Moving horizon observers and observer-based control

Michalska, H.; Mayne, D. Q.

doi:10.1109/9.388677

Cited by 309 publications

(172 citation statements)

References 14 publications

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“…However no significant progress with respect to the output feedback case has been made. The existing solutions are either of local nature (Scokaert et al, 1997;Magni et al, 1998) or difficult to implement (Michalska and Mayne, 1995). In this paper we have outlined, that using results from (Atassi and Khalil, 1999), "semi-global" stability results on a bounded region of attraction and recovery of performance can be achieved utilizing NMPC and a suited high gain observer for a class of systems.…”

Section: Discussionmentioning

confidence: 99%

“…In this case, little can be said about the stability of the closed loop, since there exists no universal separation principle for nonlinear systems as it does for linear systems. In (Michalska and Mayne, 1995) a moving horizon observer is designed, that together with the NMPC scheme proposed in (Michalska and Mayne, 1993) is shown to lead to (semi-global) closed loop stability if no model-plant mismatch and disturbances are present. In (Magni et al, 1998), see also (Scokaert et al, 1997), local asymptotic stability for observer based discretetime nonlinear MPC is obtained for "weakly detectable" systems.…”

Section: Introductionmentioning

confidence: 99%

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On output feedback nonlinear model predictive control using high gain observers for a class of systems

Imsland

Findeisen

Bullinger³

et al. 2001

IFAC Proceedings Volumes

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In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarantee local stability. Here we consider the combination of stabilizing instantaneous NMPC schemes with high gain observers. For a special MIMO system class we show that the closed loop is asymptotically stable, and that the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme are recovered for a high gain observer with large enough gain and thus leading to semi-global/non-local results.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On output feedback nonlinear model predictive control using high gain observers for a class of systems

Imsland

Findeisen

Bullinger³

et al. 2001

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…The solution to this constrained problem is [17] When this value for K, is used in place of K, in (43), the result is the constrained state estimate…”

Section: Gain Projectionmentioning

confidence: 99%

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Simon

2010

IET Control Theory &Amp; Applications

762

468

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The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

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“…There exists several methods achieving finite-time convergence, e.g. sliding mode observers (see [15,16]), moving horizon observer (see [17]), but some of these are not continuous like sliding mode observers. We consider here continuous finite-time observer.…”

Section: • Kazantzis and Kravaris Observer Which Uses Thementioning

confidence: 99%