Structured Set Membership identification of nonlinear systems with application to vehicles with controlled suspension

Milanese, M.; Novara, Carlo

doi:10.1016/j.conengprac.2006.01.001

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…However, since accurately modeling the dynamics of a nonrigid airfoil is challenging, model-based control design may not perform satisfactorily on the real system. In this case, methods for identifying nonlinear systems [14], [15] can be applied to derive more accurate models.…”

Section: Model Identificationmentioning

confidence: 99%

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Power Kites for Wind Energy Generation [Applications of Control]

Canale¹,

Fagiano²,

Milanese³

2007

IEEE Control Syst.

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T he problems posed by electric energy generation from fossil sources include high costs due to large demand and limited resources, pollution and CO 2 production, and the geopolitics of producer countries. These problems can be overcome by alternative sources that are renewable, cheap, easily available, and sustainable. However, current renewable technologies have limitations. Indeed, even the most optimistic forecast on the diffusion of wind, photovoltaic, and biomass sources estimates no more than a 20% contribution to total energy production within the next 15-20 years.Excluding hydropower plants, wind turbines are currently the largest source of renewable energy [1]. Unfortunately, wind turbines require heavy towers, foundations, and huge blades, which impact the environment in terms of land usage and noise generated by blade rotation, and require massive investments with long-term amortization. Consequently, electric energy production costs are not yet competitive with thermal generators, despite recent increases in oil and gas prices.

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Section: Model Identificationmentioning

confidence: 99%

“…where T p = N p t, is the prediction horizon of N p steps, x(τ ) is the state predicted inside the prediction horizon according to (15) using W t (t) = 0 and x(t k ) = x(t k ), and the piecewise constant control input ũ(t) belonging to the sequence U = {ũ(t)}, t ∈ [t k , t k+T p ] is defined as …”

Section: Mpc For Kitegenmentioning

confidence: 99%

Power Kites for Wind Energy Generation [Applications of Control]

Canale¹,

Fagiano²,

Milanese³

2007

IEEE Control Syst.

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“…In order to check if better results could be obtained, the iterative identification algorithm of [35] has been applied. In this algorithm, the LTI system L is not assumed known but is identified at each step of the algorithm.…”

Section: Identification Examplementioning

confidence: 99%

Structured nonlinear system identification

Vincent

Poolla

Novara

2015

Control-Oriented Modelling and Identification: Theory and Practice

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To obtain an identified model from data, the system identification practitioner has to make an important choice: to specify the set of candidate models, or model structure. This choice can play an outsized role on the success or failure of the identification process. If the model structure is specified too restrictively, so that the true system is not represented, then the identified model will be biased. On the other hand, if the model structure is specified too generally, then the identified model can have a high variance, and a significant amount of data may be needed to reduce the sensitivity to measurement noise. Ideally, the practitioner should choose the model structure so that it encodes all the information that is known with high confidence. The structured nonlinear system identification approach is designed to give the practitioner a very flexible model structure that can easily be configured to be as restrictive or permissive as the a-priori information about the system warrants. In this chapter, a complete introduction to structured identification is developed, with examples relevant to many different real-world applications integrated throughout.

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Robust Hurwitz Stability and Performance Analysis of H-Infinity Controlled Forward-Velocity Dynamics of UAVs in Close Formation Flight Using Bounded Phase Conditions in a Kharitonov Framework

Johnson¹,

Dasgupta²

2014

J. Inst. Eng. India Ser. C

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Structured Set Membership identification of nonlinear systems with application to vehicles with controlled suspension

Cited by 9 publications

References 32 publications

Power Kites for Wind Energy Generation [Applications of Control]

Power Kites for Wind Energy Generation [Applications of Control]

Structured nonlinear system identification

Robust Hurwitz Stability and Performance Analysis of H-Infinity Controlled Forward-Velocity Dynamics of UAVs in Close Formation Flight Using Bounded Phase Conditions in a Kharitonov Framework

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