2015 - Fellow of the Royal Academy of Engineering (UK)
2015 - Rufus Oldenburger Medal, The American Society of Mechanical Engineers
2011 - Richard E. Bellman Control Heritage Award
2008 - Fellow of the International Federation of Automatic Control (IFAC)
2005 - IEEE Control Systems Award “For pioneering contributions to the theory and application of robust process, model predictive, and hybrid systems control.”
2005 - IEEE Fellow For contributions to robust and model predictive control and control of hybrid systems.
1993 - Member of the National Academy of Engineering For analysis of the effects of design on process operability and the development of techniques for robust process control.
The scientist’s investigation covers issues in Control theory, Model predictive control, Mathematical optimization, Optimal control and Control engineering. Control theory is represented through his Control theory, Control system, Hybrid system, Robust control and Nonlinear system research. In his study, Direct torque control is inextricably linked to Power electronics, which falls within the broad field of Model predictive control.
His Mathematical optimization research is multidisciplinary, incorporating elements of Convex optimization, Piecewise linear function and Affine transformation. In his study, Bellman equation is strongly linked to Discrete time and continuous time, which falls under the umbrella field of Optimal control. His Internal model study in the realm of Control engineering interacts with subjects such as Control.
His main research concerns Control theory, Model predictive control, Mathematical optimization, Optimal control and Control engineering. His Control theory study is mostly concerned with Control theory, Nonlinear system, Control system, Robust control and Robustness. His Model predictive control research is multidisciplinary, relying on both Computational complexity theory, Optimization problem and Linear system.
His studies deal with areas such as Stability, Computation and Hybrid system as well as Mathematical optimization. The study incorporates disciplines such as Dynamic programming and Constrained optimization in addition to Optimal control. His Control engineering research is multidisciplinary, incorporating perspectives in Process control and Control.
His primary areas of study are Mathematical optimization, Model predictive control, Control theory, Control engineering and Optimal control. His research integrates issues of Linear system, Electric power system, Affine transformation, Stability and Quadratic equation in his study of Mathematical optimization. His Quadratic equation research includes themes of Artificial neural network, Semidefinite programming and Robustness.
The various areas that Manfred Morari examines in his Model predictive control study include Computational complexity theory, Hybrid system, Nonlinear system, Optimization problem and Solver. His study in Control theory is interdisciplinary in nature, drawing from both Power, Quadratic programming and Computation. He usually deals with Power and limits it to topics linked to Wind power and Tractive force.
Manfred Morari focuses on Model predictive control, Mathematical optimization, Control theory, Control engineering and Control theory. The study incorporates disciplines such as Computational complexity theory, Software, Solver and Nonlinear system in addition to Model predictive control. His research in Mathematical optimization intersects with topics in Probabilistic logic, Electric power system, Robustness and Convex optimization.
His research investigates the connection between Control theory and topics such as Power that intersect with issues in Steady state and Aerospace engineering. Manfred Morari works mostly in the field of Control engineering, limiting it down to concerns involving Efficient energy use and, occasionally, HVAC. His study looks at the relationship between Control theory and topics such as Quadratic equation, which overlap with Semidefinite programming.
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Robust process control
Manfred Morari;Evanghelos Zafiriou.
Model predictive control: theory and practice—a survey
C. E. Garcia;D. M. Prett;M. Morari.
Control of systems integrating logic, dynamics, and constraints
Alberto Bemporad;Manfred Morari.
The explicit linear quadratic regulator for constrained systems
Alberto Bemporad;Manfred Morari;Vivek Dua;Efstratios N. Pistikopoulos.
Model predictive control: past, present and future
Manfred Morari;Jay H. Lee.
Computers & Chemical Engineering (1999)
Robust constrained model predictive control using linear matrix inequalities
Mayuresh V. Kothare;Venkataramanan Balakrishnan;Manfred Morari.
Internal model control. A unifying review and some new results
Carlos E. Garcia;Manfred Morari.
Industrial & Engineering Chemistry Process Design and Development (1982)
Internal model control: PID controller design
Daniel E. Rivera;Manfred Morari;Sigurd Skogestad.
Industrial & Engineering Chemistry Process Design and Development (1986)
Robust model predictive control: A survey
Alberto Bemporad;Manfred Morari.
Lecture Notes in Control and Information Sciences (1999)
A unified framework for the study of anti-windup designs
Mayuresh V. Kothare;Peter J. Campo;Manfred Morari;Carl N. Nett.
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