David Mayne

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David Quinn Mayne
Born(1930-04-23)April 23, 1930
Germiston, Gauteng South Africa
Died27 May 2024(2024-05-27) (aged 94)
Oxford
Alma mater Witwatersrand University, Imperial College London
Scientific career
Fields Control theory
Electrical engineering
Mathematical optimization
Institutions Imperial College London, Harvard University, University of California, Davis
Doctoral advisor John Westcott
Doctoral students Peter E. Caines

David Quinn Mayne, FRS, FIEEE, FREng [1] (23 April 1930 - 27 May 2024) was a South African-born British academic, engineer, teacher and author. His pioneering and lasting contribution is in the field of control systems engineering. [2] His research interests centred on optimization and optimization-based design, nonlinear control, control of constrained systems, model predictive control and adaptive control.

Contents

Career

Having obtained his BSc.(Eng) at the University of the Witwatersrand David Mayne began his career in 1950 as a lecturer there (1950–54; 1957–59). In 1954 he took up a two year post working as an electrical engineer at the British Thomson-Houston Company, Rugby, England. At the end of 1956 he returned to his academic post at the University of Witwatersrand to develop a new course in automatic control and gaining a MSc.(Eng). He next applied for a research position at Imperial College London. Impressed by his MSc thesis, Arnold Tustin and John Westcott, appointed him as lecturer. [2]

He lectured at Imperial College London from 1959-67 and in 1967 obtained his DSc (Eng) and PhD at the University of London under John Westcott. [3] He was a Research Fellow at Harvard (1971). At Imperial College he was Professor of Control theory (1971–91) as well as concurrently heading the Department of Electrical Engineering (1984–88).

He was subsequently a professor in the Dept. of Electrical and Computer Engineering at University of California, Davis from 1989-96. [4] In 1996 he became Professor Emeritus and Senior Research Investigator in the Control and Power Research Group of the Department of Electrical and Electronic Engineering at Imperial College London. He was named honorary professor at Beihang University in Beijing in 2006. His students included Peter Caines.

Contribution to science

Mayne's research work is regarded as not only having had a lasting impact on the development of control theory, but his leadership style has inspired generations of new researchers. [2]

Among his many breakthroughs, arguably his most important contribution was his development of a rigorous mathematical method for analysing Model predictive control algorithms (MPC). It is currently used in tens of thousands of applications and is a core part of the advanced control technology by hundreds of process control producers. MPC's major strength is its capacity to deal with nonlinearities and hard constraints in a simple and intuitive fashion. His work underpins a class of algorithms that are provably correct, heuristically explainable, and yield control system designs which meet practically important objectives. [2]

Parisini and Astolfi consider that, "Mayne is also responsible for developing the first two-filter solution to the smoothing problem. This opened the door to substantial developments and is recognised as a pivotal contribution and precursor of the so-called particle filtering. Another cutting-edge contribution was his work on optimization-based design. He was an early user of exact penalty functions for optimization using sequential quadratic programming. The exact penalty method overcomes the widely referenced Maratos effect, identified by one of Mayne’s Ph.D. students. He also contributed to the early development of algorithms for non-differentiable and semi-infinite optimization problems". [2]

Personal life

David Quinn Mayne was born in Germiston, South Africa. He completed his education up to Master's level at the University of the Witwatersrand. [4] Early in his career he married fellow South African, Josephine. They had three daughters. The family moved to the UK in the 1950s where Mayne continued his research. He died in Oxford aged 94.

Awards and affiliations

Selected publications

Papers

Papers on optimization and optimal control

Related Research Articles

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Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from a linear–quadratic regulator (LQR). Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry.

Trajectory optimization is the process of designing a trajectory that minimizes some measure of performance while satisfying a set of constraints. Generally speaking, trajectory optimization is a technique for computing an open-loop solution to an optimal control problem. It is often used for systems where computing the full closed-loop solution is not required, impractical or impossible. If a trajectory optimization problem can be solved at a rate given by the inverse of the Lipschitz constant, then it can be used iteratively to generate a closed-loop solution in the sense of Caratheodory. If only the first step of the trajectory is executed for an infinite-horizon problem, then this is known as Model Predictive Control (MPC).

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References

  1. 1 2 "List of Fellows". Royal Academy of Engineering. Archived from the original on 21 May 2020. Retrieved 21 April 2018.
  2. 1 2 3 4 5 Parisini, Thomas; Astolfi, Alessandro (10 June 2024). "Professor David Q Mayne FREng FRS 1930 - 2024". Imperial College London news. Retrieved 14 June 2024.
  3. ↑ "David Mayne". The Mathematics Genealogy Project. Retrieved 9 September 2022.
  4. 1 2 Knoesen, André (6 June 2024). "In Memory of Professor Emeritus David Q. Mayne". University of California, Davis . Retrieved 14 June 2024.
  5. ↑ "IEEE Control Systems Award Recipients" (PDF). IEEE . Retrieved 30 March 2011.
  6. ↑ "IEEE Control Systems Award". IEEE Control Systems Society. Archived from the original on 29 December 2010. Retrieved 30 March 2011.
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