Generalized Cyclic Pursuit: An Estimator-Based Model-Reference Adaptive Control Approach
Antoine Ansart, Jyh‐Ching Juang · 2020
The paper proposes an estimation and control method about sustaining the motion of a group of autonomous agents under the Generalized Cyclic Pursuit (GCP) laws, where formation patterns can be formed by assigning eigenvalues of the system to be marginally stable. In the present paper, a Linear Quadratic Estimator (LQE), used to estimate the absolute position based on information exchange, is coupled with a Model Reference Adaptive Control (MRAC) to sustain the motion of agents and thus maintain the desired patterns in the presence of uncertainties and noise. Simulation results are provided to verify the proposed approach in area coverage applications.