Adaptive arrival cost update for improving Moving Horizon Estimation performance

Sánchez, G.; Murillo, Marina Hebe; Giovanini, Leonardo

doi:10.1016/j.isatra.2017.02.012

Cited by 24 publications

(22 citation statements)

References 27 publications

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“…whose parameters

P_{k - N | k}^{- 1}

and

{\bar{x}}_{k - N}

are recursively updated using the information available at time k [18]. This way of defining

Γ_{k - N | k}

avoids the introduction of artificial cycling in the estimation process (see [19]).…”

Section: Preliminaries and Setupmentioning

confidence: 99%

“…The updating mechanism (8) provides adaptation capabilities that allow the estimator to incorporate the relevant information available in

y_{false[0, k false]}

and follow changes in the system (see Figs. 3 and 4 of [18]).…”

Section: Preliminaries and Setupmentioning

confidence: 99%

“…Remark 1 The sequence

bold-italicP : = (}{, P_{0 | 0}, \dots, P_{0 | N - 1}, P_{k - N | k})

\forall k \in Z_{\geq 0}

is positive definite, it is decreasing in norm and it is bounded. The proof of these properties follows similar steps as in [18]. …”

Section: Preliminaries and Setupmentioning

confidence: 99%

“…The interest in such estimation methods stems from the possibility of dealing with a limited amount of data and the ability to incorporate constraints. In recent years, both theoretical properties of several MHE schemes, including efficient computational methods, have been studied (see [13–18]).…”

Section: Introductionmentioning

confidence: 99%

“…This paper introduces the RGAS and convergence analysis for an MHE estimator based on adaptive arrival cost proposed in [18] for the practical case of non‐linear detectable systems subject to bounded disturbances. This formulation allows us to overcome several drawbacks in the existing literature, obtaining improved estimates for the disturbance gains and the minimal estimation horizon and providing the stability proof for both FIE and MHE.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

Deniz¹,

Murillo²,

Sánchez³

et al. 2020

IET Control Theory & Appl

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“…whose parameters

P_{k - N | k}^{- 1}

and

{\bar{x}}_{k - N}

are recursively updated using the information available at time k [18]. This way of defining

Γ_{k - N | k}

avoids the introduction of artificial cycling in the estimation process (see [19]).…”

Section: Preliminaries and Setupmentioning

confidence: 99%

“…The updating mechanism (8) provides adaptation capabilities that allow the estimator to incorporate the relevant information available in

y_{false[0, k false]}

and follow changes in the system (see Figs. 3 and 4 of [18]).…”

Section: Preliminaries and Setupmentioning

confidence: 99%

“…Remark 1 The sequence

bold-italicP : = (}{, P_{0 | 0}, \dots, P_{0 | N - 1}, P_{k - N | k})

\forall k \in Z_{\geq 0}

is positive definite, it is decreasing in norm and it is bounded. The proof of these properties follows similar steps as in [18]. …”

Section: Preliminaries and Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

Deniz¹,

Murillo²,

Sánchez³

et al. 2020

IET Control Theory & Appl

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Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

Zhang

Jiang

et al. 2022

Asian Journal of Control

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In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.

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Simultaneous moving horizon estimation and control for nonlinear systems subject to bounded disturbances

Deniz

Sánchez

Murillo

et al. 2023

Intl J Robust & Nonlinear

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In this work, we address the output-feedback control problem for nonlinear systems under bounded disturbances using a moving horizon approach. The controller is posed as an optimisation-based problem that simultaneously estimates the state trajectory and computes future control inputs. It minimizes a criterion that involves finite backward and forward horizons with respect to the unknown initial state, measurement noises and control input variables.The main novelty of this work relies on linking the lengths of the forward and backward windows with the closed-loop stability, assuming detectability and decoding sufficient conditions to assure system stabilizability. It leads to a formulation that does not require to be a Control Lyapunov Function for the terminal cost of the controller. Simulation examples are carried out to compare the performance of solving simultaneously and independently the estimation and control problems. Furthermore, the examples show how the controller influences the length of the estimation window through its gain.

show abstract

Adaptive arrival cost update for improving Moving Horizon Estimation performance

Cited by 24 publications

References 27 publications

Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

Simultaneous moving horizon estimation and control for nonlinear systems subject to bounded disturbances

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