A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain c...A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain conditions is obtained. An error system around the steady state is studied further. Moreover, the condition for the state having no traffic jam is derived. A new control scheme is presented to suppress the traffic jam in the modified coupled map car-following model under the open boundary. A control signal including the velocity differences between the following and the considering vehicles, and between the preceding and the considering vehicles is used. The condition under which the traffic jam can be well suppressed is analysed. The results are compared with that presented by t^onishi et al. （the KKH model）. The simulation results show that the temporal behaviour obtained in our model is better than that in the KKH model. The simulation results are in good agreement with the theoretical analysis.展开更多
In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, ...In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer （HGO） is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.展开更多
为了扩大原子力显微镜(Atomic Force Microscope,AFM)使用范围,研制了一套大范围高速AFM系统。针对大范围高速扫描时Z方向控制问题,提出了前馈反馈混合控制方法。前馈控制包括自动调平前馈和基于前一行扫描前馈,前者通过多线扫描确定样...为了扩大原子力显微镜(Atomic Force Microscope,AFM)使用范围,研制了一套大范围高速AFM系统。针对大范围高速扫描时Z方向控制问题,提出了前馈反馈混合控制方法。前馈控制包括自动调平前馈和基于前一行扫描前馈,前者通过多线扫描确定样品倾斜位置,将所有扫描点的倾斜位移差用函数式表达,然后将其换算为Z向驱动电压后驱动下扫描器运动;后者利用前一行扫描高度数据作为当前行Z向扫描器驱动的参考输入。反馈控制为在普通比例-积分(PI)控制基础上改进的动态P参数PI控制,P参数设置与误差大小有关。实验结果表明:采用本控制方法最大控制误差由40.17nm减小为6.01nm,误差均方根值由22.85nm减小为2.01nm,明显抑制了误差信号,提高了Z向控制效果,获得了更精确的高度图像。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11072117,10802042,and 60904068)the Natural Science Foundation of Zhejiang Province,China (Grant No.Y6100023)+1 种基金the Natural Science Foundation of Ningbo,China (Grant No.2009B21003)the K.C.Wong Magna Fund in Ningbo University,China
文摘A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain conditions is obtained. An error system around the steady state is studied further. Moreover, the condition for the state having no traffic jam is derived. A new control scheme is presented to suppress the traffic jam in the modified coupled map car-following model under the open boundary. A control signal including the velocity differences between the following and the considering vehicles, and between the preceding and the considering vehicles is used. The condition under which the traffic jam can be well suppressed is analysed. The results are compared with that presented by t^onishi et al. （the KKH model）. The simulation results show that the temporal behaviour obtained in our model is better than that in the KKH model. The simulation results are in good agreement with the theoretical analysis.
文摘In this paper, we investigate the learning issue in the adaptive neural network （NN） output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer （HGO） is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.
文摘为了扩大原子力显微镜(Atomic Force Microscope,AFM)使用范围,研制了一套大范围高速AFM系统。针对大范围高速扫描时Z方向控制问题,提出了前馈反馈混合控制方法。前馈控制包括自动调平前馈和基于前一行扫描前馈,前者通过多线扫描确定样品倾斜位置,将所有扫描点的倾斜位移差用函数式表达,然后将其换算为Z向驱动电压后驱动下扫描器运动;后者利用前一行扫描高度数据作为当前行Z向扫描器驱动的参考输入。反馈控制为在普通比例-积分(PI)控制基础上改进的动态P参数PI控制,P参数设置与误差大小有关。实验结果表明:采用本控制方法最大控制误差由40.17nm减小为6.01nm,误差均方根值由22.85nm减小为2.01nm,明显抑制了误差信号,提高了Z向控制效果,获得了更精确的高度图像。
