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 energy often is not an economically viable solution. Optimizing the entire production process, in particular recognizing all optimization opportunities 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 generate all valid behavior strategies for the system, combined with energy usage modeling through state machines, to automatically identify the most energy-efficient strategy.
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 [
Using the braking energy is a difficult task, though, as it requires the synchronization 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 advantage 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.
2.5 power consumption 2 over time 1.51
0.5 blue – arm A -0.50 red – arm B -1 -1.5 2.5 ctooontvsaeulrmptoipmwtieeorn 1.512 0.5 0 unoptimized arm
movement arm accelerates and moves arm decelerates
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 increased if multiple mutually exclusive opportunities are present. Choosing the best combination of these opportunities is a non-trivial task, and manually implementing the optimizations may even introduce new defects.
As a solution to these challenges we propose an automated approach to finding the optimal behavior strategy for a production system. Our approach combines scenario-based behavior modeling and state machines for modeling energy usage. From the scenarios we can automatically generate all valid behavior strategies, 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 [
This paper is structured as follows: Sect. 2 introduces an example of a production system used to explain our approach. Sect. 3 explains our approach for finding the most energy-efficient controller and Sect. 4 describes preliminary results of this approach. The paper concludes with an outlook for future research in Sect. 6 and related work in Sect. 5. 2
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
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 derive 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 actual 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 generated. 3.1
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 :Controller :Arm blankPickedUp moveToPress accelerating moving decelerating arrivedAtPress
controller synthesis press.start
armA.decelerate armB.accelerate armB.accelerate arrivedAtPress
-5 decelerating 5 accelerating
10 moving update energy values generate PLC code
extract precisely calculate best candidates actual power consumption 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
approach, we use a modeling language extending the concepts of Live Sequence
Charts to specify scenarios and we use the Play-Out algorithm [
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
The aforementioned Play-Out algorithm, together with a modified game-solving
algorithm used by UPPAAL TIGA [
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
Pellicciari et al. [
To the best of our knowledge, synthesizing energy-efficient discrete controllers
for production systems has barely been investigated outside of our own work in
[
In future research, we are cooperating with researchers from the field of mechatronics. 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.