A fuzzy logic-based adaptive strictly negative-imaginary formation controller for multi-quadrotor systems

Multi-robot systems have played essential roles in both civilian and military domains. The task assigned to the robotic systems has become more complicated and time-consuming to be carried out by a single robot. To reduce the costs and to improve the robustness and the efficacy of the system, formation control approaches for networked robots are highly desirable e.g., for a team of Unmanned Aerial Vehicles (UAVs). However, current state-of-the-art algorithms are faced with several challenging issues due to the presence of uncertainties such as wind gusts. Since traditional PID controllers or other fixed-gain control systems may not lead to satisfactory outcomes, we present a novel adaptive Strictly Negative Imaginary (SNI) formation control strategy based on knowledge-based Fuzzy Inference Systems to deal with the challenging dynamics of flight environments. To facilitate the information sharing process, a decentralised control architecture is designed. Each UAV only needs to measure its relative position with respect to its neighbours with a sensor such as a laser range-finder or machine vision system. Consequently, the required formation is formed by maintaining the desired relative positions between UAVs. We rigorously compare the performance of our adaptive SNI control systems with respect to the effectiveness of the conventional PID controllers. Our research highlights the efficacy of the proposed adaptive control systems, not only using numerical simulations, but also by conducting real-time flight tests.