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...

• Type: Journal
• Year: 2010
• Where: CDC
• Authors: Naresh N. Nandola, Daniel E. Rivera
• Keywords: CDC 2010, Control Systems, Hybrid Systems, Nonlinear Hybrid, Nonlinear Hybrid Systems