=Paper=
{{Paper
|id=Vol-1725/poster2
|storemode=property
|title=An Approach for Synthesizing Energy-Efficient Controllers for Production Systems from Scenario-Based Specifications
|pdfUrl=https://ceur-ws.org/Vol-1725/poster2.pdf
|volume=Vol-1725
|authors=Joel Greenyer,Daniel Gritzner
|dblpUrl=https://dblp.org/rec/conf/models/GreenyerG16
}}
==An Approach for Synthesizing Energy-Efficient Controllers for Production Systems from Scenario-Based Specifications==
https://ceur-ws.org/Vol-1725/poster2.pdf
An Approach for Synthesizing Energy-Efficient
Controllers for Production Systems from
Scenario-Based Specifications?
Joel Greenyer1 and Daniel Gritzner1
Leibniz Universität Hannover, Fachgebiet Software Engineering, Welfengarten 1,
D-30167 Hannover, Germany,
{greenyer|daniel.gritzner}@inf.uni-hannover.de
Abstract. Rising costs of energy production have made saving energy
an important topic in many areas, including modern production systems.
One idea is taking advantage of the braking energy becoming available
when decelerating moving components of a machine. However, using the
braking energy is a difficult task, as it requires the synchronization of
the movement of different parts of a production system. Storing this en-
ergy often is not an economically viable solution. Optimizing the entire
production process, in particular recognizing all optimization opportu-
nities while still fulfilling all requirements, is a difficult and error-prone
task. To support engineers in this task, we propose behavior modeling
through scenario-based specifications, to be able to automatically gen-
erate all valid behavior strategies for the system, combined with energy
usage modeling through state machines, to automatically identify the
most energy-efficient strategy.
Keywords: Energy-Efficiency, Optimization, Scenarios, Controller Synthesis
1 Introduction
Rising costs of energy production have made saving energy an important topic
in many areas, including modern production systems. However, not all ideas for
saving energy are easily implementable. One increasingly popular idea is taking
advantage of the braking energy becoming available when decelerating moving
parts of a machine [3].
Using the braking energy is a difficult task, though, as it requires the synchro-
nization of the movement of various parts of a production system. A simplified
example of this is shown in Fig. 1 which shows the power consumed by two robot
arms over time. When the two arms move after each other, the braking energy
is lost entirely. If the second arm starts accelerating at the same time as the first
arm starts decelerating, the total energy cost for moving both arms decreases
significantly as the cost for moving the second arm is reduced by taking advan-
tage of the braking energy of the first arm. Storing braking energy instead of
using it directly is often not an economically viable solution.
?
This research is funded by the DFG project EffiSynth.
unoptimized arm optimized arm
2.5
movement 2.5
movement
arm accelerates
power consumption 2
and moves
2
over time
1.5 1.5
1 1
0.5 0.5
blue – arm A
0 0
-0.5 -0.5
red – arm B -1
arm decelerates
-1
-1.5 -1.5
2.5 2.5
2 2
total power 1.5 1.5
consumption
over time
1 1
0.5 0.5
0 0
Fig. 1. Simplified curves of the power consumption over time of moving robot arms in
a production system
While the basic idea of using braking energy is simple enough, applying this
to an entire production process is a difficult task. The behavior of the system
cannot be modified arbitrarily without violating requirements. This may make
finding opportunities for optimization difficult and this difficulty is further in-
creased if multiple mutually exclusive opportunities are present. Choosing the
best combination of these opportunities is a non-trivial task, and manually im-
plementing the optimizations may even introduce new defects.
As a solution to these challenges we propose an automated approach to find-
ing the optimal behavior strategy for a production system. Our approach com-
bines scenario-based behavior modeling and state machines for modeling energy
usage. From the scenarios we can automatically generate all valid behavior strate-
gies, i.e., strategies which do not violate the specification. The energy models
can then be used to identify the most energy-efficient strategy.
In this paper we expand on our work in [3] by adding explicit energy models
to improve readability of the models and to separate the modeling of an object’s
attributes such as energy usage from the modeling of the object’s class’ behav-
ior. Additionally, we extend our approach to include iterative improvements of
the generated strategies to add the ability to account for complex electrical phe-
nomena not captured by our models. In ongoing research we also work towards
generating executable code for the generated strategies.
