In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness.

With the expansion of application range of quadrotors, high-performance safety flight is getting more attention, where the health of actuators is critical. However, based on the commonly used loss of efficiency models, such fault-tolerant control methods are limited in performance to deal with different types of actuator faults in targeted ways. In this paper, by fully utilizing the more detailed and accurate models, the proposed adaptive robust fault-tolerant control has strong fault tolerance ability while maintaining excellent trajectory tracking accuracy. Firstly, the actuator model is established including motor dynamics and propeller model, which can reflect different types of actuator faults to the changes in different physical parameters instead of single-type efficiency factors. Additionally, aerodynamic drag is explicitly considered in quadrotor dynamics for improving control accuracy. Then, adaptive robust control is developed on these bases with comprehensive adaptation mechanism. To be specific, actuator parameters are actively estimated by recursive least square, so that actuator faults can be compensated directly in a targeted way without fault diagnosis. Subsequently, aerodynamic drag is effectively compensated through gradient-type adaptation, while the remaining uncompensated uncertainties are further suppressed by robust feedback. Finally, the comparative experiments demonstrate that the proposed method achieves much higher control accuracy than other compared methods, and it can maintain the same level of accuracy in faulty and fault-free case. Upon sudden faults, the proposed method exhibits the fastest response speed with minimal positional overshoot. Note to Practitioners —This paper aims to improve the flight safety of quadrotors while maintaining good trajectory tracking accuracy. Considering the most critical factor, i.e., actuator faults, it is usually modeled as loss of efficiency in most of literature on fault-tolerant control. However, the causes of faults are diverse, and it is not possible to effectively compensate for all kinds of faults through single-type efficiency coefficients. In fact, the occurrence of faults is associated with a change in a certain parameter of the system. Based on this, by fully utilizing the dynamics model of quadrotor and actuator, the effective online adaptive estimation algorithm is designed specifically for key actuator parameters related to faults. Therefore, more targeted compensation can be achieved for actuator faults caused by different reasons. In addition, those parameters that cannot be identified in advance but has great impact on accuracy, such as aerodynamic drag coefficients, are also explicitly considered and adaptively compensated. As for the remaining uncompensated uncertainties including external disturbances, robust feedback is introduced to ensure stability against them. The experimental results indicate that, for different types of actuator faults, the proposed method can achieve excellent trajectory tracking accuracy comparable to the fault-free cases. The proposed method can also be applied to other kinds of autonomous vehicles, such as underwater vehicles and aerospace vehicles.

With increasingly challenging applications for quadrotors, higher requirements are emerging for tracking accuracy and safety. While high accuracy is a prerequisite for complex tasks, safety is ensured through tolerance to actuator faults and resistance to external disturbances. In this article, adaptive robust control (ARC) integrated with Gaussian processes (GPs), i.e., ARC-GP, is proposed to achieve enhanced accuracy, fault tolerance, and anti-disturbance. These three requirements are interrelated and affected by uncertainties. The primary idea of this article is to categorize uncertainties into parametric and nonparametric types, which are then addressed through parameter adaptation and GP, respectively. First, a detailed dynamic model is established, including actuator models that reflect different types of faults corresponding to changes in different physical parameters. Then, parameter adaptation is designed, with direct and indirect methods adopted for different parameters. In particular, the actuator parameters are effectively estimated to achieve targeted fault compensation. Regarding GP for nonparametric uncertainties, its model parameters are also updated via parameter adaptation. The GP thereby also learns parameter estimation errors along with external disturbances. Accordingly, ARC controllers are designed, for which robust feedback terms are constructed to further mitigate uncertainties on the basis of the covariances predicted by GP. The experiments demonstrate that the proposed ARC-GP can actively tolerate various types of actuator faults and better resist wind disturbances.

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method.