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.

Acoustic metamaterials have been proposed for numerous applications including subwavelength imaging, impedance matching, and lensing. Yet, their application in compressive sensing and imaging has not been fully investigated. When metamaterials are used as resonators at certain frequencies, they can generate random radiation patterns in the transmitted waves from the transducers and received waves from a target. Compressive sensing favors such randomness inasmuch as it can increase incoherence by decreasing the amount of mutual information between any two different measurements. This study aims at assessing whether the use of resonating metamaterial unit cells in a single-layered non-optimized array between a number of ultrasound transceivers and targets can improve the sensing capacity, point-spread function of the sensing array (their beam focusing ability), and imaging performance in point-like target detection. The theoretical results are promising and can open the way for more efficient metamaterial designs with the aim of enhancing ultrasound imaging with lower number of transceivers compared to the regular systems.

This paper presents the design, fabrication, and experimental validation of an origami-inspired reconfigurable antenna. The proposed antenna can operate as a monopole or an inverted-L antenna, by changing its configuration. Doing so changes its operational frequency, principal radiation mode, and directivity. Measurements show that the antenna is able to change its resonance frequency from 750 MHz to 920 MHz---equivalent to 22.6\% frequency shift. Simulations are carried out to compare the radiation characteristics of the antenna (gain and radiation pattern) in both configurations. The results validate that the antenna has a reconfigurable bandwidth, which enables a better channel characterization for RF systems.

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 letter presents the first experimental results of our three-dimensional (3D) millimeter-wave (mm-wave) Compressive-Reflector-Antenna (CRA) imaging system. In this prototype, the CRA is 3D-printed and coated with a metallic spray to easily introduce pseudo-random scatterers on the surface of a traditional reflector antenna (TRA). The CRA performs a pseudo random coding of the incident wavefront, thus adding spatial diversity in the imaging region and enabling the effective use of compressive sensing (CS) and imaging techniques. The CRA is fed with a multiple-input-multiple-output (MIMO) radar, which consists of four transmitting and four receiving ports. Consequently, the mechanical scanning parts and phase shifters, which are necessary in conventional physical or synthetic aperture arrays, are not needed in this system. Experimental results show the effectiveness of the prototype to perform a successful 3D reconstruction of a T-shaped metallic target.