Online Seminar Series for IFAC TC 1.2 Adaptive & Learning Systems

The main objectives of our "Online Seminar Series for IFAC TC 1.2 Adaptive & Learning Systems" are:

• Promote the latest research results in the field of Adaptive and Learning Systems;
• Create a forum for high-quality discussion of both theoretical and practical perspectives;
• Reach and engage the researchers and practitioners in our community worldwide;
• Provide a platform for the young generation to network with the rest of the world.

Organization of the Seminar Series:

We plan to run the online seminar series on a monthly basis. We will invite speakers at different career stages, both within and outside of the TC, to share their research with the community. To encourage and train the young generation, e.g., PhD students. We will also organise special events within the seminars series to provide a venue for them to share and discuss their research with their peers and experts in the community.

The seminar will be open to all researchers and practitioners across the world. It will be scheduled at a suitable time (in the afternoon, UTC time) to maximise the possible attendance. The lectures will be recorded and hosted on IFAC YouTube Channel (upon the permissions of the speakers and approval of IFAC).

Details of Next Seminar

The next webinar in this series will be given by Professor John Ringwood, Maynooth University, Ireland.

Title: Energy maximising control for wave energy systems: an extremum seeking problem?
Time: December 19, 2022 (4pm UTC)

Abstract: Wave energy systems represent another category of renewable energy system with a control objective of energy maximisation. Typically, data-driven methods, including extremum-seeking control, have found good application for renewable energy systems, especially for solar PV and wind systems. For these applications, the objective function (energy converted) is directly measurable and relatively immediate control action can be taken. In wave energy systems, due to the stochastic nature of the excitation force (described by a wave spectrum), an integration interval, consisting of a significant number of pseudo wave cycles, must be employed to get a reasonable statistical measure of the converted energy, precluding the use of control action of a wave-by-wave or intra-wave basis. Nevertheless, model-based techniques, which operate on an intra-wave basis, have been shown to be clearly superior to controllers which operate on an ‘average’ wave, or wave spectrum, basis.

Despite these difficulties, there is considerable motivation to employ data-driven control techniques in the wave energy application. Physics-based hydrodynamic models, employed in model-based control designs, present a significant fidelity challenge, particularly those which might lend themselves to real-time control application, while the sensitivity properties (to modelling errors) of wave energy control systems have been shown to be challenging. This webinar will articulate the control problem for wave energy systems, focussing on aspects that both encourage use of, but also present challenges to, data-driven control, such as extremum seeking. One mitigating factor, perhaps favouring a data-driven control solution, is the presence of persistent excitation, which may preclude the requirement for additional perturbation typically associated with extremum-seeking techniques.

In the discussion period, the speaker would be more than grateful for suggestions from experts in the data-driven control community as to how this problem might be effectively approached!

To join the seminar, you may use the meeting link below:

Join Teams Meeting
Meeting ID: 341 190 311 789
Passcode: gEe2cw

We will open the meeting 15 minutes earlier. The participants must join earlier - 10 minutes before the start. It will take some time to let them all in :-)

All of you are welcome! See you there!

Tiago Roux Oliveira (Chair of the TC 1.2: Adaptive and Learning Systems)
Bing Chu  (Vice-Chair for Social Media of the TC 1.2: Adaptive and Learning Systems)

Past Webinars:

  •  Professor Martin Guay, Queen’s University. 
    • Title: Data driven control of unknown nonlinear systems using extremum seeking control
    • Time: May 27, 2022 (8am PST, 10am CDT, 3pm UTC)
    • Video:
  • Professor Tamer Basar, University of Illinois Urbana-Champaign.
    • Title: Policy Optimization for Optimal Control with Guarantees of Robustness
    • Time: April 27, 2022 (8am PST, 10am CDT, 3pm UTC)
    • Video:
  • Professor Miroslav Krstic, University of California, San Diego.
    • Title: The Magical Worlds of Adaptive Stabilization and Optimization
    • Time: March 14, 2022 (8am Pacific Daylight Time, 3pm Universal Time UTC)
    • Video: