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Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty

Emadodin Jandaghi, Mingxi Zhou, Paolo Stegagno, Chengzhi Yuan

Abstract

This paper presents a two-layer control framework for Autonomous Underwater Vehicles (AUVs) designed to handle uncertain nonlinear dynamics, including the mass matrix, previously assumed known. Unlike prior studies, this approach makes the controller independent of the robot's configuration and varying environmental conditions. The proposed framework applies across different environmental conditions affecting AUVs. It features a first-layer cooperative estimator and a second-layer decentralized deterministic learning controller. This architecture supports robust operation under diverse underwater scenarios, managing environmental effects like changes in water viscosity and flow, which impact the AUV's effective mass and damping dynamics. The first-layer estimator enables seamless inter-agent communication by sharing crucial system estimates without relying on global information. The second-layer controller uses local feedback to adjust each AUV's trajectory, ensuring accurate formation control and dynamic adaptability. Radial basis function neural networks enable local learning and knowledge storage, allowing AUVs to efficiently reapply learned dynamics after system restarts. Simulations validate the effectiveness of this framework, marking it as a significant advancement in distributed adaptive control systems for AUVs, enhancing operational flexibility and resilience in unpredictable marine environments.

Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty

Abstract

This paper presents a two-layer control framework for Autonomous Underwater Vehicles (AUVs) designed to handle uncertain nonlinear dynamics, including the mass matrix, previously assumed known. Unlike prior studies, this approach makes the controller independent of the robot's configuration and varying environmental conditions. The proposed framework applies across different environmental conditions affecting AUVs. It features a first-layer cooperative estimator and a second-layer decentralized deterministic learning controller. This architecture supports robust operation under diverse underwater scenarios, managing environmental effects like changes in water viscosity and flow, which impact the AUV's effective mass and damping dynamics. The first-layer estimator enables seamless inter-agent communication by sharing crucial system estimates without relying on global information. The second-layer controller uses local feedback to adjust each AUV's trajectory, ensuring accurate formation control and dynamic adaptability. Radial basis function neural networks enable local learning and knowledge storage, allowing AUVs to efficiently reapply learned dynamics after system restarts. Simulations validate the effectiveness of this framework, marking it as a significant advancement in distributed adaptive control systems for AUVs, enhancing operational flexibility and resilience in unpredictable marine environments.
Paper Structure (14 sections, 6 theorems, 51 equations, 4 figures, 1 table)

This paper contains 14 sections, 6 theorems, 51 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. PRELIMINARIES AND PROBLEM STATEMENT
  3. Notation and Graph Theory
  4. Radial Basis Function NNs
  5. Problem Statement
  6. two-layer distributed controller architecture
  7. First Layer: Cooperative Estimator
  8. Second Layer: Decentralized Deterministic Learning Controller
  9. Accurate learning from Formation Control
  10. Formation Control with Pre-learned Dynamics
  11. Simulation
  12. DDL Formation Learning Control Simulation
  13. Simulation for Formation Control with Pre-learned Dynamics
  14. Conclusion

Key Result

Lemma 1

Consider any continuous recurrent trajectoryA recurrent trajectory represents a large set of periodic and periodic-like trajectories generated from linear/nonlinear dynamical systems. A detailed characterization of recurrent trajectories can be found in wang2018deterministic.$Z(t) : [0, \infty) \rig

Figures (4)

  • Figure 1: Proposed two-layer distributed controller architecture for each AUVs
  • Figure 2: Network topology of multi-AUV system with 0 as virtual leader
  • Figure 5: Fomation control for all agents
  • Figure 6: $L_2$ norms of partial NN weights for AUV 3

Theorems & Definitions (16)

  • Lemma 1
  • Remark 1
  • Remark 2
  • Remark 3
  • Theorem 1
  • Remark 4
  • Theorem 2
  • Theorem 3
  • Remark 5
  • Remark 6
  • ...and 6 more