Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
Simulating Operability of Wheel Loaders
Operator Models and Quantification of Control Effort
Reno Filla
Emerging Technologies, Volvo Construction Equipment, Eskilstuna, Sweden
E-mail: [email protected]
Summary
In working machines the human operator is essential for the performance of the total system. Productivity and energy
efficiency are both dependent not only on inherent machine properties and working place conditions, but also on how the
operator manoeuvres the machine. In order to operate energy-efficient the operator has to experience the machine as
harmonic. This is important to consider during the development of such working machines.
The influence of the human operator is an aspect that is traditionally neglected in dynamic simulations conducted in
concept design, because the modelling needs to be extended beyond the technical system. In the research presented in this
paper we show two approaches to rule-based simulation models of a wheel loader operator. Both operator models interact
with the machine model just as a human operator does with the actual machine. It is demonstrated that both operator
models are able to adapt to basic variations in workplace setup and machine capability. A “human element” can thus be
introduced into dynamic simulations of working machines, providing more relevant answers with respect to
operator-influenced complete-machine properties such as productivity and energy efficiency.
Operability of a machine is traditionally evaluated by professional test operators using physical prototypes. The presented
research demonstrates how this can be complemented by a calculated measure for the operator’s control effort, derived
from the recorded control commands of the machine operator. This control effort measure can also be calculated from the
control commands of an operator model in a simulation, such as those presented in this paper. It thus also allows for an
assessment of operability already in the concept design phase.
Keywords: operator model, operability, working machines
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
output, otherwise, along with other comfort issues, mostly
1
Introduction
to be taken care of by cab designers and ergonomists (also
known as “the HMI people”, with this abbreviation
To summarise the summary, in this paper make the case
narrowly interpreted as standing for “human-machine
that operability needs to be considered early on in the
interface”).
development of wheel loaders, alongside such established
design targets as productivity and energy efficiency. We
touch upon research that shows how proper operator
models can introduce a “human element” into dynamic
simulations, providing more relevant answers with respect
to operator-influenced complete-machine properties such
as productivity and energy efficiency. We then show two
ways of also drawing conclusions on the operability of
wheel loaders by analysing either measurement data from
physical tests or simulation results.
2
Background
This limited view completely fails to recognise the
interaction between machine, operator and working
environment – and the implications for both machine
owner and equipment manufacturer. To begin with, the
OEM and the machine owner have a business relationship,
connecting the machine owner’s financial situation to the
OEM’s. It is thus in the equipment manufacturer’s own
best interest to design ma-chines such that the machine
owner’s business case is optimised by maximising income
and minimising costs.
High productivity is a prerequisite for income maximisation, as is high energy efficiency to minimise the total
Wheel loaders are highly versatile machines and are built
cost of ownership. The connection to operability is that a
in vastly varying sizes. Due to their versatility, wheel
high productivity requirement certainly puts pressure on
loaders need to fulfil a great many requirements, which
the operator, i.e. increases the operator’s workload.
are often interconnected and sometimes contradictory.
According to [2], workload is “the portion of the
Leaving aspects such as total cost of ownership,
operator’s limited capacity actually required to perform a
availability, legislation compliance, etc. aside, the most
particular task”. A demand of in-creased productivity will
important machine properties are
thus take up a larger portion of the operator’s limited
capacity – until the limit is reached and the joint system,
•
Productivity (expressed for example in ton/hour)
•
Fuel efficiency (expressed for example in
longer capable of increasing productivity. Thus, an upper
ton/litre) or better Energy efficiency (expressed
limit in obtainable productivity exists not necessarily
for example in ton/kWh)
because of the machine having reached its limit, but
Operability.
because of the human operator.
•
According to the definition used in our work, operability
is “the ease with which a system operator can perform the
assigned mission with a system when that system is
functioning as designed” [1].
With productivity having been the traditional selling point
in the pasty and thus the most established design criterion
for engineers working with product development, energy
efficiency has rightly come into focus in the last decade.
Operability, however, is still considered by many
development engineers to be something of a “soft
property”, and possible to improve by installing engines
and/or hydraulics and/or drivetrains with higher power
which is formed by the machine and its operator, is no
The second objective of most operators after fulfilling the
required productivity is to minimise their own workload.
