PD-type iterative learning consensus control approach for an electromechanical actuator-based multiagent system

Abstract

Achieving consensus tracking control of a multiagent system (MAS) is challenging. This article proposes an innovative consensus control scheme of a MAS that is composed of electromechanical actuators. The open-loop derivative-type iterative learning control (ILC) is adopted as the baseline consensus controller. The baseline controller has systematically evolved to a proportional-derivative-type ILC to achieve better consensus tracking control for the said actuator. The proposed ILC procedure is synthesized by including the weighted sum of the tracking error as well as the tracking error-derivative variables. The respective learning gains of the aforementioned tracking error variables are pre-calibrated to ensure faster trajectory tracking with better accuracy. The PD-type ILC law strengthens the system’s disturbance resilience and improves its asymptotic convergence rate. The designed controllers are tested on two different communication topologies via simulations and reliable hardware experiments, in which the virtual leader provides the desired trajectory to four agents. Only the fixed agents interact with the leader to obtain the desired trajectory information in different communication topologies. The fixed agent guarantees accurate trajectory tracking behavior by modifying the control effort according to the deviation between its actual trajectory and the trajectories of the neighboring agents and the virtual leader. The corresponding test results indicate that the proposed PD-type ILC significantly enhances the tracking accuracy and the convergence rate of the system compared to the D-type ILC, validating the effectiveness of the proposed control scheme under different communication topologies.