In this paper, the problem of actuator and sensor faults of a quadrotor unmanned aerial vehicle (QUAV) system is studied. In the system fault model, time delay, nonlinear term, and disturbances of QUAV during the flight are considered. A fault estimation algorithm based on an intermediate observer is proposed. To deal with a single actuator fault, an intermediate variable is introduced, and the intermediate observer is designed for the system to estimate fault. For simultaneous actuator and sensor faults, the system is first augmented, and then two intermediate variables are introduced, and an intermediate observer is designed for the augmented system to estimate the system state, faults, and disturbances. The Lyapunov–Krasovskii functional is used to prove that the estimation error system is uniformly eventually bounded. The simulation results verify the feasibility and effectiveness of the proposed fault estimation method.

Ultra-wideband (UWB) technology has been widely used for indoor positioning of UAVs due to its excellent range performance. The traditional UWB positioning system requires at least three anchors to complete 3D positioning. Reducing the number of anchors further means reducing the cost and difficulty of deployment. Therefore, this paper proposes a positioning model using only two anchors. In this model, the altitude of the UAV is measured by a rangefinder. Then, the position of the UAV is projected onto the horizontal plane, converting 3D positioning into 2D positioning. The rangefinder’s range accuracy is higher than that of the UWB, which is beneficial for improving 3D positioning accuracy. In addition, an altitude fusion method of integrating rangefinder and barometer data is designed to realize the switching of altitude data and barometer calibration to solve the problem of obstacles under the UAV affecting the altitude measurement. On this basis, the multi-sensor data fusion algorithm based on a dual-anchor positioning model is designed to improve positioning accuracy, and the data of the UWB, rangefinder, barometer, and accelerometer are fused by the unscented Kalman filter (UKF) algorithm. The positioning simulation and experiment show that the positioning accuracy of the dual-anchor model is generally higher than that of the three-anchor model, with decimeter-level positioning accuracy.

In this paper, an adaptive iterative learning control (AILC) law is developed for two-link rigid-flexible coupled manipulator system in three-dimensional (3D) space with time-varying disturbances and input constraints. Based on the Hamilton’s principle, a dynamic model of a manipulator system is established. The conditional equation that is coupled by ordinary differential equations and partial differential equations is derived. In order to achieve high-precision tracking of the revolving angles and vibration suppression of the elastic part, the iterative learning control law based on the disturbance observer is considered in the process of the design controller. The composite Lyapunov energy function is proposed to prove that the angle errors and elastic deformation can eventually converge to zero with the increase of the number of iterations. Ultimately, the simulation results to rigid-flexible coupled manipulator system are given to prove the convergence of the control objectives under the adaptive iterative learning control law.

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO_x emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

Accurate altitude estimation is critical for unmanned aerial vehicles (UAVs), yet barometric measurements are susceptible to atmospheric drift and dynamic disturbances. To address these limitations, this paper proposes a dual-layer, real-time differential barometric altimetry framework that integrates a ground reference station with an onboard fusion scheme based on Adaptive Weighted Averaging (AWA) and an Adaptive Extended Information Filter (AEIF). The ground reference station suppresses low-frequency atmospheric variations, while the onboard AEIF incorporates a physical pressure–height model and adaptive noise estimation to maintain a fast dynamic response. The proposed method is validated through numerical simulations, hardware-in-the-loop (HIL) experiments, and real flight tests. In a two-hour outdoor flight test, compared with barometric systems operating without a reference station, the proposed approach reduces the altitude RMSE from 4.05 m to 0.31 m, achieving an approximately order-of-magnitude improvement in representative scenarios and demonstrating decimeter-level altitude measurement accuracy.