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Data-driven modeling and feedback control play a vital role in several application areas ranging from robotics, control theory, manufacturing to management of assets, financial portfolios and supply chains. Many such problems in one way or another are rela ...
Multicomponent reactions provide efficient means to access molecular complexity. Herein, we report a copper-catalyzed three-component reaction of diazo compounds, alcohols and ethynyl benziodoxole (EBX) reagents for the synthesis of propargyl ethers. Exten ...
Optimization of industrial processes aims at minimizing operating cost or maximizing economic profit while respecting plant constraints. In process industry, real-time optimization (RTO) is often considered to ensure optimal plant operation and constraint ...
We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while ...
This paper addresses the steady-state optimization of continuous processes in the presence of uncertainty in the form of unknown or time-varying model parameters, structural plant-model mismatch, and disturbances. To address these issues, we assume that ce ...
In this paper, a new data-driven method for designing robust controllers is proposed for systemswith sector-bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex cons ...
A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can b ...
For lumped homogeneous reaction systems, this paper presents a kinetic model identification scheme that provides maximum-likelihood parameter estimates and guarantees convergence to global optimality. The use of the extent-based incremental approach allows ...
The problem of allocating the closed-loop poles of linear systems in specific regions of the complex plane defined by discrete time-domain requirements is addressed. The resulting non-convex set is inner-approximated by a convex region described with linea ...
We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...