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Modelling and assembly of the full vehicle 369
Fig. 6.44 Pilot Induced Oscillation (PIO) – not exclusively an aeronautical
phenomenon
PIO is caused when the operator is unable to recognize the effects of small
control inputs and therefore increases those inputs, before realizing they
were excessive and reversing them through a similar process. It is analogous
to the ‘excess proportional control gain oscillation’ discussed in classical
control theory (Leva et al., 2002). For road vehicles, drivers most
likely to induce PIO in steering tend to be inexperienced or anxious drivers
travelling at a speed with which they are uncomfortable. This type of PIO
is not to be confused with the typical experience of drivers of skidding
vehicles when the initial skid is corrected but the vehicle subsequently
‘fishtails’ or simply departs in the opposite direction – this is a ‘phasing at
resonance’ control error. The driver fails to apply a ‘feed forward’ (open
loop, knowledge-based) correction in advance of the vehicle’s response to
compensate for the delay in vehicle response.
PIO also occurs in tractive (i.e. throttle) control inputs and is the reason
even experienced drivers are incapable of travelling at a constant speed on
highways; perception of changes in following distance is universally poor.
If too little attention is spent on the driving task or if insufficient following
distance is left, these PIOs become successively amplified by following
drivers until the speed variation results in a ‘shunt’ accident. Radar-based
cruise control systems will alleviate this risk but are no substitute for attentive
driving while anything less than the whole vehicle fleet is fitted with it.
6.13.1 Steering controllers
There are a variety of controller models suitable for modelling driver behaviour
in existence. Some, such as ADAMS/Driver developed as part of the
MSC.ADAMS modelling package, are very complete – others, such as the
two-loop feedback control model used by the authors, are simpler. The analyst
must consider the needs of the simulation (and the financial constraints
of the company) and choose the most appropriate level of modelling to
achieve the task at hand. Driver models in general fall into two categories:
(1) Optimum control models. Optimum control models use some form
of ‘penalty function’ – a measure used to assess the quality of control
achieved. For example, for a vehicle steering model the appropriate variable
might be lateral deviation from the intended path. Optimum control models
use repeated simulations of the event and numerical optimization methods
to ‘tune’ the parameters for a control system to minimize the value(s) of the
penalty function(s) over the duration of the event of interest. For learned