Stabilization of a Class of Large-Scale Systems of Linear Hyperbolic PDEs via Continuum Approximation of Exact Backstepping Kernels (2403.19455v2)
Abstract: We establish that stabilization of a class of linear, hyperbolic partial differential equations (PDEs) with a large (nevertheless finite) number of components, can be achieved via employment of a backstepping-based control law, which is constructed for stabilization of a continuum version (i.e., as the number of components tends to infinity) of the PDE system. This is achieved by proving that the exact backstepping kernels, constructed for stabilization of the large-scale system, can be approximated (in certain sense such that exponential stability is preserved) by the backstepping kernels constructed for stabilization of a continuum version (essentially an infinite ensemble) of the original PDE system. The proof relies on construction of a convergent sequence of backstepping kernels that is defined such that each kernel matches the exact backstepping kernels (derived based on the original, large-scale system), in a piecewise constant manner with respect to an ensemble variable; while showing that they satisfy the continuum backstepping kernel equations. We present a numerical example that reveals that complexity of computation of stabilizing backstepping kernels may not scale with the number of components of the PDE state, when the kernels are constructed on the basis of the continuum version, in contrast to the case in which they are constructed on the basis of the original, large-scale system. In addition, we formally establish the connection between the solutions to the large-scale system and its continuum counterpart. Thus, this approach can be useful for design of computationally tractable, stabilizing backstepping-based control laws for large-scale PDE systems.
- V. Bikia, “Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence,” Ph.D. dissertation, EPFL, 2021.
- P. Reymond, F. Merenda, F. Perren, D. Rufenacht, and N. Stergiopulos, “Validation of a one-dimensional model of the systemic arterial tree,” Am. J. Physiol. Heart Circ. Physiol., vol. 297, pp. H208–H222, 2009.
- L. Guan, C. Prieur, L. Zhang, C. Prieur, D. Georges, and P. Bellemain, “Transport effect of COVID-19 pandemic in France,” Annu. Rev. Control, vol. 50, pp. 394–408, 2020.
- J. Friedrich, S. Göttlich, and M. Osztfalk, “Network models for nonlocal traffic flow,” ESAIM Math. Model. Numer. Anal., vol. 56, no. 1, pp. 213–235, 2022.
- L. Zhang, H. Luan, Y. Lu, and C. Prieur, “Boundary feedback stabilization of freeway trafficnetworks: ISS control and experiments,” IEEE Trans. Control Syst. Technol., vol. 30, pp. 997–1008, 2022.
- L. Tumash, C. Canudas-de-Wit, and M. L. Delle Monache, “Multi-directional continuous traffic model for large-scale urban networks,” Transportation Research Part B: Methodological, vol. 158, pp. 374–402, 2022.
- J. Auriol and D. Bresch-Pietri, “Robust state-feedback stabilization of an underactuated network of interconnected n+m𝑛𝑚n+mitalic_n + italic_m hyperbolic PDE systems,” Automatica, vol. 136, p. 110040, 2022.
- J. Auriol, “Output feedback stabilization of an underactuated cascade network of interconnected linear PDE systems using a backstepping approach,” Automatica, vol. 117, p. 108964, 2020.
- L. Hu, F. Di Meglio, R. Vazquez, and M. Krstic, “Control of homodirectional and general heterodirectional linear coupled hyperbolic PDEs,” IEEE Trans. Automat. Control, vol. 61, no. 11, pp. 3301–3314, 2016.
- J. Redaud, J. Auriol, and S.-I. Niculescu, “Output-feedback control of an underactuated network of interconnected hyperbolic PDE-ODE systems,” Systems Control Lett., vol. 154, p. 104984, 2021.
- F. Di Meglio, R. Vazquez, and M. Krstic, “Stabilization of a system of n+1𝑛1n+1italic_n + 1 coupled first-order hyperbolic linear PDEs with a single boundary input,” IEEE Trans. Automat. Control, vol. 58, no. 12, pp. 3097–3111, 2013.
- T. Enderes, J. Gabriel, and J. Deutscher, “Cooperative output regulation for networks of hyperbolic systems using adaptive cooperative observers,” Automatica, vol. 162, p. 111506, 2024.
