This paper aims to provide a parametric design for robust flight controller of the model-scale helicopter. The main contributions lie in two aspects. Firstly,under near-hovering condition,a procedure is presented for ...This paper aims to provide a parametric design for robust flight controller of the model-scale helicopter. The main contributions lie in two aspects. Firstly,under near-hovering condition,a procedure is presented for simplification of the highly nonlinear and under-actuated model of the model-scale helicopter. This nonlinear system is linearized around the trim values of the chosen flight mode,followed by decomposing this high-order linear model into three lower-order subsystems according to the coupling properties among channels.After decomposition,the three subsystems are obtained which include the coupling subsystem between the roll( pitch) motion and the lateral( longitudinal) motion,the subsystem of the yaw motion and the subsystem of the vertical motion. Secondly,by using eigenstructure assignment,the problem of flight controller design can be converted into solving two optimization problems and the linear robust controllers of these subsystems are designed through solving these optimization problems. Besides, this paper contrasts and analyzed the performances of the LQR controller and the parametric controller. The results demonstrate the effectiveness and the robustness against the parametric perturbations of the parametric controller.展开更多
混合专家模型(mixture of experts,MoE)是一种神经网络模型架构,其特点是在模型中引入路由网络与专家子网络,进而代替原始的稠密网络。在推理过程中,MoE架构通过路由网络选择每次需要激活的专家子网络,仅激活其中部分专家完成给定任务...混合专家模型(mixture of experts,MoE)是一种神经网络模型架构,其特点是在模型中引入路由网络与专家子网络,进而代替原始的稠密网络。在推理过程中,MoE架构通过路由网络选择每次需要激活的专家子网络,仅激活其中部分专家完成给定任务。由于采用稀疏激活机制,混合专家模型同与其性能相当的稠密模型相比,大幅减少了训练和推理过程的计算开销,使得在给定计算成本下扩展模型规模成为可能。展开更多
文摘This paper aims to provide a parametric design for robust flight controller of the model-scale helicopter. The main contributions lie in two aspects. Firstly,under near-hovering condition,a procedure is presented for simplification of the highly nonlinear and under-actuated model of the model-scale helicopter. This nonlinear system is linearized around the trim values of the chosen flight mode,followed by decomposing this high-order linear model into three lower-order subsystems according to the coupling properties among channels.After decomposition,the three subsystems are obtained which include the coupling subsystem between the roll( pitch) motion and the lateral( longitudinal) motion,the subsystem of the yaw motion and the subsystem of the vertical motion. Secondly,by using eigenstructure assignment,the problem of flight controller design can be converted into solving two optimization problems and the linear robust controllers of these subsystems are designed through solving these optimization problems. Besides, this paper contrasts and analyzed the performances of the LQR controller and the parametric controller. The results demonstrate the effectiveness and the robustness against the parametric perturbations of the parametric controller.
文摘混合专家模型(mixture of experts,MoE)是一种神经网络模型架构,其特点是在模型中引入路由网络与专家子网络,进而代替原始的稠密网络。在推理过程中,MoE架构通过路由网络选择每次需要激活的专家子网络,仅激活其中部分专家完成给定任务。由于采用稀疏激活机制,混合专家模型同与其性能相当的稠密模型相比,大幅减少了训练和推理过程的计算开销,使得在给定计算成本下扩展模型规模成为可能。
