The utilization of small unmanned aerial vehicles (SUAVs), commonly known as drones, has increased drastically in various industries in the past decade. Commercial drones face challenges in terms of safety, durability, flight performance, and environmental effects such as the risk of collision and damage. Biomimetics, which is inspired by the sophisticated flying mechanisms in aerial animals, characterized by robustness and intelligence in aerodynamic performance, flight stability, and low environmental impact, may provide feasible solutions and innovativeness to drone design. In this paper, we review the recent advances in biomimetic approaches for drone development. The studies were extracted from several databases and we categorized the challenges by their purposes—namely, flight stability, flight efficiency, collision avoidance, damage mitigation, and grasping during flight. Furthermore, for each category, we summarized the achievements of current biomimetic systems and then identified their limitations. We also discuss future tasks on the research and development associated with biomimetic drones in terms of innovative design, flight control technologies, and biodiversity conservation. This paper can be used to explore new possibilities for developing biomimetic drones in industry and as a reference for necessary policy making.

In city-wide weather prediction, wind gust information can be obtained using unmanned aerial vehicles (UAVs). Although wind sensors are available, an algorithm-based active estimation can be helpful not only as a weightless substitute but also as feedback for robust control. This paper aims to estimate the wind gusts affecting the quadrotors (a type of UAV) as the input disturbances by using a frequency-based nonlinear disturbance observer (NDOB). To obtain highly accurate estimations, frequency is considered as the main design parameter, thereby focusing the estimation on the frequency range of the wind gusts. The NDOB is developed using the Takagi-Sugeno (T-S) fuzzy framework. In this approach, the twelfth-order nonlinear model is approximated into a sixth-order T-S fuzzy model to reduce computational cost. A two-step verification method is presented, which includes MATLAB/Simulink simulations and the experiments performed using a 2.5 kg quadrotor.

Sliding mode control, an algorithm known for its stability and robustness, has been widely used in designing robot controllers. Such controllers inevitably exhibit chattering; numerous methods have been proposed to deal with this problem in the past decade. However, in most scenarios, ensuring that the specified form and the parameters selected are optimal for the system is challenging. In this work, the reinforcement-learning method is adopted to explore the optimal nonlinear function to reduce chattering. Based on a conventional reference model for sliding mode control, the network output directly participates in the controller calculation without any restrictions. Additionally, a two-step verification method is proposed, including simulation under input delay and external disturbance and actual experiments using a quadrotor. Two types of classic chattering reduction methods are implemented on the same basic controller for comparison. The experiment results indicate that the proposed method could effectively reduce chattering and exhibit better tracking performance.

Rendering a high impedance on a series elastic actuator (SEA) can improve motion control performance while retaining collision safety by a lower physical stiffness. The safe impedance range can be increased with high-gain velocity feedback control, but, in practice, this is limited by noise. This article applies the noise reduction disturbance observer to inner-loop velocity control of an SEA, attenuating noise and allowing higher safe rendered stiffness compared with standard torque and torque/velocity hierarchical control. Closed-form expressions for maximum passive stiffness and $Z$ -region are found, shown to depend strongly on the high-frequency noise gain, and used to optimize the control gains. Performance is experimentally verified on a reaction-force SEA, validating the passivity of the high-stiffness control in impact (free space and stiff environment) while rendering a safe stiffness 3.0 times the intrinsic stiffness.

The rising demand for autonomous quadrotor flights across diverse applications has led to the introduction of novel control strategies, resulting in several comparative analyses and comprehensive reviews. However, existing reviews lack a comparative analysis of experimental results from published papers, resulting in verbosity. Additionally, publications featuring comparative studies often demonstrate biased comparisons by either selecting suboptimal methodologies or fine-tuning their own methods to gain an advantageous position. This review analyzes the experimental results of leading publications to identify current trends and gaps in quadrotor tracking control research. Furthermore, the analysis, accomplished through historical insights, data-driven analyses, and performance-based comparisons of published studies, distinguishes itself by objectively identifying leading controllers that have achieved outstanding performance and actual deployment across diverse applications. Crafted with the aim of assisting early-career researchers and students in gaining a comprehensive understanding, the review’s ultimate goal is to empower them to make meaningful contributions toward advancing quadrotor control technology. Lastly, this study identifies three gaps in result presentation, impeding effective comparison and decelerating progress. Currently, advanced control methodologies empower quadrotors to achieve a remarkable flight precision of 1 cm and attain flight speeds of up to 30 m/s.

In many series-elastic actuator applications, the ability to safely render a wide range of impedance is important. Advanced torque control techniques such as the disturbance observer (DOB) can improve torque tracking performance, but their impact on safe impedance range is not established. Here, safety is defined with load port passivity, and passivity conditions are developed for two variants of DOB torque control. These conditions are used to determine the maximum safe stiffness and Z-region of the DOB controllers, which are analyzed and compared with the no DOB case. A feedforward controller is proposed which increases the maximum safe stiffness of the DOB approaches. The results are experimentally validated by manual excitation and in a high-stiffness environment.