This work presents an investigation into the parameter estimation of suspension
components and the vertical motions of wheeled vehicles from experimental data. The
estimation problems considered were for suspension dampers, a single wheel station and
a full vehicle.
Using conventional methods (gradient-based (GB), Downhill Simplex (DS)) and
stochastic methods (Genetic Algorithm (GA) and Differential Evolution (DE)), three
major problems were encountered. These were concerned with the ability and consistency
of finding the global optimum solution, time consumption in the estimation process, and
the difficulties in setting the algorithm's control parameters. To overcome these
problems, a new technique named the discrete variable Hybrid Differential Evolution
(dvHDE) method is presented.
The new dvHDE method employs an integer-encoding technique and treats all
parameters involved in the same unified way as discrete variables, and embeds two
mechanisms that can be used to deal with convergence difficulties and reduce the time
consumed in the optimisation process. The dvHDE algorithm has been validated against
the conventional GB, DS and DE techniques and was shown to be more efficient and
effective in all but the simplest cases. Its robustness was demonstrated by its application
to a number of vehicle related problems of increasing complexity. These include case
studies involving parameter estimation using experimental data from tests on automotive
dampers, a single wheel station and a full vehicle. The investigation has shown that the
proposed dvHDE method, when compared to the other methods, was the best for finding
the global optimum solutions in a short time. It is recommended for nonlinear vehicle
suspension models and other similar systems.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/3896 |
| Date | 28 October 2009 |
| Creators | Pedchote , C |
| Contributors | Purdy, D J |
| Publisher | Engineering Systems Department |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
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