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检索条件"任意字段=International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control"

共 47 条记录，以下是31-40 订阅

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Integrating fault diagnosis with nonlinear model predictive control

Integrating fault diagnosis with Nonlinear model predictive ...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Deshpande, Anjali Patwardhan, Sachin C. Narasimhan, Shankar Indian Inst Technol Syst & Control Engn Bombay 400076 Maharashtra India Indian Inst Technol Dept Chem Engn Bombay 400076 Maharashtra India Indian Inst Technol Dept Chem Engn Madras 600036 Tamil Nadu India

ISBN: (纸本)9783540726982

The abundance of batch processes and continuous processes with wide operating ranges has motivated the development of nonlinear MPC (NMPC) techniques, which employ nonlinear models for prediction. The prediction model is typically developed once in the beginning of implementation of an NMPC scheme. However, as time progresses, slow drifts in unmeasured disturbances and changes in process parameters can lead to significant mismatch in plant and model behavior. Also, NMPC schemes are typically developed under the assumption that sensors and actuators are free from faults. However, soft faults, such as biases in sensors or actuators, are frequently encountered in the process industry. In addition to this, some actuator(s) may fail during operation, which results in loss of degrees of freedom for control. Occurrences of such faults and failures can lead to a significant degradation in the closed loop performance of the NMPC. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Actuators

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Receding horizon control for free-flight path optimization

Receding horizon control for free-flight path optimization

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Hu, Xiao-Bing Chen, Wen-Hua Univ Sussex Dept Informat Brighton BN1 9RH E Sussex England Loughborough Univ Technol Dept Aero & Auto Engn Loughborough Leics England

ISBN: (纸本)9783540726982

This paper presents a Receding Horizon control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a dynamic Free-Flight (FF) environment. The motivation to introduce the concept of RHC is to improve the robust performance of solutions in a dynamic and uncertain environment, and also to satisfy the restrictive time limit in the real-time optimization of this complicated air traffic control problem. Compared with existing algorithms, the new algorithm proves more efficient and promising for practical applications.

关键词： Free flight

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Hybrid MPC: Open-minded but not easily swayed

Hybrid MPC: Open-minded but not easily swayed

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Tuna, S. Emre Sanfelice, Ricardo G. Messina, Michael J. Teel, Andrew R. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9783540726982

The robustness of asymptotic stability with respect to measurement noise for discrete-time feedback control systems is discussed. It is observed that, when attempting to achieve obstacle avoidance or regulation to a disconnected set of points for a continuous-time system using sample and hold state feedback, the noise robustness margin necessarily vanishes with the sampling period. With this in mind, we propose two modifications to standard model predictive control (MPC) to enhance robustness to measurement noise. The modifications involve the addition of dynamical states that make large jumps. Thus, they have a hybrid flavor. The proposed algorithms are well suited for the situation where one wants to use a control algorithm that responds quickly to large changes in operating conditions and is not easily confused by moderately large measurement noise and similar disturbances.

关键词： model predictive control

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The potential of interpolation for simplifying predictive control and application to LPV systems

The potential of interpolation for simplifying predictive co...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Rossiter, John Anthony Pluymers, Bert De Moor, Bart Univ Sheffield Dept Automat Control & Syst Engn Mappin St Sheffield S1 3JD S Yorkshire England Katholieke Univ Leuven ESAT SCD SISTA Dept Elect Engn B-3001 Heverlee Belgium

ISBN: (纸本)9783540726982

This paper first introduces several interpolation schemes, which have been derived for the linear time invariant case, but with an underlying objective 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 development.

关键词： Interpolation

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NMPC for complex stochastic systems using a Markov chain Monte Carlo approach

NMPC for complex stochastic systems using a Markov chain Mon...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Maciejowski, Jan M. Visintini, Andrea Lecchini Lygeros, John Univ Cambridge Dept Engn Cambridge CB2 1PZ England Univ Leicester Dept Engn Leicester LE1 7RH Leics England ETH Automat Control Lab CH-8092 Zurich Switzerland

ISBN: (纸本)9783540726982

Markov chain Monte Carlo methods can be used to make optimal decisions in very complex situations in which stochastic effects are prominent. We argue that these methods can be viewed as providing a class of nonlinear MPC methods. We discuss decision taking by maximising expected utility, and give an extension which allows constraints to be respected. We give a brief account of an application to air traffic control, and point out some other problem areas which appear to be very amenable to solution by the same approach.

关键词： Stochastic systems

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Discrete-time non-smooth nonlinear MPC: Stability and robustness

Discrete-time non-smooth nonlinear MPC: Stability and robust...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Lazar, M. Heemels, W. P. M. H. Bemporad, A. Weiland, S. Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands Embedded Syst Inst Eindhoven Netherlands Univ Siena I-53100 Siena Italy

ISBN: (纸本)9783540726982

This paper considers discrete-time nonlinear, possibly discontinuous, systems in closed-loop with model predictive controllers (MPC). The aim of the paper is to provide a priori sufficient conditions for asymptotic stability in the Lyapunov sense and input-to-state stability (ISS), while allowing for both the system dynamics and the value function of the MPC cost to be discontinuous functions of the state. The motivation for this work lies in the recent development of MPC for hybrid systems, which are inherently discontinuous and nonlinear. For a particular class of discontinuous piecewise affine systems, a new MPC set-up based on infinity norms is proposed, which is proven to be ISS to bounded additive disturbances. This ISS result does not require continuity of the system dynamics nor of the MPC value function.

