Online Identification of Piecewise Affine Systems Using Integral Concurrent Learning

Du, Yingwei; Liu, Fangzhou; Qiu, Jianbin; Buss, Martin

doi:10.1109/tcsi.2021.3099828

Cited by 20 publications

(14 citation statements)

References 43 publications

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“…Here it is necessary to note the existence of improved concurrent‐learning‐based adaptation laws, 46,53 in which, together with the calculation of

{\hat{\Theta}}_j(t)

, the time instants

{t}_i^{+}

are detected and the current state

\kappa (t)

is identified. The disadvantages of such improved methods are the necessity to store and process off‐line large amount of data on

y(t)

\varphi (t)

and

{\hat{\Theta}}_j(t)

for different values of

\kappa (t)

.…”

Section: Discussionmentioning

confidence: 99%

Unknown piecewise constant parameters identification with exponential rate of convergence

Glushchenko

Lastochkin

2022

Adaptive Control & Signal

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Summary The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known methods, to make the computational burden lower, only one model to identify all switching states of the regression is used in the developed procedure with the following two‐fold contribution. First of all, we propose a new truly online estimation algorithm based on a well‐known DREM approach to detect switching time and preserve time alertness with adjustable detection delay. Second, despite the fact that a switching signal function is unknown, the adaptive law is derived that provides global exponential convergence of the regression parameters estimates to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches. The robustness of the proposed identification procedure to the influence of external disturbances is analytically proved. Its effectiveness is demonstrated via numerical experiments, in which both abstract regressions and a second‐order plant model are used.

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“…Here it is necessary to note the existence of improved concurrent‐learning‐based adaptation laws, 46,53 in which, together with the calculation of

{\hat{\Theta}}_j(t)

, the time instants

{t}_i^{+}

are detected and the current state

\kappa (t)

is identified. The disadvantages of such improved methods are the necessity to store and process off‐line large amount of data on

y(t)

\varphi (t)

and

{\hat{\Theta}}_j(t)

for different values of

\kappa (t)

.…”

Section: Discussionmentioning

confidence: 99%

Unknown piecewise constant parameters identification with exponential rate of convergence

Glushchenko

Lastochkin

2022

Adaptive Control & Signal

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“…To the best of our knowledge, direct CT identification of switched linear models has been addressed very recently only in (Goudjil et al, 2020;Kersting and Buss, 2019;Du et al, 2021). These approaches, however, rely on strong assumptions imposed on the system's signals.…”

Section: Paper Contributions and Related Workmentioning

confidence: 99%

“…In Kersting and Buss (2019), parameter identifiers and concurrent learning is proposed based on the assumptions that discrete mode sequence as well as continuous states are measured. Integral concurrent learning is proposed in Du et al (2021) relaxing the assumption of known switching sequence. However, the continuous state is assumed to be measured.…”

Section: Paper Contributions and Related Workmentioning

confidence: 99%

“…To position our work w.r.t. these contributions, we do not impose any of the aforementioned assumptions required in (Goudjil et al, 2020;Kersting and Buss, 2019;Du et al, 2021), which are quite restrictive in practice. In particular, in our contribution the input signals used to excite the system are not restricted to sinusoidal inputs.…”

Section: Paper Contributions and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Direct identification of continuous-time linear switched state-space models

Mejari¹,

Piga²

2022

Preprint

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This paper presents an algorithm for direct continuous-time (CT) identification of linear switched state-space (LSS) models. The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block. This architecture is used to approximate the continuous-time state map of a switched system. A properly constructed objective criterion is proposed based on the integral architecture in order to estimate the unknown parameters and signals of the LSS model. A coordinate descent algorithm is employed to optimize this objective, which alternates between computing the unknown model matrices, switching sequence and estimating the state variables. The effectiveness of the proposed algorithm is shown via a simulation case study.

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“…PWA-model can efficiently solve several challenging problems in system analysis, prediction and simulation modeling, and controller design. [5][6][7][8] Globally, the PWA model can approximate nonlinear systems, and locally, the mapping from the regression to the output space allows the transfer of linear theory directly to nonlinear systems. As an alternative, neural-fuzzy models were also researched in recent years.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

An optimized excitation signal design for identification of PWA model and application to automotive throttles

Ren

2022

Measurement and Control

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In the automotive application for Electronic Control Units (ECU), dynamic models with high-precision to simulate mechatronic actuators are required for various analysis and design tasks like Hardware-in-the-Loop (HiL) simulation. It is known that the modeling of systems with significant friction effects is not accessible. The piecewise-affine (PWA) model has recently attracted considerable interest due to its high approximation capability for modeling systems with friction. Because the model quality depends intensely on the test signal design, a model-free optimized excitation signal design for identifying PWA models is presented in this paper. The presented method is demonstrated with an automotive throttle, and a PWA model is identified with a four-step identification approach. The result shows that the optimization method can achieve a better model quality, which is sufficient for the HiL simulation.

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Online Identification of Piecewise Affine Systems Using Integral Concurrent Learning

Cited by 20 publications

References 43 publications

Unknown piecewise constant parameters identification with exponential rate of convergence

Unknown piecewise constant parameters identification with exponential rate of convergence

Direct identification of continuous-time linear switched state-space models

An optimized excitation signal design for identification of PWA model and application to automotive throttles

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