This paper is structured as follows: Sect. 2 introduces an example of a pro-
duction system used to explain our approach. Sect. 3 explains our approach for
finding the most energy-efficient controller and Sect. 4 describes preliminary re-
sults of this approach. The paper concludes with an outlook for future research
in Sect. 6 and related work in Sect. 5.
2 Example
Fig. 2 shows an example of a production system. In this example blanks arrive
via a conveyor belt, are picked up by a robot arm and then put into a press. Once
deposit belt arm B
c:Controller
press
feed belt arm A
table
Fig. 2. Example of a modern production system with two conveyor belts, two robot
arms and a press.
the press finishes, a different robot arm picks up the pressed item and places it
onto a deposit conveyor belt.
The intended system behavior and requirements can easily be described in
short, intuitive scenarios, e.g., (a) arm A must be back at the conveyor belt
before the next blank arrives and (b) both arms must move away from the press
before it starts pressing to avoid damage to the arms.
3 Approach
Our approach combines scenario-based modeling and state machines as models
for energy usage. An overview of the approach is shown in Fig. 3. Scenarios
model the intended behavior of the system and can be used to automatically de-
rive controllers for the system, i.e., strategies for the system to behave that do
not violate any requirements. The energy state machines can then be used to find
the most energy-efficient candidates. Since the energy models and algorithms to
identify these candidates use approximations, a more detailed, computationally
more expensive, evaluation of each candidate is performed. These evaluations
take into account the actual power over time curves of each component as well
as the time it takes to perform the different actions. This evaluation step offers
two benefits (a) the confidence in the ranking of the candidates is increased and
(b) the approximate values in the energy model can be updated to reflect the ac-
tual energy usage more closely. Candidate selection and evaluation is performed
iteratively until the most energy-efficient controller is found. For this controller,
PLC code, to run on an actual controller used for production systems, is gener-
ated.
3.1 Scenario-Based Behavior Modeling
Scenarios are an intuitive way to describe how components of a system may, must
or must not behave. They can be used to easily describe the intended behavior,
as well as requirements, of a system from an inter-component point of view. The
scenario in the top-left of Fig. 3 describes how the system must react when an
arm successfully picks up a blank. The arm is instructed by the controller to
arm A
:Controller :Arm controller synthesis
0
blankPickedUp arrivedAtPress accelerating
moveToPress -5 10
accelerating decelerating moving
moving 5
press.start
armB.accelerate
decelerating
arrivedAtPress armA.decelerate
update
armB.accelerate energy values
generate extract precisely calculate
PLC code best candidates actual power consumption
Fig. 3. Overview of our proposed approach.
move towards the press and, while doing so, it will continuously report its state
to the controller.
A formal, scenario-based specification is a collection of scenarios. In our ap-
proach, we use a modeling language extending the concepts of Live Sequence
Charts to specify scenarios and we use the Play-Out algorithm [4] to determine
how a collection of scenarios gets interwoven into a valid behavior strategy.
3.2 Energy Consumption Modeling
We propose using state machines to model the approximate energy consumption
of each component in a system. In such a state machine, the transitions are
labeled with the same events which are also used in scenarios. The example
shown at the top of Fig. 3 illustrates this. When arm A gets instructed to move
to the press, it will start accelerating. This event will cause both the scenarios
and the associated state machine to progress further. The arm is now in a state
in which it will use approximately 10 units of energy. Eventually, a decelerating
event will occur indicating that 5 units of braking energy are now available to
other components.
3.3 Controller Synthesis
The aforementioned Play-Out algorithm, together with a modified game-solving
algorithm used by UPPAAL TIGA [1], can be used to automatically generate
behavior strategies for the specified system [2]. The scenario-based specification
induces a game graph whose transitions correspond to actions and state changes
of the components of the system. Controllers for the system can be found in this
graph by searching for cycles which satisfy all scenarios. The existence of such
cycles, i.e., being able to follow transitions indefinitely without running into a
state that violates the specification, is equivalent to the specification being free
of inconsistencies.
Fig. 3 shows a state space generated via the Play-Out algorithm from a
scenario-based specification with a controller candidate highlighted in red. Once
a candidate has been identified, the energy model can be used to assign energy
usage values to the states and transitions of the candidate. This information can
then be propagated along paths to determine the approximate energy consump-
tions of entire paths and, ultimately, of the entire controller.
3.4 Precise Power Consumption Calculation
The controller synthesis generates candidates which are only optimal according
to approximate energy values. In order to increase the confidence in our results,
we generate multiple good candidates and evaluate each of those candidates us-
ing more precise models such as the actual power curves and time required for
each activity. Additionally, these calculations also provide us with new approx-
imate values to be used in our energy models to generate better candidates in
subsequent iterations.
