A novel passing priority allocation method, based on the collision avoidance urgency, used for a formation of UAVs in a serried obstacle laden environment, is presented in this paper. The method employs the collision cone and the prediction of the collision time to evaluate the urgency of imminent collision of each UAV, so as to establish a primary criterion for the priority allocation. And, the priority update policy is also innovative designed to alleviate burdens on the instability of frequent shifty context. This paper exploits the potential field in a cooperative way to plan the path for each UAV, steering the UAVs to a given three-dimensional position, which combines the adaptive priorities to guide the UAVs passing through the barriers in turn. Numerical simulations and semi-physical experiments in designed outdoor condition are carried out to compare the performance of the proposed adaptive priorities with the regular-used default priorities. The total passing time and the energy cost of the semi-physical tests provide the promising validation in terms of the efficiency and superiority of the proposed method.

The projectile-borne data recorder is used to measure and record the position and attitude data of exterior ballistic flights, which experience high overload during the launching process. The navigation algorithm can be optimized by analyzing the data stored in the recorder. As the primary means of acquiring the navigation data, the micro inertial measurement unit (MIMU) is an inevitable part of the projectile equipment. However, its mechanical structure could hardly bear the high overload during operation. In view of the above problems, a novel projectile-borne data recorder triggered by overload is designed in this paper. The recorder could have the navigation system powered when the projectile leaves the barrel so as to activate the data recording of the high overload. Furthermore, the viability of the MIMU of the high overload is guaranteed through a specific way of system encapsulation. In the proposed design, the overload switch redundancy and the power supply redundancy were adopted to improve reliability. The proposed design is tested with practical experiments, and the results show that the proposed recorder could be effectively triggered by overloading, and the supply voltage is stable, which helps record reliable data for the projectile.

To enhance the control performance of permanent magnet synchronous motors (PMSM) under complex operating conditions such as sudden load changes and parameter perturbations caused by strong external disturbances, this paper proposes an incremental model predictive control (IMPC) strategy based on a disturbance observer (DOB), referred to as DOB-IMPC. Firstly, an incremental predictive model of PMSM is constructed, taking advantage of its inherent integral characteristic to improve the system's robustness. Secondly, a DOB is introduced to estimate the aggregated disturbances, including load disturbances and parameter uncertainties in real time, and the estimated values are fed forward to compensate for the predictive model. Subsequently, an internal optimization process of IMPC is designed based on this model, and the optimal control input sequence is obtained by solving a constrained quadratic programming problem. For the parameter setting of the IMPC itself and the additional tuning burden introduced by the DOB, conventional approaches relying on empirical expertise and repeated trial-and-error are inefficient and cannot readily guarantee optimal control performance. This paper develops a parameter-tuning mechanism based on a multi-layer perceptron (MLP) neural network, which integrates the real-time state variables of the PMSM with the overall structural information of the DOB-IMPC framework to achieve online tuning of key control parameters. Simulation and experimental results demonstrate that, compared with conventionally manually tuned MPC, the MLP-based tuning strategy reduces the speed root mean square error (RMSE) by 46.2% and the maximum speed fluctuation by 29.0% while maintaining zero overshoot. With the further incorporation of the DOB, the maximum speed fluctuation under sudden load disturbance is further reduced by 31.2% compared with MPC, and the speed RMSE under parameter perturbation conditions is reduced by 75.8%. Collectively, these results confirm that the proposed strategy delivers measurable gains in dynamic response, disturbance rejection, and parametric resilience-without compromising stability or implementation feasibility.

The number of visible navigation satellites in a multi-constellation system increases exponentially compared to a single constellation, significantly increasing the complexity of the system state equations in the data fusion algorithm of a tightly coupled navigation system. This results in an exponential increase in the computation volume for navigation and positioning, seriously affecting real-time performance. To address this, a low-cost, low-computation satellite screening algorithm is proposed. It adaptively changes the number of constellation satellites by setting different screening thresholds and adjusting the number of remaining satellites. The algorithm then sorts and differentiates the azimuth and elevation angles of the screened satellites to form a selection strategy that divides the space region multiple times, enabling fast satellite selection. This approach greatly reduces the calculation amount while ensuring positioning accuracy. Experimental results show that the proposed algorithm maintains positioning differences within 5 meters compared to traditional satellite selection algorithms, and the time consumed for selecting a single epoch is greatly reduced. This indicates that the algorithm is suitable for low-cost multi-constellation satellite positioning systems.

This paper proposes a fast satellite screening algorithm aimed at the problem of balancing between positioning accuracy and system computing efficiency in a multi-constellation system environment under the Global Navigation Satellite System (GNSS). The algorithm constructs an observation model based on a positioning error for the larger number of satellites under a multi-constellation. The space region is divided based on elevation and azimuth angles to implement the screening algorithm for the solution of the point to be determined. An analysis of the experimental data shows that the average GDOP value of this scheme is 1.835, and the position error of the point to be determined is controlled within 2.5 m when the cut-off altitude angle is 5° and the screening ratio is more than 70%.