Multiple-model control architecture for a quadrotor with constant unknown mass and inertia

This paper presents a methodology for height and yaw angle control of a quadrotor that transports an unknown constant load added before the flight. Based on measurements from the onboard sensors, estimates of inertial parameters – mass and z-axis inertia – and state variables – vertical position, velocity, yaw angle and rate – are provided resorting to Multiple-Model Adaptive Estimators. The number and worst case performance of Kalman filters is selected based on the Baram Proximity Measure. The proposed control methods are a steady state Linear Quadratic Regulator (LQR) with integrative action for the height, and an LQR controller for the yaw angle. The overall system obtained is validated with load variations of up to 10% of the vehicle mass, both in simulation and experimentally, resorting to an off-the-shelf commercially available quadrotor.     

基于LonWorks技术的校园电力能耗监控系统的研究与实现

为了有效地管理与节约校园电能耗,以＂节约型校园电力能耗监控系统＂的建设为背景,设计了基于LonWorks技术i.Lon Smart Server 2.0某高校的电力能耗监控系统,包括系统网络结构、使用的关键技术、系统最终实现。通过对系统进行测试验证,结果表明系统能够实现电力节能和监控管理功能。     

具有路径能耗优化特性的WSN无标度容错拓扑控制算法

针对数据传输型的大规模无线传感网络中路径能量损耗问题,建立在多跳模式下的网络路径能耗优化模型,得出可以使网络通信能耗最小的节点度取值规律。依据节点度的最优取值,通过控制网络平均节点度的适应度模型来构建网络拓扑,提出一种具有路径能耗优化特性的无标度容错拓扑控制算法EETA(energy efficiency topology algorithm)。动态性能分析表明,由该算法生成的网络拓扑,其节点的度分布服从幂律,具有无标度拓扑的强容错能力。仿真实验结果显示,该算法同时也降低了网络路径能量消耗,具有路径节能性。     

Efficient flow‐control algorithm cooperating with energy allocation scheme for solar‐powered WSNs

Recently, solar energy emerged as a feasible supplement to battery power for wireless sensor networks (WSNs) which are expected to operate for long periods. Since solar energy can be harvested periodically and permanently, solar‐powered WSNs can use the energy more efficiently for various network‐wide performances than traditional battery‐based WSNs of which aim is mostly to minimize the energy consumption for extending the network lifetime. However, using solar power in WSNs requires a different energy management from battery‐based WSNs since solar power is a highly varying energy supply. Therefore, firstly we describe a time‐slot‐based energy allocation scheme to use the solar energy optimally, based on expectation model for harvested solar energy. Then, we propose a flow‐control algorithm to maximize the amount of data collected by the network, which cooperates with our energy allocation scheme. Our algorithms run on each node in a distributed manner using only local information of its neighbors, which is a suitable approach for scalable WSNs. We implement indoor and outdoor testbeds of solar‐powered WSN and demonstrate the efficiency of our approaches on them. Copyright © 2010 John Wiley & Sons, Ltd.