M
Manfred Morari
Researcher at University of Pennsylvania
Publications - 871
Citations - 74244
Manfred Morari is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Model predictive control & Optimal control. The author has an hindex of 112, co-authored 863 publications receiving 68435 citations. Previous affiliations of Manfred Morari include Georgia Institute of Technology & California Institute of Technology.
Papers
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Journal ArticleDOI
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
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The explicit linear quadratic regulator for constrained systems
TL;DR: A technique to compute the explicit state-feedback solution to both the finite and infinite horizon linear quadratic optimal control problem subject to state and input constraints is presented, and it is shown that this closed form solution is piecewise linear and continuous.
Journal ArticleDOI
Control of systems integrating logic, dynamics, and constraints
Alberto Bemporad,Manfred Morari +1 more
TL;DR: A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules.
Book
Robust process control
TL;DR: A state-of-the-art study of computerized control of chemical processes used in industry is presented in this article for chemical engineering and industrial chemistry students involved in learning the micro-macro design of chemical process systems.
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Robust constrained model predictive control using linear matrix inequalities
TL;DR: This paper presents a new approach for robust MPC synthesis that allows explicit incorporation of the description of plant uncertainty in the problem formulation, and shows that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants.