An ambient proof and wide tuning range LC VCO with automatic amplitude control for tuner applications

This paper represents a LC VCO with AAC (Auto Amplitude Control), in which PMOS FETs are used as active components, and the varactors are directly connected to ground to widen Kvco linear range. The AAC circuitry adds little noise to the VCO and provides it with robust performance over a wide temperature and carrier frequency range. The VCO is fabricated in 50-GHz 0.35-mu m SiGe BiCMOS process. The measurement results show that it has -127.27-dBc/Hz phase noise at 1-MHz offset and a linear gain of 32.4-MHz/V between 990-MHz and 1.14-GHz. The whole circuit draws 6.6-mA current from 5.0-V supply.

A 1.1GHz LC VCO with Automatic Amplitude Control for Tuner Applications

This paper presents an LC VCO with auto-amplitude control （AAC）, in which pMOS FETs are used,and the varactors are directly connected to ground to widen the linear range of Kvco. The AAC circuitry adds little noise to the VCO but provides it with robust performance over a wide temperature and carrier frequency range.The VCO is fabricated in a chartered 50GHz 0.35μm SiGe BiCMOS process. The measurements show that it has - 127. 27dBc/Hz phase noise at 1MHz offset and a linear gain of 32.4MHz/V between 990MHz and 1.14GHz.The whole circuit draws 6. 6mA current from 5V supply.

移动机器人分布式超声探测系统设计

设计制作了一种基于多处理器的移动机器人分布式超声环境探测系统.该系统由上位工作模式控制模块和下位智能超声传感器阵列组成.下位智能超声传感器选用收发一体式超声传感器,每个传感器均由独立的微处理器控制,完成实时数据处理、抗干扰处理、故障报警以及并行数据通信等功能.上位工作模式控制模块可以根据不同的控制策略,使下位传感器阵列采用“阈值比较法”和“改进型递推均值滤波”算法及EERUF方法并行循环工作模式,实现不同方向传感器分组并行工作,提高了探测的实时性和准确性,以及对移动机器人控制的鲁棒性.仿真和实验的结果都验证了该系统的可靠性和有效性.

水下机器人神经网络自适应逆控制

水下环境的复杂性以及自身模型的不确定性,给水下机器人的控制带来很大困难。针对水下机器人的特点和控制方面所存在的问题,提出了基于预测 校正控制策略的水下机器人神经网络自适应逆控制结构及训练算法。通过在线辨识系统的前向模型,估计出系统的Jacobian矩阵,然后采用预报误差法实现控制器的自适应。同时,为了提高系统对于外扰的鲁棒性,在伪线性回归算法的基础上,在评价函数中引入微分项。理论分析和仿真结果表明,与原来的算法相比,微分项的引入改善了系统对于外扰的鲁棒性和动态性能。