A stabilizing model-based predictive control algorithm for nonlinear systems

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

doi:10.1016/s0005-1098(01)00083-8

Cited by 323 publications

(171 citation statements)

References 14 publications

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“…Consider a continuous stirred tank reactor (CSTR) in which an irreversible exothermic reaction A −→ B occurs, which has been studied in [19,23,28]. At the feeding side the fresh feed of pure A is mixed with a recycle stream of unreacted A with recycle flow rate (1 − )q.…”

Section: The Cstr Examplementioning

confidence: 99%

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A synthesis approach for output feedback robust model predictive control based-on input–output model

Ding

Zou

2014

Journal of Process Control

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Section: The Cstr Examplementioning

confidence: 99%

“…The objective is to regulate C A and T by manipulating T c . The nominal operating conditions, which correspond to the equilibrium C [19,23,28].…”

Section: The Cstr Examplementioning

confidence: 99%

A synthesis approach for output feedback robust model predictive control based-on input–output model

Ding

Zou

2014

Journal of Process Control

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“…In spite of the inapplicability of infinite predictive horizon in real plants, a useful proposition originated from it makes great senses during the development of NMPC theory, i.e., a long enough predictive horizon can guarantee the closed loop stability for most systems (Costa & do Val, 2003;Primbs & Nevistic, 2000). Many existing NMPC algorithm is on the basis of this result, such as Chen & Allgower (1998), Magni et al (2001). Although long predictive horizon scheme is convenient to be realized, the difficulty to obtain the corresponding threshold value makes this scheme improper in many plants, especially in systems with complicated structure.…”

Section: Introductionmentioning

confidence: 99%

A New Kind of Nonlinear Model Predictive Control Algorithm Enhanced by Control Lyapunov Functions

He¹,

Jianda²

2010

Model Predictive Control

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“…One popular approach proposed by, e.g., Chen and Allgöwer [1998], Michalska and Mayne [1993] and Magni et al [2001], is to use a linear controller that is stabilizing the linearization of the system around the origin, to define terminal state constraint and cost. Another approach found in Primbs et al [1999] and Jadbabaie et al [2001] uses the theory of Control Lyapunov Functions to define an appropriate terminal cost function.…”

Section: Nonlinear Mpcmentioning

confidence: 99%

Fighter Aircraft Maneuver Limiting Using MPC : Theory and Application

Simon¹

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Flight control design for modern fighter aircraft is a challenging task. Aircraft are dynamical systems, which 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 is becoming increasingly important as the performance and complexity of the aircraft is constantly increasing.The aeronautical industry has traditionally applied feedforward, anti-windup or similar techniques and different ad hoc engineering solutions to handle constraints on the aircraft. However these approaches often rely on engineering experience and insight rather than a theoretical foundation, and can often require a tremendous amount of time to tune.In this thesis we investigate model predictive control as an alternative design tool to handle the constraints that arises in the flight control design.We derive a simple reference tracking MPC algorithm for linear systems that build on the dual mode formulation 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 we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefit of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence.An alternative to deriving MPC algorithms with guaranteed stability properties is to analyze the closed loop stability, post design. Here we focus on deriving a tool based on Mixed Integer Linear Programming for analysis of the closed loop stability and robust stability of linear systems controlled with MPC controllers.To test the performance of model predictive control for a real world example we design and implement a standard MPC controller in the development simulator for the JAS 39 Gripen aircraft at Saab Aeronautics. This part of the thesis focuses on practical and tuning aspects of designing MPC controllers for fighter aircraft. Finally we have compared the MPC design with an alternative approach to maneuver limiting using a command governor. v Populärvetenskaplig sammanfattningStyrsystem i moderna flygplan ska sätta stopp om piloten gör en hastig manöver som äventyrar säkerheten. Speciellt viktigt är det i jaktflygplan där piloten kan tvingas manövrera flygplanet precis på gränsen för vad konstruktionen klarar. Reglermetoder från processindustrin anpassas nu för att passa flyget.En av de viktigaste faktorerna när man konstruerar ett flygplan är att flygplanet ska vara enkelt och säkert att flyga. Därför är det av yttersta vikt att man konstruerar flygplanens styrsystem så att piloten inte kan sätta flygplanet i en sådan situation att säkerheten äventyras. En sådan situation kan vara att piloten styr flygplanet så att det tappar sin lyftkraft och därmed sin förmåga att flyga eller också att kraftig turbulens u...

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A stabilizing model-based predictive control algorithm for nonlinear systems

Cited by 323 publications

References 14 publications

A synthesis approach for output feedback robust model predictive control based-on input–output model

A synthesis approach for output feedback robust model predictive control based-on input–output model

A New Kind of Nonlinear Model Predictive Control Algorithm Enhanced by Control Lyapunov Functions

Fighter Aircraft Maneuver Limiting Using MPC : Theory and Application

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