An optimizing design strategy for multiple model adaptive estimation and control

Sheldon, Stuart; Maybeck, P.S.

doi:10.1109/cdc.1990.203479

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…For the general case (S>>M), the model-set design methods proposed in [17] and [19] can be adopted with the prior information of the distribution of mode. With the bounds of each parameter, the model-set design approach in [18] only fits for linear systems. Then for the nonlinear system such as the SBRV, a new approach is needed for the model-set design.…”

Section: Estimator Designmentioning

confidence: 99%

Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

Liang

Han

2011

Sci. China Inf. Sci.

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Multiple-model approach is one of the main streams for hybrid estimation. The difficulty of this approach to estimate the hybrid state of the semi-ballistic reentry vehicle (SBRV) is model-set design. This paper proposes a quasi-Monte Carlo model set that can ensure the estimator near-optimal in the sense of minimum mean square error (MMSE). The SBRV has a high nonlinearity and its mode is spanned by multiple parameters with known bounds. The design methods and characteristics of the quasi-Monte Carlo model set are given. The proposed model set has a higher accuracy than the model-set generated by the Monte Carlo method. The theoretical analysis and simulation results show the effectiveness and reasonability of the newly designed model set.Keywords semi-ballistic reentry vehicle, hybrid estimation, model-set design, Monte Carlo method, quasiMonte Carlo method CitationLiang Y Q, Han C Z. Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle.

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Section: Estimator Designmentioning

confidence: 99%

Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

Liang

Han

2011

Sci. China Inf. Sci.

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“…In order to apply the MM method to problems with uncertain parameter s over space S, two important questions are: 1) which quantity is best selected as the estimatee (i.e., the quantity to be estimated) and 2) how to quantize the parameter space S. The following general guideline was presented in [194] for estimatee selection: If the ultimate goal is to estimate a parameter s, which is related to another parameter p nonlinearly, then a model set fs 1 , :::, s M g in the space of s is superior to a model set fp 1 , :::, p M g in the space of p even if p has a better physical interpretation. For the second question, a procedure to determine the choice of the quantization points M = fm (1) , :::, m (M) g was presented in [311], given the number of quantization points M.…”

Section: A Model-set Designmentioning

confidence: 99%

“…The resultant choice is optimal in the sense of having the minimum average weighted MSE for the true mode over the set S. In the Gaussian case, this vector minimization problem can be solved numerically in a straightforward fashion. An example was given in [311] that demonstrates its superiority to several heuristic choices, including the simple, popular uniform quantization scheme.…”

Section: A Model-set Designmentioning

confidence: 99%

Survey of maneuvering target tracking. part v: multiple-model methods

Jilkov

2005

IEEE Trans. Aerosp. Electron. Syst.

854

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This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods-the use of multiple models (and filters) simultaneously-which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

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“…This key element in multiple model control determines the title of this kind of adaptive control technique. To the best of our knowledge, this logic can be divided into three main categories: The logic relies upon stochastic concepts and is a probabilistic weighting of control actions which leads to the title ‘Multiple Model Adaptive Control (MMAC)’ 1–3; it will be discussed and evaluated in much more detail in the sequel. The logic relies upon deterministic concepts and is a supervisory switching logic which results in the title ‘Switching Supervisory Multiple Model Adaptive Control (SMMAC)’. In the SMMAC techniques, the output estimation errors of local linear models are used to select and place the best possible linear controller into the feedback loop 4–7.…”

Section: Introductionmentioning

confidence: 99%

Robust multiple model adaptive control: Modified using ν‐gap metric

Mahdianfar

Ozgoli

Momeni

2010

Intl J Robust & Nonlinear

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SUMMARYA new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the -gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open-loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the -synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on -gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case-study is a non-minimum-phase (NMP) system, which has an uncertain NMP zero; the second case-study is a mass-spring-dashpot system that has three uncertain real parameters. In the first case-study, five robust controller design methods (H 2 , H ∞ , QFT, H ∞ loop-shaping and -synthesis) are implemented and it is shown via extensive simulations that RMMAC/ /QFT method improves disturbance-rejection, when compared with the classic RMMAC. In the second case-study, two robust controller design methods (QFT and mixed -synthesis) are applied and it is shown that the RMMAC/ /QFT method improves disturbance-rejection, when compared with RMMAC/ /mixed-.

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An optimizing design strategy for multiple model adaptive estimation and control

Cited by 14 publications

References 6 publications

Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

Hybrid state estimation and model-set design of invariable-structure semi-ballistic reentry vehicle

Survey of maneuvering target tracking. part v: multiple-model methods

Robust multiple model adaptive control: Modified using ν‐gap metric

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