2023 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award
2009 - IEEE Control Systems Award “For contributions to the application of optimization to modern control theory.”
2006 - Fellow of the International Federation of Automatic Control (IFAC)
1985 - Fellow of the Royal Society, United Kingdom
1981 - IEEE Fellow For contributions to optimal control and dynamic programming.
David Q. Mayne mainly focuses on Control theory, Mathematical optimization, Optimal control, Linear system and Model predictive control. His Control theory study which covers Estimator that intersects with State observer, Set and Adaptive algorithm. His work in Mathematical optimization addresses subjects such as Stability, which are connected to disciplines such as Constraint.
His studies in Optimal control integrate themes in fields like Kalman filter, Piecewise linear function and Nonlinear system. His study in Model predictive control is interdisciplinary in nature, drawing from both Control engineering, Systems engineering, Exponential stability and Robustness. His Nonlinear control research focuses on subjects like Linear-quadratic-Gaussian control, which are linked to Automatic control.
David Q. Mayne spends much of his time researching Control theory, Mathematical optimization, Optimal control, Linear system and Nonlinear system. His studies link Model predictive control with Control theory. His study looks at the intersection of Model predictive control and topics like Stability with Constraint.
His Mathematical optimization research is multidisciplinary, relying on both Function and Algorithm, Theory of computation. David Q. Mayne has researched Optimal control in several fields, including State, Piecewise linear function, Discrete time and continuous time and Bellman equation. The Nonlinear system study combines topics in areas such as Interval and Finite set.
David Q. Mayne mainly investigates Control theory, Model predictive control, Mathematical optimization, Optimal control and Robust control. His Control theory study frequently draws connections between related disciplines such as Bounded function. His Mathematical optimization study combines topics in areas such as Function, Piecewise linear function, Parametric programming and Piecewise.
The concepts of his Optimal control study are interwoven with issues in Polyhedron, Bellman equation, Quadratic equation, Finite set and Piecewise affine. David Q. Mayne focuses mostly in the field of Robust control, narrowing it down to topics relating to Nonlinear control and, in certain cases, Constrained optimization. His Nonlinear system research is multidisciplinary, incorporating elements of Stability and Robustness.
His scientific interests lie mostly in Model predictive control, Control theory, Robust control, Optimal control and Mathematical optimization. His work carried out in the field of Model predictive control brings together such families of science as Discrete time and continuous time, Exponential stability, Nonlinear system, Control theory and Robustness. His Robust control research incorporates themes from Nonlinear control, Linear system, Quadratic programming, Finite set and Bounded function.
In Linear system, David Q. Mayne works on issues like Linear-quadratic-Gaussian control, which are connected to Adaptive control. David Q. Mayne interconnects Piecewise affine and Bellman equation in the investigation of issues within Optimal control. Constrained optimization is the focus of his Mathematical optimization research.
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Survey Constrained model predictive control: Stability and optimality
D. Q. Mayne;J. B. Rawlings;C. V. Rao;P. O. M. Scokaert.
Receding horizon control of nonlinear systems
D.Q. Mayne;H. Michalska.
IEEE Transactions on Automatic Control (1990)
Robust receding horizon control of constrained nonlinear systems
H. Michalska;D.Q. Mayne.
IEEE Transactions on Automatic Control (1993)
Model Predictive Control
David Q. Mayne.
Robust model predictive control of constrained linear systems with bounded disturbances
D. Q. Mayne;M. M. Seron;S. V. Raković.
Min-max feedback model predictive control for constrained linear systems
P.O.M. Scokaert;D.Q. Mayne.
IEEE Transactions on Automatic Control (1998)
Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations
C.V. Rao;J.B. Rawlings;D.Q. Mayne.
IEEE Transactions on Automatic Control (2003)
Differential dynamic programming
H. H. Rosenbrock;D. H. Jacobson;D. Q. Mayne.
The Mathematical Gazette (1972)
Suboptimal model predictive control (feasibility implies stability)
P.O.M. Scokaert;D.Q. Mayne;J.B. Rawlings.
IEEE Transactions on Automatic Control (1999)
Design issues in adaptive control
R.H. Middleton;G.C. Goodwin;D.J. Hill;D.Q. Mayne.
IEEE Transactions on Automatic Control (1988)
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