5 papers
DeepONet-LSTM Neural Operator for Output Feedback Control of Reaction Diffusion PDEs
Jing Zhang, Jie Qi, Linglong Jiang
This paper presents a neural operator-based approach for the output feedback boundary stabilization of reaction diffusion PDEs. The classical output feedback backstepping design re…
Backstepping-Guided Reinforcement Learning for Wide-Range Saint-Venant Canal Regulation
Chenchen Wang, Jie Qi
Backstepping control provides local stability guarantees for nonlinear Saint-Venant systems, but its regulation performance may degrade when the system operates far from the nomina…
Soft Actor-Critic with Backstepping-Pretrained DeepONet for control of PDEs
Chenchen Wang, Jie Qi, Jiaqi Hu
This paper develops a reinforcement learning-based controller for the stabilization of partial differential equation (PDE) systems. Within the soft actor-critic (SAC) framework, we…
Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State Delay
Jiaqi Hu, Jie Qi, Jing Zhang
Control of distributed parameter systems affected by delays is a challenging task, particularly when the delays depend on spatial variables. The idea of integrating analytical cont…
Neural Operator Feedback for a First-Order PIDE with Spatially-Varying State Delay
Jie Qi, Jiaqi Hu, Jing Zhang +1
A transport PDE with a spatial integral and recirculation with constant delay has been a benchmark for neural operator approximations of PDE backstepping controllers. Introducing a…