As with almost any piece of machinery, how it is used by
the operator influences how much energy it requires
during operation. Few individuals, if any, will chose to
operate each day for several hours in a way that is
energy-efficient, yet mentally and/or physically more
demanding. Despite, with little or no spare capacity left
due to high productivity demands, the operators are less
able to focus on using the machine in an energy-efficient
manner, anyway.
The business idea for the OEM is therefore to find the
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
perfect balance and to design machines with a natural,
intuitive and workload-minimising way to operate them
with highest possible energy efficiency at a productivity
level as high as required. To achieve this goal, the
operator can be actively or passively guided in controlling
the machine or assisted by (partly) automated functions.
It is therefore vital that engineers working with product
development also take the machine’s operability into
account and consider it just as important as productivity
and energy efficiency. That focus has to be established
from early on in the product development process. As with
all system properties that arise out of the complex
interplay between components and sub-systems within a
machine, freedom of design is greatest in the beginning, in
the concept development phase. This is also were the
potential impact on both development and product cost is
greatest, while the actual cost of exploring various
scenarios is lowest. It is therefore necessary to improve
3
Working cycles
The short loading cycle, sometimes also dubbed V-cycle or
Y-cycle for its characteristic driving pattern is highly
representative of the majority of applications of a wheel
loader. Typical for this cycle is bucket loading of some kind
of granular material (e.g. gravel, sand, or wood chips) on an
adjacent load receiver (e.g. a dump truck, conveyor belt, or
stone crusher) within a rather tight time frame of 25-35
seconds. For this reason, the short loading cycle has been
established as the main test cycle for operability during
development of wheel loaders – and is therefore a
necessary ingredient when the operation of wheel loaders is
to be simulated.
Figure 1 shows one possible way of dividing this
working cycle into several phases (in other circumstances it
might be meaningful to introduce new phases and events or
combine some of the phases shown below).
our methods so that we can evaluate the operability of a
working machine and operator-influenced properties like
productivity and energy efficiency early in a development
project. The tool of choice here is simulation.
2
Simulation models in general
Computer simulation of complete vehicles and machines
can today be considered to be the state of the art in the
conceptual design phase. Especially in the on-road sector,
be it on the consumer side (cars) or on the commercial
side (trucks and buses), but increasingly also for off-road
working machines, the trend has progressed from
simulation of sub-systems to the simulation of complete
systems, among other things to evaluate performance,
energy efficiency, handling, comfort, and durability.
Naturally, in order to simulate productivity, energy
efficiency and operability of wheel loaders, most of the
machines’ sub-systems and components need to be
modelled in a modular way and at various levels of detail.
Furthermore, we need to model the machine’s interactions
with its environment – which in the case of a wheel loader
happens in two places: tyres and bucket. For a more
detailed review the reader is referred to [3].
Fig. 1: Short loading cycle: phases and extension V to Y.
From phase 1 to 6, the wheel loader operator first fills
the bucket in the gravel pile, then back drives backwards
towards the reversing point (phase 4) and steers the wheel
loader to accomplish the characteristic V-pattern of a short
loading cycle. The lifting function is engaged the whole
time. The operator chooses the reversing point such that
having arrived at the load receiver and starting to empty the
bucket (phase 6), the lifting height will be sufficient to do
so without delay. In case of a bad matching between the
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
machine’s travelling speed and the lifting speed of the
may even result in unphysical results, especially when
bucket, the operator needs to drive back the wheel loader
more than one path for power transfer exists – as is the
even further than necessary for manoeuvring alone. This
case for wheel loaders, where both hydraulics and
additional leg transforms the V-pattern into a Y-pattern.
drivetrain compete for the engine torque.
In the remaining phases 7 to 10, the bucket needs to be
lowered and the operator steers the wheel loader back to the
initial position in order to fill the bucket again in the next
cycle.
The most prominent loading cycle phases with regard to
operability and operator workload are bucket filling (phase
#1), reversing (phase #4) and bucket emptying (phase #6).
These and other challenges are discussed in more detail in
[3].
