Operator Learning for Robust Stabilization of Linear Markov-Jumping Hyperbolic PDEs
This work addresses robust control for stochastic PDE systems, with applications like freeway traffic control, but is incremental as it builds on existing backstepping and operator learning methods.
The paper tackles robust stabilization of linear hyperbolic PDEs with Markov-jumping parameter uncertainty by proposing a control law using operator learning and backstepping, achieving mean-square exponential stability under conditions of small parameter deviations and neural operator approximation errors, as validated through numerical simulations.
This paper addresses the problem of robust stabilization for linear hyperbolic Partial Differential Equations (PDEs) with Markov-jumping parameter uncertainty. We consider a 2 x 2 heterogeneous hyperbolic PDE and propose a control law using operator learning and the backstepping method. Specifically, the backstepping kernels used to construct the control law are approximated with neural operators (NO) in order to improve computational efficiency. The key challenge lies in deriving the stability conditions with respect to the Markov-jumping parameter uncertainty and NO approximation errors. The mean-square exponential stability of the stochastic system is achieved through Lyapunov analysis, indicating that the system can be stabilized if the random parameters are sufficiently close to the nominal parameters on average, and NO approximation errors are small enough. The theoretical results are applied to freeway traffic control under stochastic upstream demands and then validated through numerical simulations.