The task space control of robot manipulators requires solving the thorny problem of stabilizing the compound system {outer controller - inner controller - robot manipulator}. To stabilize this compound system, both controllers must be designed by the user to achieve convergence of the tracking error. This problem is tricky to solve in the case of the control of an industrial robot manipulator because its internal controller is not accessible to users. In this work, we propose an adaptive neural network outer controller. The neural networks approximate the dynamics of the inner controller, the kinematic and dynamic parameters of the robot. Besides, the adaptive part finds parameters that achieve the stability of the global system. Since an adaptive approach is sensitive to errors in initial values, we have integrated into the controller a term that constrains the closed-loop system to maintain the prescribed performances. The effectiveness of the approach is demonstrated through Lyapunov's theory, simulation comparisons, and experimental studies.

In this paper, we consider the European Rail Traffic Management System (ERTMS) as a System-of-Systems (SoS) and propose modeling it using colored Petri nets. We formally control the European rail transport, while guaranteeing a set of cross-border security properties. This becomes an essential and challenging task since each of them have mainly developed safety and trackside rules regardless of its neighbors. The feature of this work lies in the approach that considers ERTMS Level 2 as an SoS and addresses the cross-border railway as a mode management problem. In addition, the aspects of mode activation/deactivation, starting state and handling of resource states common to multiple operating modes are taken into account in the proposed model.

ENASE 2019, 14th International Conference on Evaluation of Novel Approaches to Software Engineering, Heraklion, GRECE, 04-/05/2019 - 05/05/2019

ICSOFT 2018, 13th International Conference on Software Technologies, Porto, PORTUGAL, 26-/07/2018 - 28/07/2018

The aim of this work is to propose a formal model for determining the dynamic optimal path for transfer operations on an oil flow network within real conditions of exploitation such as maintenance activities and alignments in-service. The study focuses on a multi-objective problem to search a safe and short path to the oil transfer operations. It is based on, firstly, dynamic reliability, that depends on time and conditions of use, of engaged valves in the path; and secondly, pipes lengths; and it is developed in a High Level Petri Nets (HLPN) framework. Real operational constraints and goals in the seaport are modelled with data extracted from OREDA Database. The results show how the proposed solution leads to a safer use of the network by avoiding riskiest valves and conflicts between oil transfer operations and between transfer operations and maintenance tasks. This contribution is applied in the oil transport, but it is potentially applicable in many other areas.