Generalized Cyclic Pursuit: A Model-Reference Adaptive Control Approach
Antoine Ansart, Jyh‐Ching Juang · 2020
The paper proposes a method about sustaining the motion of a group of autonomous agents under the Generalized Cyclic Pursuit (GCP) laws. Under GCP, formation patterns can be formed by assigning eigenvalues of the system to be marginally stable. Such a control, however, is sensitive to parameter variation. In the paper, Model Reference Adaptive Control (MRAC) technique is employed to sustain the motion of agents and thus maintain the desired patterns in the presence of uncertainties. Simulation results are provided to verify the proposed approach.