The Remote Laboratory of Automatic Control tries to be a solution that helps to confront the main challenge posed by the European Space for Higher Education proposes: a Europe of Knowledge. This concept implies other challenges: knowledge mobility, European cooperation, student-centered learning, professional commitment in teaching, and intensive use of the TICs.

This group tries to confront these challenges by means of the Remote Laboratory, aiming to obtain the seven basic principles for a good education (AAHE, 1987; Chickering et al., 1996): communication with the students, teamwork and collaboration, active learning, evaluation or self-evaluation, time-on-task maximization, high expectations and respect for the diversity of the students.

The remote laboratory uses a three-tier architecture. That architecture allows for a systematic and efficient management, operation and addition of equipment, using open, flexible and non-propietary technologies for the development of the platform.

The set of systems accessible from LRA can be classified into 4 groups:

  • Pilot plants implementing real industrial processes.
  • Automation, control and monitoring devices.
  • Educational equipment.
  • Real industrial equipment.

From a teaching-learning perspective, the aims of LRA are:

  • To become a high-value resource for theoretical lessons, supporting with real-life examples the concepts of automatic control.
  • To define clearly the links between theoretical contents and their real technical implementation, and to show them through the direct access to real industrial equipment.
  • To facilitate shared use of costly physical resources and the development of laboratory networks.
  • To favor active and cooperative learning among students. 
  • To compare the technical characteristics of several expensive technologies.

From a research perspective, the platform enables working on the following research lines:

  • Remote monitoring of complex industrial processes.
  • Development of advanced monitoring tools based on machine learning and dimensionality reduction techniques.
  • Energy monitoring in buildings and industrial facilities.
  • Intelligent data analysis.
  • Critical infrastructure security and monitoring.
  • Advanced control techniques.
  • Smart grid.