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372 Multibody Systems Approach to Vehicle Dynamics
threshold to decide when they switch. In general, neural networks are run
on transistor devices or in computer simulations. They require a period of
‘training’ when they learn what settings need to be made for individual
neurons in order to produce the required outputs. Once trained, they are
extremely rapid in operation since there is very little ‘processing’ as such,
simply a cascade of voltage switching through the transistor network. If the
network is implemented as semiconductor transistors then it works at a
speed governed only by the latency of the semiconductor medium –
extremely fast indeed. Neural networks are extremely useful for controlling
highly non-linear systems for which it is too difficult to code a traditional
algorithm. However, the requirement for a large amount of data can make
the learning exercise a difficult one. Recent advances in the field reduce the
need for precise data sets of input and corresponding outputs; input data
and ‘desirable outcome’ definitions allow neural networks to learn how to
produce a desirable outcome by identifying patterns in the incoming data.
Such networks are extremely slow in comparison to the more traditional
types of network during the learning phase. In general, for driver modelling
there is little applicability for neural networks at present due to the lack of
fully populated data sets with which to teach them. It is also worth commenting
that for any input range that was not encountered during the learning
phase, the outputs are unknown and may not prove desirable. This latter
feature is not dissimilar to real people; drivers who have never experienced
a skid are very unlikely to control it at the first attempt.
(v) System identification. System identification is a useful technique, not
dissimilar in concept to neural networking. A large amount of data is
passed through one of several algorithms that produce an empirical mathematical
formulation that will produce outputs like the real thing when
given the same set of inputs. The formulation is more mathematical than
neural networking and so the resulting equations are amenable to inspection
– although the terms and parameters may lack any immediately obvious
significance if the system is highly non-linear. System identification
methods select the level of mathematical complexity required to represent
the system of interest (the ‘order’ of the model) and generate parameters to
tune a generic representation to the specific system of interest. As with
neural networks, the representation of the system for inputs that are beyond
the bounds of the original inputs (used to identify the model) is undefined.
System identification is useful as a generic modelling technique and so has
been successfully applied to components such as dampers as well as control
system and plant modelling. System identification is generally faster to
apply than neural network learning but the finished model cannot work as
quickly. The same data set availability problems for neural networking also
mean system identification is not currently applicable to driver modelling.
(vi) Adaptive controllers. Adaptive control is a generic term to describe the
ability of a control system to react to changes in circumstances. In general,
people are adaptive in their behaviour and so it would seem at first glance
that adaptive control is an appropriate tool for modelling driver behaviour.
Optimum control models, described above, generally use some form of
adaptive control to optimize the performance of a given controller architecture
to the system being controlled and the task at hand. Adaptation is a problem
in real world testing since it obscures real differences in performance;