Simulation-based analyses of Cyber-Physical Systems are fundamental in industrial design and testing approaches. The utility of analyses relies on the correct configuration of the simulation tools, which can be highly complicated. System engineers can normally judge the results, and either evaluate multiple simulation algorithms, or change the models. However, this is not possible in a co-simulation approach. Co-simulation is a technique to perform full-system simulation, by combining multiple black-box simulators, each responsible for a part of the system. In this paper, we demonstrate the difficulty of correctly configuring a co-simulation scenario using an industrial case study. We propose an approach to tackle this challenge by allowing multiple engineers, specialized in different domains, to encode some of their experience in the form of hints. These hints, together with state-of-the-art best practices, are then used to semi-automatically guide the configuration process of the co-simulation. We report the application of this approach to a use case proposed by our industrial partners, and discuss some of the lessons learned.

An architecture-based model-driven design approach, enabled by tools for system synthesis, simulation, and simulation content management, is exploited to efficiently explore design variants at a system level. This paper shows a case study where this design is successfully applied to an aircraft leading-edge slat system. In this case, different variants of design were investigated and compared.

This paper finds an analytical expression for the Jacobian matrix of a parallel manipulator as well as for its derivatives (with respect to time or to a change in joint angles) of any order. The most important part in the presented procedures is the closed-form expression for the velocity closure equations of the manipulator.

This paper extends previous work on (suboptimal) Kalman filter based identification of geometric uncertainties that occur during the manipulation of an object in contact with its environment. The setup consists of a force-controlled robot which has grasped a cylindrical peg; the relative position and orientation of the peg with respect to the robot end effector are only approximately known. The peg is moved over a surface, whose position and orientation are also uncertain. This paper describes how to estimate the above-mentioned uncertainties, even if some of the geometric uncertainties are instantaneously indeterminate, i.e., their influences on the force and/or velocity errors are instantaneously indistinguishable. Experimental results illustrate the theoretical background of the estimation problem.

This article presents a systematic and fully general model-based approach to identify geometrical uncertainties and to detect topological transitions in contact situations during compliant motion robot tasks. The emphasis is not on control but on modelling, online uncertainty identification and model validation (monitoring). The identification and monitoring methods presented are based on energy considerations and thus are invariant with respect to changes in the mathematical representations. Only one signal has to be monitored in order to validate the correctness of the contact model. The theoretical developments are illustrated by a real world experiment: a complete assembly of a peg into a hole. Due to the computational complexity, only off-line identification and monitoring are executed.

This paper presents a general approach for the identification of contact and grasping uncertainties, and the monitoring of contact situation changes in force controlled assembly operations. The identification problem is solved using virtual contact manipulators and Kalman filter techniques. Monitoring is solved by carrying out a statistical test on the sum of normalized and squared innovations of the Kalman filter, within a moving window, identification and monitoring are verified by experimental results. The paper explains how the error covariance matrix of the Kalman filter is interpreted to analyse the observability of the Kalman filter's states. Preliminary simulation results are presented for an ad hoc active sensing strategy to achieve complete state observability.

Most of the literature on the classical "peg-in-hole" problem concentrates on the insertion phase. In unstructured environments, however, the preceding phases of finding the hole and aligning the axes of peg and hole are equally important. This paper describes how to model the "peg-on-hole" contact situation, and how to specify the alignment motion for arbitrarily large alignment errors between the axes of peg and hole. The results are given for any radius of peg and hole. They are applied in a real world experiment, in which some uncertainties in the location of the hole are also resolved online by active force sensing.