9 research outputs found
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Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks
无线传感器网络,作为全球未来十大技术之一,集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术,可实时感知、采集、处理、传输网络分布区域内的各种信息数据,在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议,并针对大规模的无线传感器网络设计了一种树状路由协议,它根据节点地址信息来形成路由,从而简化了复杂繁冗的路由表查找和维护,节省了不必要的开销,提高了路由效率,实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR(AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位:工学硕士院系专业:信息科学与技术学院通信工程系_通信与信息系统学号:2332007115216
Constrained Output Iterative Learning Control
Iterative Learning Control (ILC) is a well-known method for control of systems performing
repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for
non-linear state space constrained systems. In the existing literature there is no general solution
for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm
(BEA) and resolves the transient growth error problem, which is a major obstacle in applying
ILC to non-linear systems. Another advantage of COILC is that this method can be applied to
constrained output systems. Unlike other ILC methods the COILC method employs an algorithm
that stops the iteration before the occurrence of a violation in any of the state space constraints.
This way COILC resolves both the hard constraints in the non-linear state space and the transient
growth problem. The convergence of the proposed numerical procedure is proved in this paper.
The performance of the method is evaluated through a computer simulation and the obtained
results are compared to the BEA method for controlling non-linear systems. The numerical
experiments demonstrate that COILC is more computationally effective and provides better
overall performance. The robustness and convergence of the method make it suitable for solving
constrained state space problems of non-linear systems in robotics