Lecturer:

• Prof. Paul Van den Hof, TU/e, Eindhoven

Date of the course:  8 April 2021.

Online lectures through zoom.

The program of the 4-day School is available here.

1. 09:00 – 12:30:
   •    Closed-loop identification   recording
   •    Dynamic networks modeling   recording
2. 14:00 – 17:30:
   •    Single module identification – full MISO case   recording
   •     Network identifiability – Analysis
   •     Network identifiability – Synthesis    recording
   •     Full network identification  (not covered)
   •     Single module identification – partial input 
   •     Single module identification – local direct method
   •     Single module identifiability (not covered)
   •     Discussion and reflection

Closed-loop identification:

• Chapter 10 in Lecture notes: “System Identification – Data-driven Modeling of Dynamic Systems”, Paul M.J. Van den Hof, Version February 2020.

Dynamic networks:

• P.M.J. Van den Hof, A. Dankers, P. Heuberger and X. Bombois (2013). Identification of dynamic models in complex networks with prediction error methods – basic methods for consistent module estimates. Automatica, Vol. 49, no. 10, pp. 2994-3006.
• E.M.M. Kivits and P.M.J. Van den Hof (2018). On representations of linear dynamic networks.  IFAC PapersOnLine, Vol. 51-15, pp. 838-843. Proc. 18th IFAC Symposium on System Identification (SYSYD 2018), 9-11 July 2018, Stockholm, Sweden.

Network identifiability analysis:

• H.H.M. Weerts, P.M.J. Van den Hof and A.G. Dankers (2018). Identifiability of linear dynamic networks. Automatica, Vol. 89, pp. 247-258, March 2018.
• J. M. Hendrickx, M. Gevers and A.S. Bazanella (2019). Identifiability of dynamical networks with partial node measurements. IEEE Trans. Automatic Control, Vol. 64, no. 6, pp. 2240-2253.

Network identifiability synthesis:

• X. Cheng, S. Shi and P.M.J. Van den Hof (2022). Allocation of excitation signals for generic identifiability of linear dynamic networks. To appear in IEEE Trans. Automatic Control, Vol. 67, no. 2, February 2022.

Algorithms for full network identification:

• H.H.M. Weerts, P.M.J. Van den Hof and A.G. Dankers (2018). Prediction error identification of linear dynamic networks with rank-reduced noise. Automatica, Vol. 98, pp. 256-268, December 2018.
• H.H.M. Weerts, M. Galrinho, G. Bottegal, H. Hjalmarsson and P.M.J. Van den Hof (2018). A sequential least squares algorithm for ARMAX dynamic network identification. IFAC PapersOnLine, Vol. 51-15, pp. 844-849. Proc. 18th IFAC Symposium on System Identification (SYSYD 2018), 9-11 July 2018, Stockholm, Sweden.

Single module identification:

• A. Dankers, P.M.J. Van den Hof, X. Bombois and P.S.C. Heuberger (2016). Identification of dynamic models in complex networks with predictior error methods – predictor input selection. IEEE Trans. Automatic Control, Vol. 61, no. 4, pp. 937-952.
• K.R. Ramaswamy, G. Bottegal and P.M.J. Van den Hof (2021). Learning linear models in a dynamic network using regularized kernel-based methods. Automatica, 2021, to appear.
• K.R. Ramaswamy and P.M.J. Van den Hof (2021). A local direct method for module identification in dynamic networks with correlated noise. IEEE Trans. Automatic Control, Vol. 66, no. 11, November 2021, to appear.
• K.R. Ramaswamy, P.M.J. Van den Hof and A.G. Dankers. Generalized sensing and actuation schemes for local module identification in dynamic networks. Proc. 58th IEEE Conf. Decision and Control, Nice, France, 11-13 December 2019, pp. 5519-5524.

Single module identifiability:

• S. Shi, X. Cheng and P.M.J. Van den Hof (2020). Generic identifiability of subnetworks in a linear dynamic network: the full measurement case. ArXiv: 2008.01495. Under review.
• S. Shi, X. Cheng and P.M.J. Van den Hof (2020). Single module identifiability in linear dynamic networks with partial excitation and measurement. ArXiv: 2012.11414. Under review.

Relation with diffusively coupled physical networks:

• E.M.M. Kivits and P.M.J. Van den Hof. A dynamic network approach to identification of physical systems. Proc. 58th IEEE Conf. Decision and Control, Nice, France, 11-13 December 2019, pp. 4533-4538.