Carleman linearization

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Short description: Mathematical transformation technique

In mathematics, Carleman linearization (or Carleman embedding) is a technique to transform a finite-dimensional nonlinear dynamical system into an infinite-dimensional linear system. It was introduced by the Swedish mathematician Torsten Carleman in 1932.[1] Carleman linearization is related to composition operator and has been widely used in the study of dynamical systems. It also been used in many applied fields, such as in control theory[2][3] and in quantum computing.[4][5]

Procedure

Consider the following autonomous nonlinear system:

x˙=f(x)+j=1mgj(x)dj(t)

where xRn denotes the system state vector. Also, f and gi's are known analytic vector functions, and dj is the jth element of an unknown disturbance to the system.

At the desired nominal point, the nonlinear functions in the above system can be approximated by Taylor expansion

f(x)f(x0)+k=1η1k!f[k]x=x0(xx0)[k]

where f[k]x=x0 is the kth partial derivative of f(x) with respect to x at x=x0 and x[k] denotes the kth Kronecker product.

Without loss of generality, we assume that x0 is at the origin.

Applying Taylor approximation to the system, we obtain

x˙k=0ηAkx[k]+j=1mk=0ηBjkx[k]dj

where Ak=1k!f[k]x=0 and Bjk=1k!gj[k]x=0.

Consequently, the following linear system for higher orders of the original states are obtained:

d(x[i])dtk=0ηi+1Ai,kx[k+i1]+j=1mk=0ηi+1Bj,i,kx[k+i1]dj

where Ai,k=l=0i1In[l]AkIn[i1l], and similarly Bj,i,κ=l=0i1In[l]Bj,κIn[i1l].

Employing Kronecker product operator, the approximated system is presented in the following form

x˙Ax+j=1m[Bjxdj+Bj0dj]+Ar

where x=[xTx[2]T...x[η]T]T, and A,Bj,Ar and Bj,0 matrices are defined in (Hashemian and Armaou 2015).[6]

See also

References

  1. Carleman, Torsten (1932). "Application de la théorie des équations intégrales linéaires aux systèmes d'équations différentielles non linéaires" (in en). Acta Mathematica 59: 63–87. doi:10.1007/BF02546499. ISSN 0001-5962. http://projecteuclid.org/euclid.acta/1485887967. 
  2. Salazar-Caceres, Fabian; Tellez-Castro, Duvan; Mojica-Nava, Eduardo (2017). "Consensus for multi-agent nonlinear systems: A Carleman approximation approach". 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC). Cartagena: IEEE. pp. 1–5. doi:10.1109/CCAC.2017.8276388. ISBN 978-1-5386-0398-7. https://ieeexplore.ieee.org/document/8276388. 
  3. Amini, Arash; Sun, Qiyu; Motee, Nader (2020). "Approximate Optimal Control Design for a Class of Nonlinear Systems by Lifting Hamilton-Jacobi-Bellman Equation". 2020 American Control Conference (ACC). Denver, CO, USA: IEEE. pp. 2717–2722. doi:10.23919/ACC45564.2020.9147576. ISBN 978-1-5386-8266-1. https://ieeexplore.ieee.org/document/9147576. 
  4. Liu, Jin-Peng; Kolden, Herman Øie; Krovi, Hari K.; Loureiro, Nuno F.; Trivisa, Konstantina; Childs, Andrew M. (2021-08-31). "Efficient quantum algorithm for dissipative nonlinear differential equations" (in en). Proceedings of the National Academy of Sciences 118 (35): e2026805118. doi:10.1073/pnas.2026805118. ISSN 0027-8424. PMID 34446548. Bibcode2021PNAS..11826805L. 
  5. Levy, Max G. (January 5, 2021). "New Quantum Algorithms Finally Crack Nonlinear Equations". https://www.quantamagazine.org/new-quantum-algorithms-finally-crack-nonlinear-equations-20210105/. 
  6. Hashemian, N.; Armaou, A. (2015). "Fast Moving Horizon Estimation of nonlinear processes via Carleman linearization". 2015 American Control Conference (ACC). pp. 3379–3385. doi:10.1109/ACC.2015.7171854. ISBN 978-1-4799-8684-2.