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Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics

Zhe Xu, Tao Yan, Simon X. Yang, S. Andrew Gadsden, Mohammad Biglarbegian

TL;DR

This work tackles distributed formation control for multiple mobile robots operating under disturbances and unknown, unmodeled dynamics. It introduces a three-layer strategy: a cascade and variable-structure distributed estimator to recover leader states without derivative information, a bioinspired neural-dynamics backstepping-like kinematic controller to ensure smooth inputs and avoid velocity jumps, and an online learning-based robust dynamic controller for real-time parameter estimation and disturbance rejection. Stability is established via Lyapunov analysis, with two-part proofs for estimator convergence and overall closed-loop stability, and simulations demonstrate improved tracking, bounded velocity commands, and robustness compared to baseline approaches. The approach enhances scalability and real-time performance in practical robot teams, offering a principled framework for robust, distributed formation control in the presence of unknown dynamics and disturbances.

Abstract

This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. Furthermore, to address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed. This controller provides real time parameter estimates while maintaining its robustness against disturbances. The overall stability of the proposed method is proved with rigorous mathematical analysis. At last, multiple comprehensive simulation studies have shown the advantages and effectiveness of the proposed method.

Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics

TL;DR

This work tackles distributed formation control for multiple mobile robots operating under disturbances and unknown, unmodeled dynamics. It introduces a three-layer strategy: a cascade and variable-structure distributed estimator to recover leader states without derivative information, a bioinspired neural-dynamics backstepping-like kinematic controller to ensure smooth inputs and avoid velocity jumps, and an online learning-based robust dynamic controller for real-time parameter estimation and disturbance rejection. Stability is established via Lyapunov analysis, with two-part proofs for estimator convergence and overall closed-loop stability, and simulations demonstrate improved tracking, bounded velocity commands, and robustness compared to baseline approaches. The approach enhances scalability and real-time performance in practical robot teams, offering a principled framework for robust, distributed formation control in the presence of unknown dynamics and disturbances.

Abstract

This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. Furthermore, to address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed. This controller provides real time parameter estimates while maintaining its robustness against disturbances. The overall stability of the proposed method is proved with rigorous mathematical analysis. At last, multiple comprehensive simulation studies have shown the advantages and effectiveness of the proposed method.
Paper Structure (13 sections, 53 equations, 7 figures, 1 table)

This paper contains 13 sections, 53 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries and problem statement
  3. Algebraic Graph Theory
  4. Problem Formulation
  5. Formation Protocol Design
  6. Distributed Estimator Design
  7. Bioinspired Backstepping Controller Design
  8. Learning based robust controller design
  9. Stability Analysis
  10. Step 1
  11. Step 2
  12. simulation results
  13. conclusion

Figures (7)

  • Figure 1: Communication topology of mobile robots
  • Figure 2: Trajectories of each mobile robot
  • Figure 3: Tracking error of each follower
  • Figure 4: Estimation error of distributed estimators.
  • Figure 5: Velocity commands from different controllers.
  • ...and 2 more figures