Hybrid systems have acquired deep attention in research as many systems in industries and
laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems …

This article presents a machine learning‐based model predictive control (MPC) scheme for
stabilization of hybrid dynamical systems, for which the evolution of states exhibits both …

In this work, we develop a state estimation scheme for nonlinear autonomous hybrid
systems, which are subjected to stochastic state disturbances and measurement noise …

The domain of applicability (DA) of a data-driven model is limited by its training data.
Consequently, the DA of a hybrid model which combines a first-principles model with a data …

This paper presents modeling and control of nonlinear hybrid systems using multiple
linearized models. Each linearized model is a local representation of all locations of the …

A hybrid model integrates a first‐principles model with a data‐driven model which predicts
certain unknown dynamics of the process, resulting in higher accuracy than first‐principles …

In this paper, a Model Predictive Control (MPC) method is considered to reduce the on-
stream computational complications of the nonlinear interconnected systems. Dynamic …

The fluid level control strategy is broadly used in various industrial applications, like the
boiler process, chemical process industry, nuclear power reactor, and distillation of …

The hybrid systems contain two different types: subsystems with continuous dynamic
behavior and subsystems with discrete dynamic behavior that not only coexist but also …

This paper addresses the control design of Hybrid Dynamic Systems (HDS) modeled by
Hybrid Petri Net (HPN) systems. The resolution of the problem is based on Model Predictive …