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This paper reports on the design of an open source-based control platform for the underwater remotely operated vehicle (ROV) Visor3. The vehicle’s original closed source-based control platform is first described. Due to the limitations of the previous infrastructure, modularity and flexibility are identified as the main guidelines for the proposed design. This new design includes hardware, firmware, software, and control architectures. Open-source hardware and software platforms are used for the development of the new system’s architecture, with support from the literature and the extensive experience acquired with the development of robotic exploration systems. This modular approach results in several frameworks that facilitate the functional expansion of the whole solution, the simplification of fault diagnosis and repair processes, and the reduction of development time, to mention a few.

This paper addresses the development of an equipment to teach control engineering fundamentals. The design requirements were determined by users that perform academic, research and industrial training tasks in the area of dynamic systems and process control. Such requirements include: industrial instrumentation; measurement of controlled and manipulated variables, and disturbances; process reconfigurability; different control technologies; several control strategies; appropriate materials for visualization; and compact shape to optimize lab space. The selected process is a tank system that allows one to choose among several dynamic behaviors: first, second, and third order, linear and nonlinear behavior, and dead time; the mathematical model that represents the dynamics of the system is presented. A traditional 3-stage design methodology that includes conceptual, basic and detailed design was followed. The developed equipment allows the user to select from three different technological alternatives to control the system: a PLC, an industrial controller, and a computer. With such flexibility, several control strategies can be implemented: feedback, feedforward, PID, LQG, nonlinear control (gain scheduling, sliding mode, etc.), fuzzy logic, neural networks, dynamic matrix control, etc. The developed system is being used to teach undergrad courses, grad courses, and industrial training. Additionally, the equipment is useful in research projects where grad students and researches can implement and test several advanced control techniques.