the evolution is feasible, (ii) the domain of stability, (iii) the performance of the closed-loop. CD001678.PDF (From Author) TOP Model-Based Predictive Control for Hammerstein Systems Authors: Hayco H.J. Bloemen, Ton J.J. van den Boom, Henk B. Verbruggen, Volume: 1, Page 4963 Paper number 1213 Abstract: Hammerstein systems are a class of systems represented by a static nonlinearity at the input followed by a linear dynamic block. In this paper the static input nonlinearity is transformed into a polytopic description. The remaining uncertain linear model is used in a MPC algorithm of which the optimization problem involves minimization of a linear objective function subject to Linear Matrix Inequalities (LMIs), which is a convex problem. A procedure is presented to remove a number of LMIs from the optimization problem, prior to solving it. By means of an iterative procedure the conservatism of the polytopic description can be reduced. Nominal closed loop stability of this Hammerstein MPC algorithm is guaranteed. A comparison is presented between the proposed algorithm and an algorithm which removes the nonlinearity from the control problem via an inversion. CD001213.PDF (From Author) TOP Discontinuous Feedback Stabilization Using Nonlinear Model Predictive Controllers Authors: Fernando A. C. C. Fontes, Volume: 1, Page 4969 Paper number 1095 Abstract: We propose a Model Predictive Control (MPC) framework to generate feedback controls for time-varying nonlinear systems with input constraints. One of the main features of this framework is to allow the feedback laws to be discontinuous and thereby enlarge the class of nonlinear systems that can be stabilized by continuous-time MPC. We consider a continuous-time MPC framework and perform a continuous-time stability analysis while considering that the inter-sampling times are nonzero and that the open-loop optimal control problems are solved at every sampling instant. The feedback law generated by MPC is not a function of the state at every instant of time, rather it is a function of the state at the last sampling instant. The trajectories resulting from this ``sampling-feedback'' are well-defined even when the feedback is discontinuous. Important classes of nonlinear systems that could not be stabilized by a continuous feedback, such as the nonholonomic systems, can now be addressed in a continuous-time MPC framework. CD001095.PDF (From Author) TOP