Assessment and Future Directions of Nonlinear Model Predictive Control

ISBN-13: 9783540726982 / Angielski / Miękka / 2007 / 644 str.

Frank Allgower; Lorenz Biegler

Opis

Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today's applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years, with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today's computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.

Recenzje

"It is a wide ranging text covering such areas as hybrid systems, constrained control, LPV systems, nonlinear discrete-time systems and many others. ... This is a valuable contribution to the subject and is suitable for researchers and engineers involved in all aspects of nonlinear MPC design and implementation." (ACTC applied control technology consortium, actc-control.com, March, 2016)

Spis treści

Foundations and History of NMPC.- Nonlinear Model Predictive Control: An Introductory Review.- Theoretical Aspects of NMPC.- Hybrid MPC: Open-Minded but Not Easily Swayed.- Conditions for MPC Based Stabilization of Sampled-Data Nonlinear Systems Via Discrete-Time Approximations.- A Computationally Efficient Scheduled Model Predictive Control Algorithm for Control of a Class of Constrained Nonlinear Systems.- The Potential of Interpolation for Simplifying Predictive Control and Application to LPV Systems.- Techniques for Uniting Lyapunov-Based and Model Predictive Control.- Discrete-Time Non-smooth Nonlinear MPC: Stability and Robustness.- Model Predictive Control for Nonlinear Sampled-Data Systems.- Sampled-Data Model Predictive Control for Nonlinear Time-Varying Systems: Stability and Robustness.- On the Computation of Robust Control Invariant Sets for Piecewise Affine Systems.- Nonlinear Predictive Control of Irregularly Sampled Data Systems Using Identified Observers.- Nonlinear Model Predictive Control: A Passivity-Based Approach.- Numerical Aspects of NMPC.- Numerical Methods for Efficient and Fast Nonlinear Model Predictive Control.- Computational Aspects of Approximate Explicit Nonlinear Model Predictive Control.- Towards the Design of Parametric Model Predictive Controllers for Non-linear Constrained Systems.- Interior-Point Algorithms for Nonlinear Model Predictive Control.- Hard Constraints for Prioritized Objective Nonlinear MPC.- A Nonlinear Model Predictive Control Framework as Free Software: Outlook and Progress Report.- Robustness, Robust Design, and Uncertainty.- Robustness and Robust Design of MPC for Nonlinear Discrete-Time Systems.- MPC for Stochastic Systems.- NMPC for Complex Stochastic Systems Using a Markov Chain Monte Carlo Approach.- On Disturbance Attenuation of Nonlinear Moving Horizon Control.- Chance Constrained Nonlinear Model Predictive Control.- Close-Loop Stochastic Dynamic Optimization Under Probabilistic Output-Constraints.- Interval Arithmetic in Robust Nonlinear MPC.- Optimal Online Control of Dynamical Systems Under Uncertainty.- State Estimation and Output Feedback.- State Estimation Analysed as Inverse Problem.- Minimum-Distance Receding-Horizon State Estimation for Switching Discrete-Time Linear Systems.- New Extended Kaiman Filter Algorithms for Stochastic Differential Algebraic Equations.- Industrial Perspective on NMPC.- NLMPC: A Platform for Optimal Control of Feed- or Product-Flexible Manufacturing.- Experiences with Nonlinear MPC in Polymer Manufacturing.- Integration of Advanced Model Based Control with Industrial IT.- Putting Nonlinear Model Predictive Control into Use.- NMPC and Process Control.- Integration of Economical Optimization and Control for Intentionally Transient Process Operation.- Controlling Distributed Hyperbolic Plants with Adaptive Nonlinear Model Predictive Control.- A Minimum-Time Optimal Recharging Controller for High Pressure Gas Storage Systems.- Robust NMPC for a Benchmark Fed-Batch Reactor with Runaway Conditions.- Real-Time Implementation of Nonlinear Model Predictive Control of Batch Processes in an Industrial Framework.- Non-linear Model Predictive Control of the Hashimoto Simulated Moving Bed Process.- Receding-Horizon Estimation and Control of Ball Mill Circuits.- Hybrid NMPC Control of a Sugar House.- Application of the NEPSAC Nonlinear Predictive Control Strategy to a Semiconductor Reactor.- Integrating Fault Diagnosis with Nonlinear Model Predictive Control.- NMPC for Fast Systems.- A Low Dimensional Contractive NMPC Scheme for Nonlinear Systems Stabilization: Theoretical Framework and Numerical Investigation on Relatively Fast Systems.- A New Real-Time Method for Nonlinear Model Predictive Control.- A Two-Time-Scale Control Scheme for Fast Unconstrained Systems.- Novel Applications of NMPC.- Receding Horizon Control for Free-Flight Path Optimization.- An Experimental Study of Stabilizing Receding Horizon Control of Visual Feedback System with Planar Manipulators.- Coordination of Networked Dynamical Systems.- Distributed NMPC, Obstacle Avoidance, and Path Planning.- Distributed Model Predictive Control of Large-Scale Systems.- Distributed MPC for Dynamic Supply Chain Management.- Robust Model Predictive Control for Obstacle Avoidance: Discrete Time Case.- Trajectory Control of Multiple Aircraft: An NMPC Approach.