The longitudinal altitude and airspeed coordinated control is critical for unmanned fixed-wing aircrafts, especially during the taxiing landing process However, due to the strongly coupled relation of kinetic energy and gravitational potential energy, the precision longitudinal control is difficult. The traditional longitudinal control method based on PID (Proportional Integral-Differential) usually adopts different control parameters at different flight stages. But the regulation of the control structure is complex, and some fluctuation may exist during the transition process, which will affect the control performance. The TECS (Total Energy Control System) method adopts the decoupling strategy that the throttle controls the total energy and the pitch angle controls the energy balance between kinetic and potential. However, the parameters-adjusting process is relatively complex and the adaptability to model nonlinearity is imperfect. Based on the INDI (Incremental Nonlinear Dynamic Inversion) method and the trim relation of the aircrafts, a longitudinal altitude/airspeed decoupling control method is proposed in this paper. Firstly, the process of control law design is systematically described. The core points of states balance and transition are described for the design of the method. Secondly, the feasibility and basic control effects of the control method are verified by simulations. During the simulation, a variable coefficient PI pseudo is proposed, to accommodate the requirements of accurate gliding and smooth grounding. Finally, to further verify the effectiveness and superiority of the proposed method, comparisons between the proposed method and the TECS method is carried out. From the simulation results, the in the sense of precise altitude control during auto landing process.

To enhance the robustness of the nonlinear dynamic inverse (NDI) technique in the presence of model uncertainties, this study introduces a control scheme that integrates a reinforcement learning (RL) agent with the NDI approach. Initially, a fixed-wing unmanned aerial vehicle (UAV) is selected as the primary subject of investigation, and its attitude dynamics model is established. A control system employing a nonlinear disturbance observer within a dynamic inverse framework has been developed based on this model. Subsequently, the stability of the resulting control system is verified through Lyapunov analysis. Following this, a twin delayed deep deterministic policy gradient (TD3) agent is introduced, with the closed-loop system serving as the training environment. Through continuous interaction with its surroundings, the agent learns to dynamically adjust control parameters in response to control errors. Ultimately, the trained RL agent is utilized to optimize the control parameters for the dynamic system, and a flight simulation of the fixed-wing UAV’s attitude control is conducted. The simulation results demonstrate that the control parameters can be adaptively adjusted using the TD3-NDI method, which mitigates overshoot and suppresses oscillations during the control process. These findings confirm the effectiveness and robustness of the proposed control strategy.

With high aerodynamic efficiency and low observable design for flying wing configuration, the control ability decreases and the flying risk increases, especially during the takeoff and landing processes for unmanned aerial vehicle (UAV). To solve the nonlinear characteristic problem of the flying wing configuration at a high angle of attack (AoA) for a low aspect ratio flying wing UAV, a controllable region and stability analysis method is proposed in the paper. Firstly, the aerodynamic characteristics and static stability of a flying wing UAV are analyzed. Secondly, according to the characteristics of aerodynamic data, an efficient longitudinal trim state calculation method under the low‐speed range is proposed. The trim state contour map can be used effectively with the proposed trim state calculation method, which can further analyze the constraint relationship between different trim states and the controllable region of the UAV. Thirdly, the stability of different trim states is analyzed through the phase portrait between AoA and airspeed. With the analysis result, longitudinal attitude and AoA decoupling control laws are designed based on the incremental nonlinear dynamic inversion (INDI) method. The effectiveness and adaptability of the designed control methods are verified through simulation at high AoA.

The ground taxiing phase is a crucial stage for the autonomous takeoff and landing of fixed-wing unmanned aerial vehicles (UAVs), and its trajectory tracking accuracy and stability directly determine the success of the UAV’s autonomous takeoff and landing. Therefore, researching the adaptive robust optimal control technology for UAV taxiing is of great significance for enhancing the autonomy and environmental adaptability of UAVs. This study integrates the linear quadratic regulator (LQR) with sliding mode control (SMC). A compensation control signal is generated by the SMC to mitigate the potential effects of uncertain parameters and random external disturbances, which is then added onto the LQR output to achieve a robust optimal controller. On this basis, through ANFIS (Adaptive Neuro-Fuzzy Inference System), the nonlinear mapping relationship between multiple state parameters such as speed, lateral/heading deviation and the weight matrix of the LQR controller is learned, realizing a data-driven adaptive adjustment mechanism for controller parameters to improve the tracking accuracy and anti-interference stability of the UAV’s taxiing trajectory.

Due to the uncertain nonlinear dynamics and the coupling motions of the longitudinal and lateral axes, the attitude control of flying-wing vehicles presents significant challenges. Proportional-Integral-Derivative (PID) control with gain scheduling relies on small disturbance linearization theory, which limits control performance under substantial nonlinear behavior. Nonlinear control techniques, such as Model Predictive Control (MPC) and Nonlinear Dynamic Inversion (NDI), are heavily dependent on accurate models, and their robustness diminishes when aerodynamic models are mismatched. To address these limitations, this paper proposes an attitude control algorithm based on Deep Reinforcement Learning (DRL), which considers the coupling of lateral and longitudinal motions as well as airspeed. The algorithm establishes a clear mapping between ‘State-Action-Reward’ by utilizing an error-related state space. The proposed algorithm significantly enhances training efficiency and test success rates compared to DRL algorithms that incorporate attitude angle. Furthermore, it introduces physical constraints on the control surface deflection and an additional loss function term, resulting in smooth actuation signals and attitude responses. In simulated environments, the algorithm was compared with other state-of-the-art algorithms, and the results demonstrate its strong robustness in the presence of severe turbulence disturbances and model uncertainties. The algorithm is also assessed through real vehicle experiments, which fully corroborate its effectiveness. Note to Practitioners—This paper presents a DRL-based attitude controller. This approach addresses challenges such as nonlinear dynamics, lateral-longitudinal coupling, and internal/external disturbances. We use an end-to-end learning framework to achieve simultaneous control of pitch, roll, and airspeed. To our knowledge, this is the first study to implement field flight experiments using the DDPG algorithm. Both simulations and field tests demonstrate that the proposed controller tracks attitude and waypoints effectively. Future work will focus on using real-world flight data to enable online adjustments.