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Browse AllUnmanned aerial vehicles (UAVs) equipped with chemical contaminant sensors offer an innovative approach for rapid and safe remote chemical reconnaissance. However, the proliferation of diverse UAV platforms presents a critical challenge: how to select the optimal platform for a given mission scenario. This paper establishes a systematic suitability analysis framework for deploying chemical contaminant sensors on three mainstream UAV platforms-multirotor, fixed-wing, and vertical take-off and landing (VTOL) types. We analyze payload capacity, endurance, flight stability, environmental adaptability, and mission system integration. To reduce decision subjectivity, we develop an Analytic Hierarchy Process (AHP)-based quantitative evaluation model incorporating expert surveys and consistency tests to validate the weighting process. Beyond the evaluation framework, we explore key engineering challenges in platform-sensor integration, including aerodynamic interference, vibration and electromagnetic compatibility, and power management. We compare quantitative measurements and qualitative features across platforms in simulated mission scenarios. The resulting selection criteria provide a theoretical basis and a reproducible decision-support framework for developing chemical detection systems applicable to emergency response, environmental monitoring, and public safety.
To achieve the key control objectives of thrust regulation and inlet protection for a rocket-based combined cycle (RBCC) engine under wide flight envelope, high-speed operating conditions, this study is based on a nonlinear component-level RBCC engine model developed in MATLAB/Simulink. A state space model is established using a least squares system identification method. By incorporating actuator dynamics, an augmented system model is formulated, and a Differential Evolution (DE) optimized Linear Quadratic Regulator (LQR) multivariable closed-loop control strategy is proposed. Simulation results show that the proposed strategy significantly improves both response speed and disturbance-rejection capability. Compared with the conventional LQR controller, the maximum oscillation of the inlet stability margin is reduced from 11% to 4.4%, and the thrust settling time is shortened by 83%. These results verify the feasibility of the proposed control method under complex operating conditions.
This paper focuses on recursive estimation for stochastic systems under the round-robin (RR) protocol. In recursive estimation, it is assumed that the state evolution process of the signal is unknown, and the relevant estimation research is only carried out relying on the mean and covariance functions of the process. To effectively avoid the occurrence of transmission channel conflicts and ensure the fairness of data allocation during transmission, this paper chooses to adopt the RR protocol to schedule and manage data communication. Specifically, this scheduling rule stipulates that only one sensor is allowed to acquire the network communication opportunity within a fixed time period, and the entire scheduling process is executed cyclically in a preset fixed order. Based on the observable information available in the system, this paper further designs and proposes a recursive estimation algorithm by means of the innovation method. Finally, a numerical simulation experiment is given to verify the effectiveness and feasibility of the proposed estimation algorithm.
As green development has become a global unified strategy, major developed countries in the world have successively announced their ambition to achieve carbon neutrality in this century. The new energy vehicle and traction battery industry has ushered in a high-quality development moment, and the safety of traction batteries has also been widely valued worldwide. As the traction battery is mainly arranged at the bottom of the vehicle, enhancing the operational safety of new energy vehicles necessitates a breakthrough in performance testing for bottom-impact protection. It is essential to develop a robust evaluation methodology that can accurately and efficiently evaluate the extent of damage inflicted on the battery system following a bottom impact or collision. Based on the bottom impact experiment of a typical traction battery pack, this paper studies the damage morphology and damage mechanism of the bottom impact. By integrating finite element mechanical simulations, it provides a detailed analysis of the evolving mechanical states across different zones of the battery pack during the impact event. The findings contribute to enhancing safety protection strategies for traction batteries in bottom impact scenarios and offer crucial support for evaluating the post-impact safety performance of new energy vehicles.