In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We ach...In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.展开更多
This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references ...This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references quickly. At first, nonsingular fast terminal slidingmode control is developed, which can guarantee not only the stability but also finite-timeconvergence of the closed-loop system. As the parameters of the designed controllers playa vital role for control performance, an improved beetle antennae search algorithm is proposedto optimise them. By employing the historical information of the beetle’s antennaeand dynamically updating the step size as well as the range of its searching, the optimisingis accelerated considerably to ensure the efficiency of the quadrotor control. The superiorityof the proposed control scheme is demonstrated by simulation experiments, from whichone can see that both the error and the overshooting of the trajectory tracking are reducedeffectively.展开更多
In order to solve the current situation that unmanned aerial vehicles(UAVs)ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace,a UAV route planning method that consider...In order to solve the current situation that unmanned aerial vehicles(UAVs)ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace,a UAV route planning method that considers regional risk assessment is proposed.Firstly,the low-altitude airspace is discretized based on rasterization,and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map.The path risk value is taken as the cost,the particle swarm optimization-beetle antennae search(PSO-BAS)algorithm is used to plan the spatial 3D route,and it effectively reduces the generated path redundancy.Finally,cubic B-spline curve is used to smooth the planned discrete path.A flyable path with continuous curvature and pitch angle is generated.The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost.At the same time,the path redundancy is low,and the curvature and pitch angle continuously change.It is a flyable path that meets the UAV performance constraints.展开更多
文摘In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective function.We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update rule.The proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size.Since ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective function.Resultantly,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex functions.The efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural network.The performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.
基金Fujian Provincial Science and Technology Major Project(No.2020HZ02014)Education and Teaching Reform Research Project for Colleges and Universities in Fujian Province(No.FBJG20210239)Huaqiao University Graduate Education Teaching Reform Research Funding Project(No.20YJG009).
文摘This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references quickly. At first, nonsingular fast terminal slidingmode control is developed, which can guarantee not only the stability but also finite-timeconvergence of the closed-loop system. As the parameters of the designed controllers playa vital role for control performance, an improved beetle antennae search algorithm is proposedto optimise them. By employing the historical information of the beetle’s antennaeand dynamically updating the step size as well as the range of its searching, the optimisingis accelerated considerably to ensure the efficiency of the quadrotor control. The superiorityof the proposed control scheme is demonstrated by simulation experiments, from whichone can see that both the error and the overshooting of the trajectory tracking are reducedeffectively.
基金supported by the National Natural Science Foundation of China(61601497)the Natural Science Basic Research Plan in Shaanxi Province of China(2022JM-412)the Air Force Engineering University Principal Fund(XZJ2020005).
文摘In order to solve the current situation that unmanned aerial vehicles(UAVs)ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace,a UAV route planning method that considers regional risk assessment is proposed.Firstly,the low-altitude airspace is discretized based on rasterization,and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map.The path risk value is taken as the cost,the particle swarm optimization-beetle antennae search(PSO-BAS)algorithm is used to plan the spatial 3D route,and it effectively reduces the generated path redundancy.Finally,cubic B-spline curve is used to smooth the planned discrete path.A flyable path with continuous curvature and pitch angle is generated.The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost.At the same time,the path redundancy is low,and the curvature and pitch angle continuously change.It is a flyable path that meets the UAV performance constraints.
