Viktor Just

Model-Based Design of Mechatronic Systems by Means of Semantic Web Ontologies and Reusable Solution Elements

When designing complex mechatronic systems, a team of developers will be facing many challenges that can impede progress and innovation if not tackled properly. In meeting them simulation tools play a central role. Yet it is often impossible for a single developer to foresee the overall impact a design decision will have on the system and on the other domains involved. For this task multi-domain simulation tools exists, but because of its complexity and the different levels of detail that are needed, the effort to specify a complete system from scratch is very high. Another challenge is the selection of the most suitable solution elements provided by the manufacturers. Currently they are often chosen manually from catalogues. The development engineer is therefore usually inclined to employ well-known solution elements and suppliers. To tackle both challenges our aim is an increase in efficiency and innovation by means of generally available solution knowledge, such as well-proven solution patterns, ready-to-use solution elements, and established simulation models [1].Our paper presents a tool-supported, sequential design process. From the outset, the comprehensive functional capability of the designed system is supervised by means of multi-domain simulation. At significant points in the design process, solution knowledge can be accessed as it is stored in ontologies and therefore available via Semantic Web [2]. Thus, one can overcome barriers resulting from different terminologies or referential systems and furthermore infer further knowledge from the stored knowledge. The paper focuses on an early testing in the conceptual design stage and on the subsequent semantic search for suitable solution elements. After the specification of a principle solution for the mechatronic system by combining solution patterns, an initial multi-domain model of the system is created. This is done on the basis of the active structure and of idealized simulation models which are part of a free library and associated with the chosen solution patterns via the ontologies. In further designing the controlled system and its parameters with the completed model, the developer defines additional criteria to be fed into the subsequent semantic search for solution elements. Information on the latter is provided by the manufacturers as well as detailed simulation models, which are used to analyze the functional capability of the concretized system. Therefore, the corresponding idealized models are replaced automatically with the parameterized models of the solution elements containing for example the specific friction model for the chosen motor. We show this process using the concrete example of a dough-production system. In particular, we focus on its transport system. Resulting requirements for the simulation models and their level of detail are expound, as well as the architecture and benefits of the ontologies.Copyright

Design and control of cooperative ball juggling DELTA robots without visual guidance

Cooperative ball juggling is one of the most difficult tasks when performed through autonomous robots. States of the ball (position and velocity) play a vital role for the stability and duration of a long rally. Cameras are normally used in ball juggling to calculate these parameters, the use of which is not only computationally expensive but also requires a lot of hardware to determine. In this paper, we propose a control loop for cooperative ball juggling using parallel DELTA robots without visual guidance. In contrast to using a visual system for ball states feedback, an observer based on the reflection laws is designed to calculate the continuous position and velocity of the ball during juggling. Besides the conventional controller blocks, the proposed control loop consists of the ball prediction and the plate striking movement generation blocks. Two controllers are designed for the stability and tracking of variable reference height of the ball during juggling: One controller calculates the velocity of the striking plate to achieve the reference height of the ball during juggling and the second controls the actuator angles. A simulation study and hardware experiments show applicability of the designed observer and validation of the proposed control loop.

Dynamical model of ball juggling Delta robots using reflection laws

In this paper, the dynamical model of the ball juggling robot is presented. The dynamical model takes advantage of the fact that instead of using the camera or vision system, this dynamical model can be used to continuously calculate ball velocity and position during juggling or playing between two robots. In ball juggling or playing robot experiments the most difficult task is to get the position and velocity of the ball during play. This paper deals with calculating the position and velocity of the ball continuously during juggling or playing with the rigid racket. Basic physics reflection laws are used to calculate the outgoing velocity of the ball after each hit. The hitting of the ball on the racket is detected by measuring the distance between the ball contact point and the rigid racket surface. To get the velocity and position of the ball throughout the juggling, the gravitational effect is also incorporated. An overall structure of the stand alone model is also proposed to get the position and velocity continuously. This model is computationally less expensive and gives better insight of the juggling. To validate this dynamical model, experiments are performed on the test bench. The results of this dynamical model are compared and analysed with the results obtained from the RecurDyn simulation and test bench experiment.

Efficient Design of Complex Mechatronic Products Using the Example of the Separation-Process in an ATM

The substantial challenge with designing mechatronic systems is the fully inclusive consideration of the system already in the early development phases. On designing mechatronic systems the conception phase is directly followed by the “Mechatronic Composition” [1]. In this contribution the mechatronic composition is subdivided into three phases: “Basic-System Composition”, “Idealized Composition” and “Holistic Composition”. The model-based analysis and synthesis provide the basis for the mechatronic composition. Beside the general procedure at modelling mechatronic systems the contribution discusses the adequate depth of the modelling considering the model validation. In the second part of the contribution the application of the three-phase Mechatronic Composition is represented by the advancement of the separator module of an ATM.Copyright

Consistency analysis for requirements, functions, and system elements: Requirements for the entire development process

Tomorrows systems will be based on close interactions of mechanics, electrics/electronics, control engineering, software technology or new materials, as well as possessing inherent intelligence that will make them superior to mechatronics. Their main features are adaptability, robustness, and proactivity. Intelligent systems are multidisciplinary and therefore, they need to be developed in a discipline-spanning manner. Two targets arising from this are on the one hand a consistent superordinate process model, and on the other hand an appropriate support for this process model with sufficient methods. One step towards reaching those targets is more formalization in systems engineering for traditional engineering. A systematic use of different requirement levels in a given development process is displayed in this contribution. It is shown that, when interpreted in the right way, requirements provide one option to interconnect the different phases inside this development process. Four levels of requirements are defined and allocated to a development process. For a process models applicability, it is beneficial to provide supporting methods. We discuss certain methods for the different development phases of the V-model. Starting with goals of the development, the evolution from goals towards functions and systems is described via enriched partial models, which provide an early description of the system behavior. The interactions of the partial models with the requirement levels are described to increase consistency between requirements, functions and system elements. A benefit emerging with this is the advantageous traceability of requirements. To formalize requirements connections to the system, an analysis method is presented, which quantifies connectivity of each element, as well as the degree of connections inside the entire system. Hence, the possibilities of examining the connections between requirements, goals and system elements are expanded.

Design and Control of a Vertical Ball Juggling Delta Robot Without Visual Guidance

In this paper, the design and control of a vertical ball juggling Delta robot is presented. The position and velocity of the ball, factors play an important role during juggling. Quite often these factors are calculated using visual guidance. This paper introduces a control algorithm to juggle the ball vertically in two dimensions (2D) without visual guidance method. Instead of normal visual guidance method, an observer based on the reflection laws is used to get the continuous position and velocity of the ball. The next hitting time and the hitting velocity of the ball are predicted using the projectile motion equations. Three different controllers are designed for the stability and tracking of variable reference height of the ball during juggling and to keep the ball from falling off. The validation of this proposed control algorithm for ball juggling is shown by the simulation and preliminary experimental results.