Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

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

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 …

The precise control of the spatiotemporal process in a roller kiln is crucial in the production
of Ni–Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely …

Abstract In a previous work [20], an economic model predictive control (EMPC) system for
parabolic partial differential equation (PDE) systems was proposed. Through operating the …

Modeling high-spatial dimensional (high-D) distributed parameter systems (DPSs) is very
difficult because of the spatially distributed characteristic and complex spatiotemporal …

The local dynamic mode decomposition with control (LDMDc) technique combines the
concept of unsupervised learning and the DMDc technique to extract the relevant local …

Modeling of the parabolic distributed parameter system (DPS) with the Karhunen-Loéve (KL)
method under sparse sensing will become very difficult because the information from the …

We focus on output feedback control of distributed processes whose infinite dimensional
representation in appropriate Hilbert subspaces can be decomposed to finite dimensional …

For many real complicated industrial processes, the accurate system model is often
unavailable. In this paper, we consider the partially unknown spatially distributed processes …