Intelligent systems

Research focused on adaptive and distributed embedded and integrated systems for intelligent infrastructures to enhance electrification, decentralisation and digitalisation.

Keywords and applications

Keywords

Distributed embedded systems; intelligent infrastructures; IoT/M2M communication; distributed optimisation and decision-making

 

Applications
Electric vehicle charging infrastructures; digitalisation of energy systems, etc.

Description

The cyber-physical systems lab is a laboratory containing a set of physical apparatus (mostly electro-mechanical systems) or experiments equipped with a set of actuators and sensors, all linked to computers so that machine intelligence algorithms can be tested and evaluated. The implementation of these algorithms, whether targeted towards control, monitoring or fault diagnosis, can be done with current tools for rapid prototyping such as Matlab/Simulink. It is also possible to control some of the experiments using low cost embedded boards like Arduino, Raspberry pi and Beaglebone. Design and implementation of the controller can be done in Matlab/Simulink as well.

Recently, we have been working on the implementation and challenges of Networked Control Systems (NCSs). NCSs have attracted intense attention from both academia and industry due to the multidisciplinary nature among the areas of communication networks, computer science and control. Other than the NCSs where feedback control loops are closed via communication networks, the more advanced case of distributed NCSs where many control loops are in contact, is implemented in the lab. In this regard, we have developed a distributed networked control system where all nodes are raspberry pi boards, and they communicate to each other and to the server over internet. All code generation is done in Matlab/Simulink and then deployed to the boards over internet.

Services for companies

  • Test of different classes of algorithms (control, estimation, fault-diagnosis, system identification, motion planning, reinforcement learning) and their comparison in terms of performance
  • Continuing education and training throughout tailored courses of advanced techniques and hands-on experiments using Matlab/Simulink. 

Technical specification of equipment

The set of experiments includes

  • Multidimensional system for torsional dynamics. Experiment to test control algorithms reducing oscillatory behaviour on motor systems with important inertia and elasticity (loop shaping)
  •  Rotary flexible link. Used for advanced motion planning, feedforward and estimation and networked control systems
  • Combination of 2-3 rotary flexible links: Used for networked control, distributed control and application of IoT principles
  • Coupled process control experiment. Ideal experiment to test/learn nonlinear control, nonlinear identification and fault diagnosis through the generation of faults
  • Heat flow experiment. Test of active machine learning algorithms in the area of ventilation and HVAC systems
  • 3D crane and tower crane. Experiment to test motion planning and accurate position in motion control systems
  • 6 degreed-of-freedom joint compliant control robot. Fully accessible robot to implement different control algorithms, including compliant ones.

Contact

M. Hossein Ramezani
Assistant Professor
SDU Mechatronics
Mads Clausen Institute

 

T +45 6550 9217
ramezani@mci.sdu.dk

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