mlsanityData to 14 Oct 2026

Backstepping-Guided Reinforcement Learning for Wide-Range Saint-Venant Canal Regulation

arXiv 2608.20089 ↗First posted 20 Aug 2026math.AP cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

Backstepping control provides local stability guarantees for nonlinear Saint-Venant systems, but its regulation performance may degrade when the system operates far from the nominal equilibrium. This letter proposes a backstepping-guided soft actor-critic (SAC) controller framework that incorporates model-based control knowledge into reinforcement learning (RL). The nominal backstepping control law is first learned by deep operator network (DeepONet) and embedded into the actor and critic networks as prior informed feature representations. The learned prior is further combined with the SAC policy to generate the final control input, while a transfer-learning strategy preserves the useful backstepping knowledge during adaptation to the nonlinear dynamics. Simulation results on the Sambre River model demonstrate that the proposed method improves learning efficiency and maintains effective regulation over larger initial deviations than backstepping control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 19 references that are in the map. First posted on arXiv 20 Aug 2026.

Builds on

13 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations18 May
  2. 2024Actor-Critic Physics-informed Neural Lyapunov Control13 Mar

Other references · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  3. 2023Neural Operators for Bypassing Gain and Control Computations in PDE Backsteppingcited by 29
  4. 2023Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learningcited by 24
  5. 2023Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEscited by 18
  6. 2023Neural Operators for PDE Backstepping Control of First-Order Hyperbolic PIDE with Recycle and Delaycited by 13
  7. 2018Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Controlcited by 11
  8. 2023Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithmscited by 6
  9. 2023Online identification and control of PDEs via Reinforcement Learning methodscited by 5
  10. 2024Neural Operator Feedback for a First-Order PIDE with Spatially-Varying State Delaycited by 2
  11. 2021PI controllers for the general Saint-Venant equationscited by 1

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