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Found 7 papers, 6 papers with code
PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations
With this gym, we then present the first set of model-free, reinforcement learning algorithms for solving this series of benchmark problems, achieving stability, although at a higher cost compared to model-based PDE backstepping.
Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE
This requires an adaptive approach to PDE control, i. e., an estimation of the plant coefficients conducted concurrently with control, where a separate PDE for the gain kernel must be solved at each timestep upon the update in the plant coefficient function estimate.
Moving-Horizon Estimators for Hyperbolic and Parabolic PDEs in 1-D
In the paper we provide explicit formulae for MHEs for both hyperbolic and parabolic PDEs, as well as simulation results that illustrate theoretically guaranteed convergence of the MHEs.
Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
The recently introduced neural operators (NO) can be trained to produce the gain functions, rapidly in real time, for each state value, without requiring a PDE solution.
Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs
The designs of gains for controllers and observers for PDEs, such as PDE backstepping, are mappings of system model functions into gain functions.
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
While, in the existing PDE backstepping, finding the gain kernel requires (one offline) solution to an integral equation, the neural operator (NO) approach we propose learns the mapping from the functional coefficients of the plant PDE to the kernel function by employing a sufficiently high number of offline numerical solutions to the kernel integral equation, for a large enough number of the PDE model's different functional coefficients.
Concurrent Policy Blending and System Identification for Generalized Assistive Control
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters.
