A multiobjective evolutionary algorithm approach is proposed for the H∞ design of an electromagnetic suspension (EMS) control system for a maglev vehicle. The evolutionary algorithm is used in conjunction with an H∞ loop-shaping design procedure to perform multiobjective search over a set of possible weighting function structures and parameter values in order to satisfy a number of conflicting design criteria. It is demonstrated that the proposed approach offers a number of potential benefits over other ‘mixed optimization’ techniques by allowing the control engineer to select from a number of satisfactory design solutions of differing complexity.
ZakianV.Critical systems and tolerable inputs. Int. J. Control, 1989, 494, 1285–1289.
2.
WhidborneJ. F.LiuG. P.Critical Control Systems: Theory, Design and Applications, 1993 (Research Studies Press, Taunton).
3.
McFarlaneD. C.GloverK.Robust Controller Design Using Normalised Coprime Factor Plant Descriptions, Vol. 138, Lecture Notes on Control and Information Science, 1990 (Springer-Verlag, Berlin).
4.
WhidborneJ. F.PostlethwaiteI.GuD.-W.Robust controller design using H∞ loop-shaping and the method of inequalities. IEEE Trans. Control Systems Technol., 1994, 2 (4), 455–461.
PostlethwaiteI.WhidborneJ. F.MuradG.GuD.-W.Robust control of the benchmark problem using H∞ methods and numerical optimization techniques. Automatica, 1994, 30 (4), 615–619.
7.
WhidborneJ. F.MuradG.GuD.-W.PostlethwaiteI.Robust control of an unknown plant—the IFAC 93 benchmark. Int. J. Control, 1995, 61 (3), 589–640.
8.
KortümW.UtztA.Control law design and dynamic evaluations for a maglev vehicle with a combined lift and guidance suspension system. Trans. ASME, J. Dynamic System Measmt and Control, 1984, 106, 286–292.
9.
SinhaP. K.Electromagnetic Suspension: Dynamics and Control, 1987 (Peter Peregrinus, London).
10.
MüllerP. C.Design of optimal state-observers and its application to maglev vehicle suspension control. In Proceedings of Fourth IFAC Symposium on Multivariable Technological Systems, Fredericton, Canada, 1977, pp. 175–182.
11.
ZakianV.A performance criterion. Int. J. Control, 1986, 43 (3), 921–931.
12.
ChipperfieldA. J.FlemingP. J.PohlheimH. P.A genetic algorithm toolbox for MATLAB. In Proceedings of International Conference on Systems Engineering, Coventry, 6–8 September 1994, pp. 200–207.
13.
FonsecaC. M.FlemingP. J.An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computing, 1995, 3 (1), 1–16.
14.
MühlenbeinH.Schlierkamp-VoosenD.Predictive models for the breeder genetic algorithm: I. continuous parameter optimization. Evolutionary Computing, 1993, 1 (1), 25–49.
15.
FonsecaC. M.FlemingP. J.Multiobjective optimal controller design with genetic algorithms. In Proceedings of IEE Control ‘94, 1994, pp. 745–749.
16.
GoldbergD. E.SergestP.Finite Markov chain analysis of genetic algorithms. In Proceedings of Second International Conference on Genetic Algorithms (Ed. GrefenstetteJ. J.), 1987, pp. 1–8.
17.
GoldbergD. E.RichardsonJ.Genetic algorithms with sharing for multimodal function optimization. In Proceedings of Second International Conference on Genetic Algorithms (Ed. GrefenstetteJ. J.), 1987, pp. 41–49.
18.
SilvermanB. W.Density Estimation for Statistics and Data Analysis, 1986 (Chapman and Hall, London).
19.
DebK.GoldbergD. E.An investigation of niche and species formation in genetic function optimization. In Proceedings of Third International Conference on Genetic Algorithms (Ed. SchafferJ. D.), 1989, pp. 42–50.
20.
HuntK. J.Controller synthesis with genetic algorithms: The evolutionary metaphor in the context of control system optimization. Glasgow University Mechanical Engineering Technical Report, 1992.
21.
PattonR. J.LiuG. P.Robust control design via eigenstructure assignment, genetic algorithms and gradient-based optimization. IEE Proc. D, 1994, 141 (3), 202–208.
