Fast Adaptive Observers for Battery Management Systems

Jenkins, Benjamin M.; Krupadanam, Ashish; Annaswamy, Anuradha M.

doi:10.1109/tcst.2019.2891234

Cited by 31 publications

(15 citation statements)

References 36 publications

(5 reference statements)

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“…The goal in either case is to determine conditions under which this convergence take place. These conditions are linked to properties defined as persistent excitation (PE) and uniform observability [128,129,66,130,131]. These PE properties are usually associated with the underlying regressor ω, and typically realized by choosing the exogenous signals such as r(t), the input into the reference model M appropriately, which the control designers have the freedom to select.…”

Section: Learning = Parameter Estimationmentioning

confidence: 99%

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A Historical Perspective of Adaptive Control and Learning

Annaswamy¹,

Fradkov²

2021

Preprint

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This article provides a historical perspective of the field of adaptive control over the past seven decades and its intersection with learning. A chronology of key events over this large time-span, problem statements that the field has focused on, and key solutions are presented. Fundamental results related to stability, robustness, and learning are sketched. A brief description of various applications of adaptive control reported over this period is included.

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Section: Learning = Parameter Estimationmentioning

confidence: 99%

“…Necessary and sufficient conditions for this convergence requires that the regressor ω be persistently exciting. Several results also exist in ensuring accelerated convergence of these estimates [162,163,131,164,159,165]) using matrix regressors, a time-varying learning rate for Γ, and dynamic regressor extension and mixing.…”

Section: Adaptive Observersmentioning

confidence: 99%

A Historical Perspective of Adaptive Control and Learning

Annaswamy¹,

Fradkov²

2021

Preprint

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“…With an objective to relax the PE condition, a matrix regressor (MR) based adaptive observer for SISO LTI plants is designed, [19][20][21][22] where the notion of strong persistent excitation (S-PE) is introduced to obtain online computation of degree of persistence and leads to faster convergence. Recently, adaptive control architectures are introduced where the properties of finite excitation 23,24 are used for the convergence of parameter estimation error to zero.…”

Section: Introductionmentioning

confidence: 99%

“…From(22) and(23) it can be proved that w 1 (t), w 2 (t) ∈  ∞ . Thus, from(21) it can be shown that u where Y f is the upper bound of ||Y f || Proof. Since Y (t), Y f (t), θ(t) ∈  ∞ and from(17) it can be established that 𝜇(t) is exponentially decaying, it can be shown from (15) that x(t) ∈  ∞ .…”

mentioning

confidence: 99%

Finite excitation based robust adaptive observer for MIMO LTI systems

Katiyar

Roy

Bhasin

2021

Adaptive Control & Signal

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The article addresses the problem of a robust adaptive observer design for multi-input multi-output linear time-invariant plants subjected to bounded disturbances in the system dynamics and the output relation. An adaptive observer design is proposed based on the finite excitation leading to a switched estimation strategy thus relaxing persistence of excitation (PE) condition. The novel finite persistence excitation (f-PE) condition introduced in this work is significantly milder than the PE condition in terms of excitation requirement and online verifiability. The switched estimator uses the finite excitation via input signal and ensures uniformly ultimately bounded stability of the estimation error dynamics. A multiple switching strategy, which continuously tracks the information richness (about the unknown parameters), is utilized to reduce the intermediate bounds and is proved to be instrumental in improving the result.

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“…The underlying structure in many of the adaptive identification and control problems consists of a linear regression relation between two dominant errors in the system [4], [13], [14]. Examples include adaptive observers [15]- [19] and certain classes of adaptive controllers [1]. The underlying algebraic relation is often leveraged in order to lead to a fast convergence through the introduction of a time-varying learning rate in the parameter estimation algorithm, which leads to the wellknown recursive least squares algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation in Adaptive Control of Time-Varying Systems Under a Range of Excitation Conditions

Gaudio,

Annaswamy,

Lavretsky

et al. 2019

Preprint

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This paper presents a new parameter estimation algorithm for the adaptive control of a class of time-varying plants. The main feature of this algorithm is a matrix of time-varying learning rates, which enables parameter estimation error trajectories to tend exponentially fast towards a compact set whenever excitation conditions are satisfied. This algorithm is employed in a large class of problems where unknown parameters are present and are time-varying. It is shown that this algorithm guarantees global boundedness of the state and parameter errors of the system, and avoids an often used filtering approach for constructing key regressor signals. In addition, intervals of time over which these errors tend exponentially fast toward a compact set are provided, both in the presence of finite and persistent excitation. A projection operator is used to ensure the boundedness of the learning rate matrix, as compared to a time-varying forgetting factor. Numerical simulations are provided to complement the theoretical analysis.

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Fast Adaptive Observers for Battery Management Systems

Cited by 31 publications

References 36 publications

A Historical Perspective of Adaptive Control and Learning

A Historical Perspective of Adaptive Control and Learning

Finite excitation based robust adaptive observer for MIMO LTI systems

Parameter Estimation in Adaptive Control of Time-Varying Systems Under a Range of Excitation Conditions

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