4
Choosing instead to precisely track a time series of system
states (for instance engine speed and engine torque) does
not change that much, it rather makes the results more
dubious. This is because a real operator uses a machine
according to a strategy or tactic, i.e. the inputs are
controlled, according to the output. For example, forcing a
simulated wheel loader to precisely match a time series of
engine speed and engine torque means that we still track
the output signals of a reference machine (i.e. its speed),
The need for proper operator models
Traditionally, simulation engineers who model complete
vehicles or machines do not include any driver or operator
model, but control their virtual experiments by relatively
and still with all the dynamics of that reference machine
(e.g. engine dynamics) hardcoded into the required output
signal, but now we have also for example implicitly
hardcoded values for axle ratio, wheel diameter, and
losses due to internal friction and ground resistance.
simple algorithms that prescribe a specific time sequence
The last simple option then would be to track a time series
of either input signals, system states or output signals, all
of input signals in a forward simulation. A first approach
of which force the simulated vehicle or machine to follow
might be to simply record a human operator’s control
the chosen cycle.
signals, adjust them by filtering, scaling etc., and input
The latter method, called inverse (or backward) simulation
is used predominantly in the conceptual design phase. This
method takes a fixed output (e.g. vehicle speed) and
calculates backwards in order to obtain the results. Such
simulations are fast and often used for optimisation or
exploration of the design space. They are also frequently
used when comparing the energy efficiency of different
vehicles, for instance using the synthetic on-road New
European Driving Cycle (NEDC). The problem is not only
that cycles like the NEDC are simplistic to the point of
actually being unrealistic, but also that often mere
quasi-static calculations are performed, ignoring any
them into the model. This method is applicable in
situations where a technical system’s reaction to a specific
time sequence of input signals is of interest. One example
might be a brake test or an avoidance manoeuvre to test
stability. This will give correct results with respect to the
dynamic behaviour of the system, but it replaces the
adaptive closed-loop control of a human driver or operator
with open-loop control. We again have the problem of
implicitly hardcoded values, as already mentioned above.
The simulation results may be correct, but irrelevant
because the output does not match the desired working
cycle.
dynamic effects. Research is being done with the aim of
A combination of these principle modelling approaches is
including dynamics into inverse simulation, e.g. [4], but it
also used. Simulation engineers sometimes want to avoid
still ignores the human operator’s ability to adapt to the
modelling the complete system and give the load from one
machine and the working environment. Forcing a
sub-system as a reference in time or position. In [5] and
simulation model of a wheel loader to behave precisely
[6] for example, the authors did not want to model the
according to a specific output will in many cases result in
hydraulic system of a backhoe or wheel loader and thus
forced input signals that do not reflect how a human
load the engine according to a defined power/torque
operator would have used the machine. In some cases, it
profile, which has been determined earlier in either a
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
measurement or simulation. The problem with this is that
these loads can become unsynchronized with the
simulation model (will be applied too early or too late,
time-wise or position-wise) and will thus make such
comparisons invalid. One particular challenge is the
reversing point in a short loading cycle, which, as already
mentioned, the operator chooses such that having arrived
at the load receiver, the lifting height will be sufficient to
start emptying the bucket without the need for any
additional lifting. If the model tracks machine speed and
hydraulic load given by positions, then it will always
reverse at the same point and in a comparison of machines
with different hydraulic system performance the bucket
height will be different at the load receiver. If instead the
5
Operator models developed in our work
In our work two operator models were realised. Both
focus on the tactical (manoeuvring) and operational
(control) aspects, and are not concerned with the
strategical level (planning). Both models are separate
entities and the information exchange with machine model
and working environment is limited to operator inputs and
outputs similar to a human being, i.e. the operator has
neither insight nor influence at a deeper level. In general
the operator model and machine model act as black box
models in relation to each other. No internal states are
sent, because the intention is to model human-machine
interaction, which naturally occurs through a limited
speed reference values are given at specific times instead
number of channels.
of positions, then a machine that takes longer to accelerate
The methodology employed for both operator models,
to a certain speed reference will in total have travelled a
essentially based on un-structured and semi-structured
shorter distance during this phase. In both cases, the
research interviews with professional operators, is
simulation results will not be comparable (see [3] for a
described in [9], the models themselves in [10] and [11],
more detailed review).
respectively.
What we need are therefore simulation models of the
The first operator model [10] focused on bucket filling and
human operator that use the system model like a human
has been realised as state equations in ADAMS with an
operator would use the physical system in reality. Just as
algorithm reminiscent of fuzzy logic (using parallel and
the requirement on the system model’s level of detail is
nested STEP functions to resemble membership functions,
dependent on the question we want the simulation to
without the need for defuzzification). This approach
answer, so is the requirement on the operator model’s
proved to be successful in the bucket filling phase of a
level of detail. In some cases it might actually be enough
short
to let a simple PI-controller with anti-windup track a
provided by ADAMS proved quite cumbersome and
time-series of vehicle speed values, like in [7] and more or
resulted in less satisfactory modelling of the remaining
less also in [8]. But in all cases where the behaviour and
phases of a short loading cycle.
limitations of the operator affect the behaviour and
limitations of the entire system, we clearly need operator
models with a greater level of detail, models that
experience feedback and can make more complex
decisions rather than just slavishly follow an arbitrary path
or trajectory. Any such operator model needs to be
•
•
cycle.