- L. Paunonen and J.-P. Humaloja, “On robust regulation of PDEs: from abstract methods to PDE controllers,” in IEEE Conference on Decision and Control, 2022, pp. 7352–7357.
- V. D. Blondel, J. M. Hendrickx, and J. N. Tsitsiklis, “On Krause’s multi-agent consensus model with state-dependent connectivity,” IEEE Trans. Automat. Control, vol. 54, no. 11, pp. 2586–2597, 2009.
- G. Ferrari-Trecate, A. Buffa, and M. Gati, “Analysis of coordinations in multi-agent systems through partial difference equations,” IEEE Trans. Automat. Control, vol. 51, no. 6, pp. 1058–1063, 2006.
- T. Meurer and M. Krstic, “Finite-time multi-agent deployment: A nonlinear PDE motion planning approach,” Automatica, vol. 37, pp. 2534–2542, 2011.
- D. Nikitin, C. Canudas-de-Wit, and P. Frasca, “A continuation method for large-scale modeling and control: from ODEs to PDE, a round trip,” IEEE Trans. Automat. Control, vol. 67, pp. 5118–5133, 2022.
- J. Wei, E. Fridman, and K. H. Johansson, “A PDE approach to deployment of mobile agents under leader relative position measurements,” Automatica, vol. 106, pp. 47–53, 2019.
- J. Qi, R. Vazquez, and M. Krstic, “Multi-agent deployment in 3-D via PDE control,” IEEE Trans. Automat. Control, vol. 60, no. 4, pp. 891–906, 2015.
- P. Frihauf and M. Krstic, “Leader-enabled deployment into planar curves: A PDE-based approach,” IEEE Trans. Automat. Control, vol. 56, pp. 1791–1806, 2011.
- J. Zhang, R. Vazquez, J. Qi, and M. Krstic, “Multi-agent deployment in 3-D via reaction-diffusion system with radially-varying reaction,” Automatica, vol. 161, p. 111491, 2024.
- L. Bhan, Y. Shi, and M. Krstic, “Neural operators for bypassing gain and control computations in PDE backstepping,” IEEE Trans. Automat. Control, 2024, early access.
- J. Auriol, K. A. Morris, and F. Di Meglio, “Late-lumping backstepping control of partial differential equations,” Automatica, vol. 100, pp. 247–259, 2019.
- R. Vazquez, G. Chen, J.-F. Qiao, and M. Krstic, “The power series method to compute backstepping kernel gains: Theory and practice,” in IEEE Conference on Decision and Control, 2023.
- V. Alleaume and M. Krstic, “Ensembles of hyperbolic PDEs: stabilization by backstepping,” arXiv, 2307.13195, 2023.
- I. Atamas, S. Dashkovskiy, and V. Slynko, “Lyapunov functions for linear hyperbolic systems,” IEEE Trans. Automat. Control, vol. 68, pp. 6496–6508, 2023.
- A. Terrand-Jeanne, V. Andrieu, V. Dos Santos, and C.-Z. Xu, “Adding integral action for open-loop exponentially stable semigroups and application to boundary control of PDE systems,” IEEE Trans. Automat. Control, vol. 65, no. 11, pp. 4481–4492, 2020.
- R. Vazquez and M. Krstic, “Marcum Q-functions and explicit kernels for stabilization of 2×2222\times 22 × 2 linear hyperbolic systems with constant coefficients,” Syst. Control Lett., vol. 68, pp. 33–42, 2014.
- I. Farmaga, P. Shmigelskyi, P. Spiewak, and L. Ciupinski, “Evaluation of computational complexity of finite element analysis,” in The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2011, pp. 213–214.
- J.-M. Coron, R. Vazquez, M. Krstic, and G. Bastin, “Local exponential H2superscript𝐻2H^{2}italic_H start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT stabilization of a 2×2222\times 22 × 2 quasilinear hyperbolic system using backstepping,” SIAM J. Control Optim., vol. 51, no. 3, pp. 2005–2035, 2013.