Zihuan Cheng

For special tail sitter configurations such as the ducted fan tail sitter unmanned aerial vehicle (UAV), the widely used trajectory planning methodology based on differential flatness might not be applicable due to complex aerodynamic coupling effects. As a result, the flight mode transition remains a challenging task. In this paper, we address the time optimal altitude-hold flight mode transition issue for a class of ducted fan tail sitter UAV. The foundation of the framework is the dynamic transition corridor in which the limitation of jerk is particularly considered, aiming to thoroughly reflect the dynamic feature of aggressive maneuvers. Based on this, we propose a time optimal strategy to generate feasible altitude-hold transition trajectories. Simultaneous, by fully utilizing the manifestation of time optimal altitude-hold flight behavior revealed by the transition corridor, we try to tackle the time optimal altitude-hold transition by means of a novel model-free control scheme. Comparative simulations show that both of the transition strategies achieve satisfactory performance on time optimal altitude-hold transition in the absence of disturbance, while the model-free control scheme exhibits better robustness under external disturbance.

This paper is concerned with the attitude tracking problem of quadrotor unmanned aerial vehicles (UAVs) with respect to endogenous uncertainties, exogenous disturbances and actuator failures. Two different control methods are proposed to solve this problem. First, an adaptive extended state observer (AESO)-based control framework is devised to tackle the difficulties caused by model uncertainties and external disturbances. A fault-tolerant control method is proposed to cope with the occurrence of actuator failure, which is modeled as a constant loss of effectiveness. Another method employs AESOs to compensate for lumped disturbances, which include endogenous uncertainties, exogenous disturbances and actuator failures. Then, the error can exponentially converge to a bounded set. Finally, simulations are performed to ensure the feasibility of the designed technique.

This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.

The sea breeze is a low‐frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low‐frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator‐based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.

For agile fixed-wing aerial robots, the vertical/horizontal flight mode transition is a fundamental requirement for high maneuverability. This letter focuses on a challenging implementation in which the aircraft conducts the transitional flight at fixed altitude along a straight path. Implemented on a small ducted fan fixed-wing, we propose a transition-corridor-based control strategy to conduct the flight transition neatly. The transition corridor is described as a region comprising all the admissible flight velocities, accelerations and attitudes, bounded by the capacity of both the propulsion and control system of the vehicle. By planning the transition trajectory in obedience with the identified transition corridor, as well as adopting a unified full envelope flight control scheme, the ducted fan fixed-wing successfully accomplishes the practical transitional flight. Experimental results are satisfactory in both flight mode transition and altitude stabilization.

In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course.