However,
using
functionality
In spite of this, we were able to show that this operator
model was useful due to a certain adaptability. We
demonstrated this by simulating the same wheel loader
model with two different torque converters. This
component is known to have a vital impact on machine
performance and energy efficiency. In order to obtain the
adaptive (in the sense of the operator model itself
same traction force, a “weaker” converter requires higher
being able to adapt to basic scenario variations)
slip between pump and turbine – which for similar traction
flexible (in the sense of easy to manually adapt to
force requirements leads to higher engine speeds over a
a new scenario)
•
loading
transparent (in the sense of easy to understand
and validate).
complete loading cycle. A human operator compensates
for this with higher accelerator pedal values, which we
have proven earlier in tests on a physical prototype
machine. But this also affects the hydraulic system, which
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
in turn requires compensation. Figure 2 shows that the
This operator model’s capability to adapt to basic
operator model managed to adapt to the new machine
variations in the working place has also been validated by
characteristics by running the engine at higher speeds –
running simulations with varying parameter setups. One
just as a human operator would have done.
example is that of changed speed in the bucket’s lift
hydraulics, emulating insufficient pump capacity. A
human operator adapts to this by reversing farther with the
wheel loader (thus extending the V-pattern to a Y) until
finally driving forward to the load receiver to dump the
bucket’s load. This could also be demonstrated in our
simulations without any modification of the operator
model.
In summary, both operator models developed prescribe the
machine’s working cycle in a more generic way,
Fig. 2: Simulated engine duty for wheel loaders equipped with
different torque converters
independently of the machine’s technical parameters. The
characteristics of whole components or sub-systems can
therefore be changed without compromising the validity of
the simulation. This gives more relevant answers with
The second operator model [11], with a focus on the
respect to machine productivity and energy efficiency.
remaining phases of the short loading cycle, was realised
The next chapter will explain how also important aspects
in Stateflow and co-simulated with ADAMS (Figure 3).
of a wheel loader’s operability can be evaluated by means
Simulink was used as the integrating environment.
of simulation
6
Assessing operability of wheel loaders
A more detailed explanation of operability issues of wheel
loaders can be found in [3]. In the remainder of this paper
we will look at two specific results of our work: the
machine harmony diagram and a measure for control
effort during bucket filling. Given proper models, both
methods can also be used with simulation results, rather
than measurement data obtained in physical testing.
Fig. 3: Co-simulation of Stateflow and ADAMS, with Simulink
as integrating environment
The block “SignalConditioning” in Figure 3 contains rate
limiting functions that prevent the operator model from
too rapid changes in the actuation of a control. The idea
with these was to resemble human limitations. One idea,
not realised, was to not directly apply the control
commands generated by the operator model, but to use
PID controllers in between operator model and machine
model. The gains of these controllers could then be used
as an additional way to reflect human limitations.
7
Machine harmony diagram
In an earlier section the phases of a short loading cycle
have been described: bucket filling, followed by driving
backwards, reversing such that having arrived at the load
receiver the lifting height will be just sufficient to start
emptying the bucket, during all this the lifting function
being engaged without interruption.
This is comprehensibly visualised in a diagram such as in
Figure 4, showing bucket height over integrated machine
speed (i.e. the machine’s travelling distance, provided no
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
tyre-slip occurred).
case in practice:
Figure 5 shows three selected short loading cycles,
originating from a non-stop re-cording, during which the
operator changed his bucket filling style. As can be seen,
the bucket height when leaving the bank varies, but the
operator succeeded very well in choosing a reversing point
in order to reach the load receiver with the bucket at a
sufficient height for emptying.
Fig. 4: Machine harmony diagram with identified phases of a
short loading cycle
The ratio of lift speed to machine speed, which is crucial
to how long the wheel loader needs to be driven
backwards until reversing, can here be seen as the graph’s
slope. The graph is fairly straight from the beginning of
phase 2 (where the loader leaves the bank) to the end of
phase 5 (where the loader arrives at the load receiver).
