Stability and Robustness of Nonlinear Receding Horizon Control

Nicolao, Giuseppe De; Magni, Lalo; Scattolini, Riccardo

doi:10.1007/978-3-0348-8407-5_1

Cited by 109 publications

(67 citation statements)

References 37 publications

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“…Many of the methods, their stability properties, performance and implementation are analyzed in the easily accessible paper by Nicolao et al [2000].…”

Section: Nonlinear Mpcmentioning

confidence: 99%

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Simon

2014

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Aircraft are dynamic systems that naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems are becoming increasingly important as the performance and complexity of the controlled systems is constantly increasing. It is especially important in the design of control systems for fighter aircraft. These require maximum control performance in order to have the upper hand in a dogfight or when they have to outmaneuver an enemy missile. Therefore pilots often maneuver the aircraft very close to the limit of what it is capable of, and an automatic system (called flight envelope protection system) against violating the restrictions is a necessity.In other application areas, nonlinear optimal control methods have been successfully used to solve this but in the aeronautical industry, these methods have not yet been established. One of the more popular methods that are well suited to handle constraints is Model Predictive Control (MPC) and it is used extensively in areas such as the process industry and the refinery industry. Model predictive control means in practice that the control system iteratively solves an advanced optimization problem based on a prediction of the aircraft's future movements in order to calculate the optimal control signal. The aircraft's operating limitations will then be constraints in the optimization problem.In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. In reference tracking model predictive control for linear systems we build on the dual mode formulation of MPC and our goal is to make minimal changes to this framework, in order to develop a reference tracking algorithm with guaranteed stability and low complexity suitable for implementation in real time safety critical systems.To reduce the computational burden of nonlinear model predictive control several methods to approximate the nonlinear constraints have been proposed in the literature, many working in an ad hoc fashion, resulting in conservatism, or worse, inability to guarantee recursive feasibility. Also several methods work in an iterative manner which can be quit time consuming making them inappropriate for fast real time applications. In this thesis we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence.v Populärvetenskaplig sammanfattningFlygplan är dynamiska system som naturligt innehåller en mängd begränsningar och olinjäriteter så som t.ex. max tillåten lastfaktor, anfallsvinkel och roderlägen. Att ta hänsyn till dessa begränsningar i designen av styrsystem blir allt viktiga...

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“…Many of the methods, their stability properties, performance and implementation are analyzed in the easily accessible paper by Nicolao et al [2000].…”

Section: Nonlinear Mpcmentioning

confidence: 99%

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Simon

2014

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“…Therefore stability results from model predictive control are applicable. Conditions for guaranteed stability of the closed loop system under nominal conditions usually require stabilizing constraints of the state variables at the end of the optimization horizon together with a suitable evaluation of the final state [15]. This is approximated by a quadratic penalty term with symmetric, positive definite weighting matrixQ which extends the original objective functional.…”

Section: Trajectory Generationmentioning

confidence: 99%

Trajectory Tracking for Boom Cranes Based on Nonlinear Control and Optimal Trajectory Generation

Arnold

Neupert

Sawodny

et al. 2007

2007 IEEE International Conference on Control Applications

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Bibliografische Information der Deutschen Bibliothek © Technische Universität Ilmenau (Thür.) 2007Diese Publikationen und alle in ihr enthaltenen Beiträge und Abbildungen sind urheberrechtlich geschützt. Mit Ausnahme der gesetzlich zugelassenen Fälle ist eine Verwertung ohne Einwilligung der Redaktion strafbar. PrefaceDear Participants,Confronted with the ever-increasing complexity of technical processes and the growing demands on their efficiency, security and flexibility, the scientific world needs to establish new methods of engineering design and new methods of systems operation. The factors likely to affect the design of the smart systems of the future will doubtless include the following:• As computational costs decrease, it will be possible to apply more complex algorithms, even in real time. These algorithms will take into account system nonlinearities or provide online optimisation of the system's performance.• New fields of application will be addressed. Interest is now being expressed, beyond that in "classical" technical systems and processes, in environmental systems or medical and bioengineering applications.• The boundaries between software and hardware design are being eroded. New design methods will include co-design of software and hardware and even of sensor and actuator components.• Automation will not only replace human operators but will assist, support and supervise humans so that their work is safe and even more effective.• Networked systems or swarms will be crucial, requiring improvement of the communication within them and study of how their behaviour can be made globally consistent.• The issues of security and safety, not only during the operation of systems but also in the course of their design, will continue to increase in importance.The title "Computer Science meets Automation", borne by the 52 nd International Scientific Colloquium (IWK) at the Technische Universität Ilmenau, Germany, expresses the desire of scientists and engineers to rise to these challenges, cooperating closely on innovative methods in the two disciplines of computer science and automation.The IWK has a long tradition going back as far as 1953. In the years before 1989, a major function of the colloquium was to bring together scientists from both sides of the Iron Curtain. Naturally, bonds were also deepened between the countries from the East. Today, the objective of the colloquium is still to bring researchers together. They come from the eastern and western member states of the European Union, and, indeed, from all over the world. All who wish to share their ideas on the points where "Computer Science meets Automation" are addressed by this colloquium at the Technische Universität Ilmenau.All the University's Faculties have joined forces to ensure that nothing is left out. Control engineering, information science, cybernetics, communication technology and systems engineering -for all of these and their applications (ranging from biological systems to heavy engineering), the issues are being covered.Together...

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“…For example we mainly focus on NMPC for continuous time systems using sampled measurement information and do not go into details on discrete time NMPC strategies. For more self contained reviews on NMPC we refer to [3,17,22,69,80].…”

Section: Introductionmentioning

confidence: 99%

State and Output Feedback Nonlinear Model Predictive Control: An Overview

Findeisen

Imsland

Allgöwer

et al. 2003

European Journal of Control

301

185

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The purpose of this paper is twofold. In the first part we give a review on the current state of nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of predictive control we outline some of the theoretical, computational, and implementational aspects of this control strategy. Most of the theoretical developments in the area of NMPC are based on the assumption that the full state is available for measurement, an assumption that does not hold in the typical practical case. Thus, in the second part of this paper we focus on the output feedback problem in NMPC. After a brief overview on existing output feedback NMPC approaches we derive conditions that guarantee stability of the closed-loop if an NMPC state feedback controller is used together with a full state observer for the recovery of the system state.

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Stability and Robustness of Nonlinear Receding Horizon Control

Cited by 109 publications

References 37 publications

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Model Predictive Control in Flight Control Design : Stability and Reference Tracking

Trajectory Tracking for Boom Cranes Based on Nonlinear Control and Optimal Trajectory Generation

State and Output Feedback Nonlinear Model Predictive Control: An Overview

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