This work designed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) and validated the controller using a simulated PX4-conducted quadcopter. To achieve this goal, this research (1) developed a dynamic mathematical model; (2) built a PX4-based simulated UAV following the model-based design process; (3) developed appropriate sliding mode control laws for each degree of freedom; (4) implemented disturbance observers on the proposed SMC controller to achieve finer disturbance rejection such as crosswind effect and other mutational disturbances; (5) optimized the SMC controller’s parameters based on particle swarm optimization (PSO) method; and (6) evaluated and compared the quadcopter’s tracking performance under a range of noise and disturbances. Comparisons of PID control strategies against the SMC were documented under the same conditions. Consequently, the SMC controller with disturbance observer facilitates accurate and fast UAV adaptation in uncertain dynamic environments.

In this study, a sliding mode surface controller (SMC) designed for a quadcopter is experimentally tested. The SMC was combined with disturbance observers in six degrees of freedom of the quadcopter to effectively reject external disturbances. While respecting stability conditions all control parameters were automatically initialized and tuned using a simulation-based offline particle swarm optimization (PSO) algorithm, followed by onboard manual fine-tuning. To demonstrate its superiority, the SMC was compared with a PSO-optimized PID controller in terms of agility, stability, and the accurate tracking of hover, rectangular, and figure-eight pattern trajectories. To evaluate its robustness, the SMC controller was extensively tested in a small, enclosed, turbulent space while being subjected to a series of external disturbances, such as hanging payloads and lateral wind.

This paper presents a robust approach for achieving accurate indoor positioning and autonomous navigation of quadcopters through the fusion of multiple radars and an inertial measurement unit (IMU) named hybrid-filtered Radar–Inertial Odometry (Hybrid-RIO). The Hybrid-RIO system integrates four-directional Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars simultaneously with a high-performance IMU to continuously estimate the quadcopters’ position, velocity, and orientation even in low-light and indoor environments. The autonomous flight commands from the system further enable indoor navigation without requiring human intervention. Experimental results reveal notable advancements in both the accuracy and consistency of positioning. The integration of the proposed Hybrid-RIO approach holds promise in a wide spectrum of domains, including cave exploration, tunnel rescue operations, and indoor navigation solutions.

This work presents and experimentally tests the framework used by our context-aware, distributed team of small Unmanned Aerial Systems (SUAS) capable of operating in real time, in an autonomous fashion, and under constrained communications. Our framework relies on a three-layered approach: (1) an operational layer, where fast temporal and narrow spatial decisions are made; (2) a tactical layer, where temporal and spatial decisions are made for a team of agents; and (3) a strategical layer, where slow temporal and wide spatial decisions are made for the team of agents. These three layers are coordinated by an ad hoc, software-defined communications network, which ensures sparse but timely delivery of messages amongst groups and teams of agents at each layer, even under constrained communications. Experimental results are presented for a team of 10 small unmanned aerial systems tasked with searching for and monitoring a person in an open area. At the operational layer, our use case presents an agent autonomously performing searching, detection, localization, classification, identification, tracking, and following of the person, while avoiding malicious collisions. At the tactical layer, our experimental use case presents the cooperative interaction of a group of multiple agents that enables the monitoring of the targeted person over wider spatial and temporal regions. At the strategic layer, our use case involves the detection of complex behaviors, i.e., the person being followed enters a car and runs away, or the person being followed exits the car and runs away, which require strategic responses to successfully accomplish the mission.