Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs
This work enables faster and more efficient control design for parabolic PDEs, with potential applications in adaptive and gain scheduling control for nonlinear PDEs, though it is an incremental extension from prior work on hyperbolic PDEs.
The authors tackled the problem of designing controller and observer gains for parabolic PDEs by learning a neural operator that maps system model functions to gain functions, eliminating the need to solve PDEs for each new functional coefficient. They proved that the neural operator-generated gains maintain stability and convergence, achieving a three orders of magnitude speedup in generating gain functions compared to traditional PDE solvers.
Unlike ODEs, whose models involve system matrices and whose controllers involve vector or matrix gains, PDE models involve functions in those roles functional coefficients, dependent on the spatial variables, and gain functions dependent on space as well. The designs of gains for controllers and observers for PDEs, such as PDE backstepping, are mappings of system model functions into gain functions. These infinite dimensional nonlinear operators are given in an implicit form through PDEs, in spatial variables, which need to be solved to determine the gain function for each new functional coefficient of the PDE. The need for solving such PDEs can be eliminated by learning and approximating the said design mapping in the form of a neural operator. Learning the neural operator requires a sufficient number of prior solutions for the design PDEs, offline, as well as the training of the operator. In recent work, we developed the neural operators for PDE backstepping designs for first order hyperbolic PDEs. Here we extend this framework to the more complex class of parabolic PDEs. The key theoretical question is whether the controllers are still stabilizing, and whether the observers are still convergent, if they employ the approximate functional gains generated by the neural operator. We provide affirmative answers to these questions, namely, we prove stability in closed loop under gains produced by neural operators. We illustrate the theoretical results with numerical tests and publish our code on github. The neural operators are three orders of magnitude faster in generating gain functions than PDE solvers for such gain functions. This opens up the opportunity for the use of this neural operator methodology in adaptive control and in gain scheduling control for nonlinear PDEs.