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- David Q. Mayne

Electronics and Electrical Engineering

UK

2023

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Electronics and Electrical Engineering
D-index
56
Citations
28,367
183
World Ranking
1223
National Ranking
61

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.

- Control theory
- Mathematical analysis
- Statistics

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.

- Survey Constrained model predictive control: Stability and optimality (6379 citations)
- Robust receding horizon control of constrained nonlinear systems (955 citations)
- Robust model predictive control of constrained linear systems with bounded disturbances (893 citations)

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.

- Control theory (47.71%)
- Mathematical optimization (45.41%)
- Optimal control (31.65%)

- Control theory (47.71%)
- Model predictive control (20.18%)
- Mathematical optimization (45.41%)

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.

- Robust model predictive control of constrained linear systems with bounded disturbances (893 citations)
- Model Predictive Control (801 citations)
- Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations (628 citations)

- Mathematical analysis
- Control theory
- Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Survey Constrained model predictive control: Stability and optimality

D. Q. Mayne;J. B. Rawlings;C. V. Rao;P. O. M. Scokaert.

Automatica **(2000)**

6933 Citations

Receding horizon control of nonlinear systems

D.Q. Mayne;H. Michalska.

IEEE Transactions on Automatic Control **(1990)**

2635 Citations

Robust receding horizon control of constrained nonlinear systems

H. Michalska;D.Q. Mayne.

IEEE Transactions on Automatic Control **(1993)**

1438 Citations

Model Predictive Control

David Q. Mayne.

**(2004)**

1429 Citations

Robust model predictive control of constrained linear systems with bounded disturbances

D. Q. Mayne;M. M. Seron;S. V. Raković.

Automatica **(2005)**

1319 Citations

Min-max feedback model predictive control for constrained linear systems

P.O.M. Scokaert;D.Q. Mayne.

IEEE Transactions on Automatic Control **(1998)**

1195 Citations

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)**

904 Citations

Differential dynamic programming

H. H. Rosenbrock;D. H. Jacobson;D. Q. Mayne.

The Mathematical Gazette **(1972)**

791 Citations

Suboptimal model predictive control (feasibility implies stability)

P.O.M. Scokaert;D.Q. Mayne;J.B. Rawlings.

IEEE Transactions on Automatic Control **(1999)**

760 Citations

Design issues in adaptive control

R.H. Middleton;G.C. Goodwin;D.J. Hill;D.Q. Mayne.

IEEE Transactions on Automatic Control **(1988)**

726 Citations

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