Model Predictive Control Of Nonlinear Parameter Varying Systems Via Receding Horizon Control Lyapunov Functions
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Model Predictive Control of Nonlinear Parameter Varying Systems Via Receding Horizon Control Lyapunov Functions
The problem of rendering the origin an asymptotically stable equilibrium point of a nonlinear system while, at the same time, optimizing some measure of performance has been the object of much attention in the past few years. in contrast to the case of linear systems where several optimal synthesis techniques (such as H infinity, H2 and l(exp 1) are well established, their nonlinear counterparts are just starting to emerge. Moreover, in most cases these tools lead to partial differential equations that are difficult to solve. In this chapter we propose a suboptimal regulator for nonlinear parameter varying, control affine systems based upon the combination of model predictive and control Lyapunov function techniques. The main result of the chapter shows that this controller is nearly optimal provided that a certain finite horizon problem can be solved on-line. Additional results include: (a) sufficient conditions guaranteeing closed loop stability even in cases where there is not enough computational power available to solve this optimization on-line; and (b) an analysis of the suboptimality level of the proposed method.
Assessment and Future Directions of Nonlinear Model Predictive Control
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.
Nonlinear Model Predictive Control
Author: Lalo Magni
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-05-25
Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together the contributions of a diverse group of internationally well recognized researchers and industrial practitioners, to critically assess the current status of the NMPC field and to discuss future directions and needs. The book consists of selected papers presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that took place from September 5 to 9, 2008, in Pavia, Italy.