3.5 PLC Code Generation
To prevent defects, including wasting energy, as well as mitigating some of the
cost caused by putting effort into writing a scenario-based specification, we work
on automatically generating code for programmable logic controllers (PLCs)
from controller we synthesized in previous steps.
4 Preliminary Results
We implemented a prototype which assigns energy values to the transitions of
candidate controllers. Every time a transition causes an energy model to change
its state, the transition is assigned a non-negative energy value. If, e.g., the arm
modeled in the top-right of Fig. 3 changes its state from accelerating to moving,
the corresponding transition in the controller is assigned a value of 10 minus any
available braking energy.
To compare different candidates, we calculate the occurrence probability of
each transition in a controller by treating controllers as Markov chains with each
outgoing transition being equally probable. A controller’s estimated mean energy
consumption is the sum of each transition’s probability times its energy value.
With this prototype and a model of the system shown in Fig. 2 with both
arms consuming and producing equal amounts of energy when moving from or
to the press, we were able to identify a strategy which could recoup more than
80% of the braking energy of the arms.
5 Related Work
Pellicciari et al. [6] try to utilize idle times of individual robot trajectories.
Wigström et al. [7] use dynamic programming to determine an optimal task
schedule for a multi robot cell. However, neither approach is able to automat-
ically find optimization opportunities outside of isolated time windows or pre-
defined energy exchange opportunities between components. Other approaches
usually have similar issues, as well as often relying only on approximations.
To the best of our knowledge, synthesizing energy-efficient discrete controllers
for production systems has barely been investigated outside of our own work in
[3]. There are algorithms for synthesizing time-optimal strategies from timed
game automata in UPPAAL TIGA [1] or from Markov decision processes in
PRISM [5]. However, neither tool is able to optimize for energy-efficiency. Also,
we strongly believe that our scenario-based approach is easier to use.
6 Future Work
In future research, we are cooperating with researchers from the field of mecha-
tronics. Issues, we want to address cooperatively, are the precise calculation of
how much power a controller consumes, PLC code generation, as well as the
evaluation of the approach proposed in this paper on an actual hardware as it
is found in real production systems.
Furthermore, we want to expand our models and synthesis algorithms to also
include time information. Our goals are to (a) model real-time requirements
such as “new blanks arrive at least every four seconds”, as well as (b) to identify
components which may move more slowly without slowing down the overall
process to take advantage of the fact that slower movement requires less energy.
References
1. G. Behrmann, A. Cougnard, A. David, E. Fleury, K. G. Larsen, and D. Lime.
UPPAAL-Tiga: Time for Playing Games! In Proc. 19th Int. Conf. on Computer
Aided Verification (CAV), pages 121–125, 2007.
2. J. Greenyer, C. Brenner, M. Cordy, P. Heymans, and E. Gressi. Incrementally
synthesizing controllers from scenario-based product line specifications. In Proc.
9th joint meeting of the European Software Engineering Conference and the ACM
SIGSOFT Symposium on the Foundations of Software Engineering 2013, 2013.
3. J. Greenyer, C. Hansen, J. Kotlarski, and T. Ortmaier. Towards synthesizing energy-
efficient controllers for modern production systems from scenario-based specifica-
tions. Procedia Technology (Proc. of the 2nd Int. Conf. on System-Integrated Intel-
ligence, SysInt 2014), 15(0):388–397, 2014.
4. D. Harel and R. Marelly. Specifying and Executing Behavioral Requirements: The
Play-In/Play-Out Approach. SoSyM, 2:82–107, 2003.
5. M. Kwiatkowska, G. Norman, and D. Parker. PRISM 4.0: Verification of proba-
bilistic real-time systems. In Proc. 23rd Int. Conf. on Computer Aided Verification
(CAV’11), volume 6806 of LNCS, pages 585–591. Springer, 2011.
6. M. Pellicciari, G. Berselli, F. Leali, and A. Vergnano. A method for reducing the
energy consumption of pick-and-place industrial robots. Mechatronics, 23(3), 2013.
7. O. Wigstrom, B. Lennartson, A. Vergnano, and C. Breitholtz. High-level schedul-
ing of energy optimal trajectories. IEEE Transactions on Automation Science and
Engineering, 10(1):57–64, 2013.