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Distributed Adaptive Consensus with Obstacle and Collision Avoidance for Networks of Heterogeneous Multi-Agent Systems

Armel Koulong, Ali Pakniyat

TL;DR

An adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions is proposed by integrating neural network based estimation and adaptive tuning laws.

Abstract

This paper presents a distributed adaptive control strategy for multi-agent systems with heterogeneous dynamics and collision avoidance. We propose an adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions. By integrating neural network-based disturbance estimation and adaptive tuning laws, the proposed strategy ensures consensus and stability in leader-following formations under fixed topologies.

Distributed Adaptive Consensus with Obstacle and Collision Avoidance for Networks of Heterogeneous Multi-Agent Systems

TL;DR

An adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions is proposed by integrating neural network based estimation and adaptive tuning laws.

Abstract

This paper presents a distributed adaptive control strategy for multi-agent systems with heterogeneous dynamics and collision avoidance. We propose an adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions. By integrating neural network-based disturbance estimation and adaptive tuning laws, the proposed strategy ensures consensus and stability in leader-following formations under fixed topologies.

Paper Structure

This paper contains 13 sections, 4 theorems, 65 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Methodology
  4. Neural Network Learning Problem
  5. Local Synchronization Error
  6. Local Error Dynamics
  7. Weighted Stability Error
  8. Main Result
  9. Proposed Distributed Control Law
  10. Proposed NN Local Tuning Laws
  11. Proof of Theorem \ref{['thm:MainResult']}
  12. NUMERICAL EXAMPLE
  13. CONCLUSION

Key Result

Lemma 1

Under Assumption assp1, the matrix $\nu_1 L + \nu_2 B$ is nonsingular.

Figures (6)

  • Figure 1: The Considered Fixed Topology of the augmented graph $\bar{\mathcal{G}}$ in Section \ref{['NUMERICALEXAMPLE']}.
  • Figure 2: Agents Positions $s_i$, for $i \in \{1,2,3,4,5\}$ and the leader $0$.
  • Figure 3: Agents Velocities $v_i$, for $i \in \{1,2,3,4,5\}$.
  • Figure 4: Relative Position Error, $E_{i0}$, for the first state, $k=1$, between Agents and Leader.
  • Figure 5: Relative Velocity Error between Agents and Leader.
  • ...and 1 more figures

Theorems & Definitions (10)

  • Definition III.1
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Lemma 3: Graph Lyapunov Equation
  • proof
  • Theorem 1
  • proof
  • proof