Research

Model Predictive Control

Design, analysis, and synthesis of linear and hybrid MPC controllers. In particular, stochastic Hybrid MPC, event-driven MPC, inverse design (controller matching), semi-automated calibration algorithms, stabilization via control Lyapunov functions.

Automotive Control

Application of advanced control strategies in Automotive control problems. Particular focus is on Model Predictive Control, stochastic control algorithms, Markov chains based modeling and control. The application of interests include engine control (idle speed, deceleration torque control), vehicle dynamics stabilization, hybrid vehicles power management, supply chain optimization, model-based catalyst control, human-machine interface.

Networked Control

Development of constrained control algorithms over network links. Stochastic network modeling, constraint enforcement against network induced delay and data losses. Smart actuation devices and sensor networks. Cooperative estimation and estimation over networks. Applications in automotive, industrial processes, and aerospace.

Hybrid Dynamical Systems

Equivalence of hybrid system models, stochastic hybrid MPC, stabilizing predictive control for hybrid systems. Even-driven hybrid control and hybrid Petri nets.

Optimization

Large scale and distributed optimization, mixed-integer programming for hybrid systems control, implementation of optimization tools, multiparametric programming and complexity reduction

Other Research Interests

Stochastic control, Markov chains, aerospace applications, reference governors, fuel cells, autonomous robots navigation.