The Lotus Free Piston Engine project story – people2

Simon Wilson setting up the motor control system in the engine test cells

Simon Wilson was in one of my 4th year MEng project groups at Sheffield Uni, and stayed on to read for a PhD with me on Temperature Estimation for Permanent Magnet AC Motors. Simon received an Industrial CASE award from Rolls-Royce Derby for the duration of his study. He can be seen here setting up the DSpace controller which supervises data acquisition and control for the engine.

Simon has subsequently joined EA Technology at Capenhurst as a consultant on new renewables technology.

Ed Winward setting up the control desk Labview front end

Next up is Ed Winward, who was working for Professor Rui Chen at Loughborough University. Ed designed and implemented the entire front-end for the free piston project in Labview (see picture left) which allowed us to start experimentation in earnest.

The best tribute to his part in this project is a YouTube video he made which documents an early run of the Free-Piston Engine

Youtube Free-Piston Video

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

Lotus free-piston experimental engine

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

D. Gladwina, P. Stewartb, , and J. Stewartb

a Department of Electronic and Electrical Engineering, University of Sheffield, Mappin St. Sheffield S1 3JD, UK

b School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK

Engineering Applications of Artificial Intelligence
Volume 24, Issue 4, June 2011, Pages 586-594



In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimise the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as simulated annealing and variable neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, genetic algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a controlled migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

Keywords: Genetic algorithms; Hardware-in-the-loop; Migration; Response surfaces; Engines