Efficient nonlinear programming (NLP) algorithms and modeling platforms have led to
powerful process optimization strategies. Nevertheless, these algorithms are challenged by …

Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to
describe the system dynamics of industrial spatially distributed processes (SDPs). In this …

Abstract The Hamilton–Jacobi–Bellman (HJB) equation can be solved to obtain optimal
closed-loop control policies for general nonlinear systems. As it is seldom possible to solve …

The data-driven H∞ control problem of nonlinear distributed parameter systems is
considered in this paper. An off-policy learning method is developed to learn the H∞ control …

Modeling the temperature distribution of a battery is critical to its safe operation. Data-based
modeling methods are computationally efficient, but require a large number of sensors …

This work focuses on predictive control of linear parabolic partial differential equations
(PDEs) with boundary control actuation subject to input and state constraints. Under the …

This work focuses on predictive control of linear parabolic partial differential equations
(PDEs) with state and control constraints. Initially, the PDE is written as an infinite …

Over the past three decades, pressure swing adsorption (PSA) processes have been widely
used as energy-efficient gas separation techniques, especially for high purity hydrogen …

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent
advances on adaptive algorithms and machine learning methods designed for nonlinear …

Developing reduced-order models for nonlinear parabolic partial differential equation (PDE)
systems with time-varying spatial domains remains a key challenge as the dominant spatial …