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Titlebook: Assessment and Future Directions of Nonlinear Model Predictive Control; Rolf Findeisen,Frank Allg?wer,Lorenz T. Biegler Book 2007 Springer

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發(fā)表于 2025-3-21 17:55:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Assessment and Future Directions of Nonlinear Model Predictive Control
影響因子2023Rolf Findeisen,Frank Allg?wer,Lorenz T. Biegler
視頻videohttp://file.papertrans.cn/164/163339/163339.mp4
發(fā)行地址Results of the international workshop entitled "Assesmentand Future Directions of Nonlinear Model Predictive Control” (NMPC′05), which was held in Freudenstadt-Lauterbad, Germany on August 26-30, 2005
學(xué)科分類Lecture Notes in Control and Information Sciences
圖書封面Titlebook: Assessment and Future Directions of Nonlinear Model Predictive Control;  Rolf Findeisen,Frank Allg?wer,Lorenz T. Biegler Book 2007 Springer
影響因子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.
Pindex Book 2007
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Conditions for MPC Based Stabilization of Sampled-Data Nonlinear Systems Via Discrete-Time Approximato be computed by the receding horizon control method based on discrete-time approximate models. Multi-rate — multistep control is considered and both measurement and computational delays are allowed. It is shown that the same family of controllers that stabilizes the approximate discrete-time model
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The Potential of Interpolation for Simplifying Predictive Control and Application to LPV Systemse of trading off performance for online computational simplicity. It is then shown how these can be extended to linear parameter varying systems, with a relatively small increase in the online computational requirements. Some illustrations are followed with a brief discussion on areas of potential d
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Techniques for Uniting Lyapunov-Based and Model Predictive Controltion level (Hybrid predictive control) and at the design level (Lyapunov-based predictive control) in a way that allows for an explicit characterization of the set of initial conditions starting from where closed-loop stability is guaranteed in the presence of constraints.
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Model Predictive Control for Nonlinear Sampled-Data Systemsaws. The given continuous-time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the ne
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Sampled-Data Model Predictive Control for Nonlinear Time-Varying Systems: Stability and Robustnessas well as the computation of the control laws, are carried out at discrete instants of time. This framework can address a very large class of systems, nonlinear, time-varying, and nonholonomic..As in many others sampled-data Model Predictive Control schemes, Barbalat’s lemma has an important role i
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