Control of MIMO nonlinear systems: A data-driven model inversion approach

Novara, Carlo; Milanese, M.

doi:10.1016/j.automatica.2018.12.026

Cited by 14 publications

(3 citation statements)

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“…In this example, the models identified using our methods resulted to be significantly more accurate than other models obtained using a standard identification technique, demonstrating the potential of the proposed identification approach. Future research activities will regard the derivation of prediction models suitable for the data-driven NMPC techniques of [24,25] and the application to problems of practical interest.…”

Section: Discussionmentioning

confidence: 99%

“…NMPC is a widely used technique for controlling complex nonlinear plants, see, e.g., [6,14,11]. Data-driven versions of this technique can be found in [27,30,16,24,25]. NMPC is based on two main operations: (i ) multi-step prediction of the plant behavior, and (ii ) synthesis of a control law via on-line optimization, based on the predicted behavior.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear system identification in Sobolev spaces

Novara¹,

Nicolì²,

Calafiore³

2019

Preprint

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We consider the problem of deriving from experimental data an approximation of an unknown function, whose derivatives also approximate the unknown function derivatives. Solving this problem is useful, for instance, in the context of nonlinear system identification for obtaining models that are more accurate and reliable than the traditional ones, based on plain function approximation. Indeed, models identified by accounting for the derivatives can provide a better performance in several tasks, such as multi-step prediction, simulation, Nonlinear Model Predictive Control, and control design in general. In this paper, we propose a novel approach based on convex optimization, allowing us to solve the aforementioned identification problem. We develop an optimality analysis, showing that models derived using this approach enjoy suitable optimality properties in Sobolev spaces. The optimality analysis also leads to the derivation of tight uncertainty bounds on the unknown function and its derivatives. We demonstrate the effectiveness of the approach with two numerical examples. The first one is concerned with approximation of an univariate function, the second one with multi-step prediction of the Chua chaotic circuit.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear system identification in Sobolev spaces

Novara¹,

Nicolì²,

Calafiore³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other works in the literature focus on learning the control law from data as in [14, 15] or on identifying the process dynamics [16]. In [17], a data‐driven control design technique which is based on the on‐line inversion of the model and copes with MIMO non‐linear system is presented. Other works, as in [18], focus on achieving high tracking performance through learning for unknown LTI systems subject to unknown disturbances.…”

Section: Introductionmentioning

confidence: 99%