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2010
IEEE

Model-on-Demand predictive control for nonlinear hybrid systems with application to adaptive behavioral interventions

10 years 7 months ago
Model-on-Demand predictive control for nonlinear hybrid systems with application to adaptive behavioral interventions
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm...
Naresh N. Nandola, Daniel E. Rivera
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Naresh N. Nandola, Daniel E. Rivera
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