关键词： Asymptotic stability

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A two-time-scale control scheme for fast unconstrained systems

A two-time-scale control scheme for fast unconstrained syste...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Gros, Sebastien Buccieri, Davide Mullhaupt, Philippe Bonvin, Dominique Ecole Polytech Fed Lausanne Lab Automat CH-1015 Lausanne Switzerland

ISBN: (纸本)9783540726982

model predictive control (MPC) is a very effective approach to control nonlinear systems, especially when the systems are high dimensional and/or constrained. MPC formulates the problem of input trajectory generation as an optimization problem. However, due to model mismatch and disturbances, frequent re-calculation of the trajectories is typically called for. This paper proposes a two-time-scale control scheme that uses less frequent repeated trajectory generation in a slow loop and time-varying linear feedback in a faster loop. Since the fast loop reduces considerably the effect of uncertainty, trajectory generation can be done much less frequently. The problem of trajectory generation can be treated using either optimization-based MPC or flatness-based system inversion. As proposed, the method cannot handle hard constraints. Both MPC and the two-time-scale control scheme are tested via the simulation of a flying robotic structure. It is seen that the MPC scheme is too slow to be considered for real-time implementation on a fast system. In contrast, the two-time-scale control scheme is fast, effective and robust.

关键词： model predictive control

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Robust model predictive control for obstacle avoidance: Discrete time case

Robust model predictive control for obstacle avoidance: Disc...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Rakovic, Sasa V. Mayne, David Q. Univ London Imperial Coll Sci Technol & Med London SW7 2BT England

ISBN: (纸本)9783540726982

The importance of the obstacle avoidance problem is stressed in [4]. Computation of reachability sets for the obstacle avoidance problem is addressed, for continuous-time systems in [4, 5] and for discrete-time systems in [12];further results appear in, for instance [2, 17, 18]. The obstacle avoidance problem is inherently non-convex. Most existing results are developed for the deterministic case when external disturbances are not present. The main purpose of this paper is to demonstrate that the obstacle avoidance problem in the discrete time setup has considerable structure even when disturbances are present. We extend the robust model predictive schemes using tubes (sequences of sets of states) [9, 11, 14] to address the robust obstacle avoidance problem and provide a mixed integer programming algorithm for robust control of constrained linear systems that are required to avoid specified obstacles. The resultant robust optimal control problem that is solved on-line has marginally increased complexity compared with that required for model predictive control for obstacle avoidance in the deterministic case. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Integer programming

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Integration of advanced model based control with industrial IT

Integration of advanced model based control with industrial ...

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Franke, Ruediger Doppelhamer, Jens ABB AG Power Generat Mannheim Germany ABB Corp Res Ladenburg Germany

ISBN: (纸本)9783540726982

Advanced model based control is a promising technology that can improve the productivity of industrial processes. In order to find its way into regular applications, advanced control must be integrated with the industrial control systems. Modern control systems, on the other hand, need to extend the reach of traditional automation systems - beyond control of the process - to also cover the increasing amount of information technology (IT) required to successfully operate industrial processes in today's business markets. The Industrial IT System 800xA from ABB provides a scalable solution that spans and integrates loop, unit, area, plant, and interplant controls. This paper introduces the 800xA and the underlying Aspect Object technology. It is shown how model knowledge and optimization solver technology are integrated into the 800xA framework. This way, advanced model based control solutions can be set up in an open and modularly structured way. New model and solver aspects can be combined with available aspects covering standard functionality like process connectivity, management of process data, trend&history data and application data, as well as operator graphics. A nonlinear model-based predictive controller (NMPC) for power plant start-up is treated as example. This paper discusses how NMPC can be integrated with a modern control system so that standard concepts are re-used for this advanced model based control concept.

关键词： model predictive control

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Distributed model predictive control of large-scale systems

Distributed model predictive control of large-scale systems

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international workshop on assessment and future directions of nonlinear model predictive control

作者： Venkat, Aswin N. Rawlings, James B. Wright, Stephen J. Shell Global Solut US Inc Westhollow Technol Ctr POB 4327 Hooston TX 77210 USA Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9783540726982

Completely centralized control of large, networked systems is impractical. Completely decentralized control of such systems, on the other hand, frequently results in unacceptable control performance. In this article, a distributed MPC framework with guaranteed feasibility and nominal stability properties is described. All iterates generated by the proposed distributed MPC algorithm are feasible and the distributed controller, defined by terminating the algorithm at any intermediate iterate, stabilizes the closed-loop system. The above two features allow the practitioner to terminate the distributed MPC algorithm at the end of the sampling interval, even if convergence is not attained. Further, the distributed MPC framework achieves optimal systemwide performance (centralized control) at convergence. Feasibility, stability and optimality properties for the described distributed MPC framework are established. Several examples are presented to demonstrate the efficacy of the proposed approach.

关键词： Decentralized control

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