It is well-known that one the most important challenges in industrial manufacturing processes is the reduction of lead-times and delays during the on-site commissioning. In applications in which several automation systems are used, such as in aeronautics or automotive manufacturing cells, several PLC's and drives are used to control the motion of the different electromechanical components. In order to verify and validate (V&V) automation code before deployment in the production environment, virtual commissioning (VC) techniques have become of great interest in the last decades. Even if VC simulation approaches, based on software/hardware-in-the-loop (SiL, HiL), have shown effectiveness to V&V automation code, the continuity from the execution to the simulation layers may not be ensured due to a lack of interoperability in the toolchain. Additionally, the development of reliable component models is a complex task of the VC solution since it requires a high level of mechatronics expertise. In order to avoid losing information between the execution and the simulation layers of VC toolchains, this paper presents a SiL based approach for interconnecting the PLC's with the VC simulation tool by means of OPC-UA-based technologies. In addition, in order to develop the functional models, we propose a library-based development. This will facilitate seamless flow of information between different PLC editors and the simulation layer. SiL simulations of the suggested approach on an automotive manufacturing cell are performed and show digital continuity from the execution to the simulation layer.

Formation control of multi-agent systems deals with groups of robots forming specific spatial geometries. Combined with the advancements of unmanned aerial vehicles (UAVs) in the past decade, formation control may potentially be applied to tasks such as search-and-rescue, surveillance, even collaborative manipulation. A key challenge is the decentralization of formation control, where each agent behaves independently using onboard sensors and computation, improving the scaleability and robustness of the system.This paper proposes a decentralized controller based on model predictive control (MPC), for the control of formations of quadrotor UAVs defined by inter-agent bearings. The use of MPC allows the controller to account for attitude kinematics, improving upon the results of existing bearing formation control methods based on rigidity and visual servoing approaches, which typically only consider the quadrotor as a single or double integrator. Furthermore the near-optimality of MPC permits a more optimal use of the quadrotors dynamic capabilities for faster maneuvering. Extensive simulations are performed to demonstrate the improved transient formation convergence and fast maneuvering permitted by this controller. Experiments show that it is indeed a real-time feasible solution for bearing formation control.

The presence of Type 2 singularities in parallel robots severely affects their performances, mainly because the platform motion control is partially lost. It also leads to a size reduction of the operational workspace. Moreover, the dynamic model of the parallel mechanism degenerates and locally, the robot becomes underactuated in the singularity. It has been proven that it is possible to cross Type 2 singularities by respecting a dynamic criterion. Nevertheless, the controllers designed up to now require a pre-planned optimized trajectory including this criterion, and as a result, this strategy can only be used by qualified users. In order to avoid this drawback and to cross these types of singularities even if the trajectory is not pre-planned, this paper proposes a controller based on virtual constraints. Furthermore, the controller is integrated in a multi-control architecture in order to switch between a classical computed torque control far from the singularity and the virtual-constraint-based control law near to the singularity locus. Experimental results on a five-bar mechanism validated the automatic Type 2 singularity crossing.