This indicates that the operator judged well when to
Fig. 5: Recorded short loading cycles with different bucket
filling styles
reverse. Otherwise, the slope would have been steeper at
the end of phase 5, indicating that the operator needed to
slow down or even stop in order for the bucket to reach
sufficient height for emptying.
Together with time plots, such Machine harmony
diagrams are a useful tool for evaluating how good a
motion balance has been achieved in the machine. To a
The distance between the point of reversing and the bank
certain extent they also show the human operator’s (or the
(or load receiver) is an indication of how good a balance
operator model’s) operating style.
has been achieved between the two motions of lifting and
driving. Introduced in late 2002, engineers at Volvo call
this a Machine harmony diagram, because it visualises
aspects that are important for a machine’s operability.
Another interesting aspect is the visualisation of the
operator’s style of bucket filling and bucket emptying. The
chosen bucket filling style also indirectly influences the
position where the loader needs to reverse: Leaving the
bank at the beginning of phase 2 with a higher bucket
means that it will take less time to raise it from there to a
8
Control effort in bucket filling
In previous papers we speculated that one approach to
quantify operability might be through the definition of an
operator input dose similar to the vibration dose value in
the assessment of whole-body vibration exposure. It was
not as easy as that, but in a recent study [12] we managed
to derive a similar measure, which we dubbed operator
control effort.
sufficient height for emptying. This means that in theory
Eighteen
operators
with
varying
operating
skill
the point of reversing will be nearer to the bank when
participated in this study, each had to test-operate a wheel
leaving with a higher bucket position, because the operator
loader in three different software settings, in randomised
adapts to the machine. It can be shown that this is also the
order and under similar conditions. In order to study the
effect of the interdependencies between drivetrain and
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
hydraulics during bucket filling (see [3][12] for a detailed
In [12] two methods for approximation of time series data
explanation), the wheel loader software was modified to
with straight lines are shown: one being a variant of the
limit engine speed, and thus traction force, whenever the
Split and Merge (SM) algorithm, which is often used in the
transmission’s 1st gear was automatically engaged, which
field of mobile robotics for transforming data from laser
occurs only during bucket filling (all machines normally
scanning to obstacle borders, the other a self-developed
nd
algorithm dubbed Stretch and Relax (SR), due to its
start in 2 gear).
During all test sessions various data were recorded off the
wheel loader’s CAN bus, enhanced by additional data,
working principle reminding of two rubber bands in series
with periodically applied loads.
either calculated or acquired from externally mounted
Since then, we have concluded that the following
sensors. All tests were also recorded on video using an
modification produces slightly better results:
externally
placed
digital
video
camera
and
later
synchronised with the acquired data from the CAN bus.
The operators’ subjective evaluations of the machines’
ease of bucket filling, perceived power and overall
ranking, together with self-reports on satisfaction with
their task performance and their personal stress level, were
captured on visual analogue scales with free comments,
•
Approximation of gas pedal, lift and tilt lever
signals with the SM algorithm
•
Combination of the three numbers by simple
summation (not RSS)
•
Normalisation of the result with the achieved
bucket load (in ton) during the cycle
performed directly after each live test-driving session.
After a theoretical discussion in [12] it is hypothesised that
operability during bucket filling can be assessed through a
measure of the operator control effort, to be generated by
approximating the control commands for accelerator
pedal, lift and tilt function with straight lines (Figure 6),
counting the number of breakpoints (or line segments)
required and RSS-combining them (the square root of the
sum of the squares of individual signal’s number of
breakpoints).
Fig. 7: Relationship between load-normalised control effort and
ease of bucket filling
Figure 7 shows that a strictly monotone relationship
between control effort and the operators’ subjective
evaluation of the ease of bucket filling could be found in
eight cases, most of them being operators with higher
skills. In three more cases a monotone relationship was
Fig. 6: Operator control commands, measured and approximated
present, meaning that the control effort data for the
machine ranked in-between highest and lowest ease of
bucket filling were not statistically significantly different
from one of their neighbours, but otherwise the same
Hydraulikdagarna, Linköping, Sverige, 17-18 april 2012
relationship could be identified, i.e. higher ranking of a
machine’s ease of bucket filling co-occurs with lower
calculated values for control effort (all with a statistical
significance of above 99.9%). Out of the eighteen
participating operators we have thus eleven cases in
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