As machine users generally only define the start and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mechanism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical setup. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profile.
Cyber-physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. In these situations, a traditional (sequential) design process becomes inefficient in finding good designs options. Instead, an integrated approach is needed where parameters in both the control and embedded domain can be chosen, evaluated and optimized to have a good solution in both domains. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem. In this paper, we show how domain knowledge can be used to guide the design-space exploration process for an advanced control system and its deployment on embedded hardware. We use domain knowledge, captured in an ontology, to reason about the relationships between parameters in the different domains. This leads to a stepwise design space-exploration process where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. In this process, we make use of cross-domain evaluation to find feasible design options with good system-level performance.
The use of technologies that automate handling goods and loading units in warehouses and depots is not new. Yet, the purchase process of these technologies issues troubles and the estimation of the economic advantages brought by one or another technology to the entire chain of operations in logistics are not always known. Faults or not documented decisions put pressure on managers and prices for services. They can cause a drop in the competitiveness of warehouse operators, particularly in uncertain conditions. Academia documented the cost of warehouse storage well. Yet, little research has looked into the economic justification of implementing automatic systems for loading or unloading activities and the impact on complementary operations. For this reason, a model is needed to calculate the cost of operations when different technical equipment is used. This research further investigates the cost categories that must be considered when purchasing automated loading/unloading technologies. The model includes the purchase and operational loading costs that new technologies generate and the cost of adjacent operations to loading activity. The case study uses forklifts as the reference scenario and provides an overview of the return on investment and a break-even period when other technologies are in use. The calculation model shows that increasing cargo volume leads to a better RoI. The same observation is also made regarding the rise in labour costs. For the latter, using human operators to handle pallets on a one-by-one basis generates an exponential increase in operational cost due to delays and faults. On the other side, the cost of implementing automated loading/unloading technologies and the consideration of technology risk determine the low economic advantages. An in-depth cost and benefit analysis shows in which situation a technology generates greater benefits. Further results of this paper show that better use of trucks' loading capacity can positively impact the financial performance of automated loading technologies, as a higher volume of cargo is moved (at once) without human intervention.
The essential advantage of the conventional stepping motor drive technique bases on step command pulses is the ability of open-loop positioning. By ruling out the cost of a position sensor, stepping motors are preferred in low power positioning applications. However, machine developers also want to obtain high dynamics with these small and cheap stepping motors. For that reason, stepping motors are used at its limits as much as possible. A drawback of the open-loop control is the continuous risk of missing a step due to overload. Due to this uncertainty, robustness is a major issue in stepping motor applications. Until today, to reduce the possibility of step loss, the motor is typically driven at maximum current level or is over-dimensioned with results in low-efficiency. Therefore in this paper, a self-learning [Formula: see text]-controller optimizing the current is presented. Moreover, to allow broad industrial applicability, this technique is computationally simple, needs no mechanical or electrical parameter knowledge and take into account the unique character of stepping motors and their conventional drive technique based on step command pulses. The proposed algorithm is validated through measurements on a hybrid stepping motor.
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