Plenty of dams in China are in danger while there are few effective methods for underwater dam inspections of hidden problems such as conduits,cracks and inanitions.The dam safety inspection remotely operated vehicle(DSIROV) is designed to solve these problems which can be equipped with many advanced sensors such as acoustical,optical and electrical sensors for underwater dam inspection.A least-square parameter estimation method is utilized to estimate the hydrodynamic coefficients of DSIROV,and a four degree-of-freedom(DOF) simulation system is constructed.The architecture of DSIROV's motion control system is introduced,which includes hardware and software structures.The hardware based on PC104 BUS,uses AMD ELAN520 as the controller's embedded CPU and all control modules work in VxWorks real-time operating system.Information flow of the motion system of DSIROV,automatic control of dam scanning and dead-reckoning algorithm for navigation are also discussed.The reliability of DSIROV's control system can be verified and the control system can fulfill the motion control mission because embankment checking can be demonstrated by the lake trials.
Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.
Sun YushanWang JianguoWan LeiHu YunyanJiang Chunmeng
