In recent years, multirotor unmanned aerial vehicles (UAVs) have become more and more important in the field of plant protection in China. Multirotor unmanned plant protection UAVs have been widely used in vast plains, hills, mountains, and other regions, and become an integral part of China’s agricultural mechanization and modernization. The easy takeoff and landing performances of UAVs are urgently required for timely and effective spraying, especially in dispersed plots and hilly mountains. However, the unclearness of wind field distribution leads to more serious droplet drift problems. The drift and distribution of droplets, which depend on airflow distribution characteristics of UAVs and the droplet size of the nozzle, are directly related to the control effect of pesticide and crop growth in different growth periods. This paper proposes an approach to research the influence of the downwash and windward airflow on the motion distribution of droplet group for the SLK-5 six-rotor plant protection UAV. At first, based on the Navier-Stokes (N-S) equation and SST k–ε turbulence model, the three-dimensional wind field numerical model is established for a six-rotor plant protection UAV under 3 kg load condition. Droplet discrete phase is added to N-S equation, the momentum and energy equations are also corrected for continuous phase to establish a two-phase flow model, and a three-dimensional two-phase flow model is finally established for the six-rotor plant protection UAV. By comparing with the experiment, this paper verifies the feasibility and accuracy of a computational fluid dynamics (CFD) method in the calculation of wind field and spraying two-phase flow field. Analyses are carried out through the combination of computational fluid dynamics and radial basis neural network, and this paper, finally, discusses the influence of windward airflow and droplet size on the movement of droplet groups.
Recently, multi-rotor unmanned aerial vehicle (UAV) becomes more and more significantly irreplaceable in the field of plant protection against diseases, pests and weeds of crops. The easy takeoff and landing performance, hover function and high spraying efficiency of UAV are urgently required to spray pesticide for crop timely and effectively, especially in dispersed plots and hilly mountains. In such situations, the current researches about UAV spray application mainly focus on studying the influence of the UAV spraying parameters on the droplet deposition, such as operation height, operation velocity and wind velocity. The deposition and distribution of pesticide droplets on crops which depends on installation position of nozzle and airflow distribution characteristics of UAV are directly related to the control effect of pesticide and crop growth in different growth periods. As a preliminary step, this study focuses on the dynamic development law and distribution characteristics of the downwash air flow for the SLK-5 six-rotor agricultural UAV. Based on compressible Reynolds-averaged Navier-Stokes (RANS) equations with an RNG k-ε turbulence model and dynamic mesh technology, the efficient three-dimensional computational fluid dynamics (CFD) method was established to analyze the flow field distribution characteristics of UAV in hover. Then the unsteady interaction flow field of the wing was investigated in detail. The downwash wind speed of the marked points for the SLK-5 UAV in hover was also tested by weather tracker. It was found that the maximum velocity value of the downwash flow was close to 10 m/s; the z-direction velocity was the main body of the wind velocity in the downwash airflow, and the comparison of the wind velocity experiment test and simulation showed that the relative error was less than 12% between the experimental and simulated values of the z-direction velocity at the marked points. Then the flow characteristics of the longitudinal and cross section were analyzed in detail, the results obtained can be used as a reference for drift and sedimentation studies for multi-rotor unmanned aerial vehicle.
In this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real‐time estimation of the unmeasured state signals. Moreover, a projection‐type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three‐dimensional vector forming robots to improve the real‐time forming accuracy.
In this article, a desired compensation version of the output feedback controller (DOFCESO) without using velocity measurement signal is proposed for precise tracking control of the three-axis electrical-optical gyro-stabilized platform in the presence of largely unknown matched and mismatched modeling uncertainties. The proposed controller takes into account not only the system parametric deviations as well as the unmeasurable signal and external disturbances. To further handle the unmeasurable signal and the external disturbances, the composite extended state observer and nonlinear disturbance observer are constructed simultaneously via integrating adaptive nonlinear feedback tracking control design. To address the uncertainties arising from parametric deviations and external disturbances, the parameter adaptation mechanism is incorporated into the composite observer design to estimates of both the unmeasurable signal and the mismatched disturbance in the adaptive backstepping design. The tracking differentiator design is introduced here to estimate the derivative of the virtual control, leading to a much simpler control structure and reduced implementation costs. Furthermore, the proposed controller guarantees final tracking accuracy in the presence of time-invariant modeling uncertainties and preserves the performance results of both control methods while overcoming their practical performance limitations. Extensive comparative experimental results are obtained to verify the high-performance nature of the proposed control strategy.
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