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Research On The System Modeling And Controlling For The Mooring Shifting Project Vessel

Posted on:2011-03-25Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z HuangFull Text:PDF
GTID:1102360305492054Subject:Control theory and control engineering
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A large number of project vessels, such as hydraulic dredge, geotextiles-laying vessel, stone dumper, are used in regulation works for Yangze River. These project vessels usually belong to a kind of non self-propelled mooring shifting vessels. Remodeled from other common ship, these vessels generally have the poor degree of automation. Under the background of market economy, high-automated project vessels will product more economic benefit. Therefore, researching automated control system of project vessels is beneficial to precision of construction operation and economic benefit.Firstly,the dissertation has studied this type vessels' system modeling. A lot of previous literatures were analyzed in detail. A linear two and three-dimensional moving model of mooring shifting project vessel was proposed,profit from the conventional ships model analysis method to analysis this vessels' moving mechanism and characteristic. Face to the this project vessel, the dissertation proposed a system identification plan based on the neural network. Take the geotextiles-laying vessel as example, parameters of the neural network model were training using the empirical datum, gathering from the engineering project. Then the displacement model had been established.Then in view of modelling and controllor optimized question, this dissertation Analysises the QPSO (quantum-behaved paticle swarm optimization) algorithm. The contraction expansion coefficient╬▓is the only parameter of QPSO algorithm.Sufficient experiment is done on╬▓from different aspects, including the problem dependence and the swarm size. According to the experimental result, it obtains the selection guidance criterion of the parameter. In order to increase efficiency of convergence, it proposed a new improved QPSO optimization. Simulation experiments indicate that this algorithm improves convergence speed. The proposed algorithm was used to optimize the moving model of the mooring shifting project vessel, which was the object model for the control system design and simulation.Based on fuzzy logic, a new track-keeping controller of mooring shifting project vessel was proposed. Particle swarm optimization algorithm was used to optimize fuzzy rules and membership. Simulation experiments indicate that particle swarm optimization algorithm is effectual for optimizing the parameters of fuzzy logic controller. The dynamic and the static performance indexes of the control system can meet the challenge of operating engineering.A new adaptive controller network architecture of project vessel was proposed in the dissertation and this architecture was based on ANFIS (adaptive neuro-nuzzy inference system). Therefore, the parameters of controller can vary with working environment to accomplish different tasks. The improved QPSO algorithm was used to optimization design the antecedent and consequent parameters of adaptive track keeping control network. The result of simulation experiments shows that the dynamic and static performance of adaptive track keeping controller based on ANFIS, was improved when compared with the controller based on fuzzy logic.The Multivariate adaptive controller network model was also purposed in the dissertation. The main learning pattern of this network model was sub-network parallel learning pattern. And the particle swarm was with corresponding sub-network parameters so that the network training processes would be easier and faster. Then, the simulation experiment of typical instance was chosen to analyze the effect of adaptive control system combined with the practical working situation of working ship. The experiment result shows that multivariate adaptive controller network was able to meet the multi-objective control requirement of project vessel.The track-keeping controller based on the fuzzy logic and improved QPSO has been applied in the project item. An automatic monitoring system was developed to monitor the operation situation of geotextiles-laying vessel. The programmable logic controller, global positioning system, multi-sensor information fusion, fieldbus and other technologies have been applied in this system. Therefore, the intelligent optimized strategy, automatic control and centralized monitoring of geotextiles-laying arrangement operation have been realized. The level of automation and intelligence for operation has been enhanced. The operation data of actual project vessel indicated that the mooring shifting intelligent control system of project vessel was effective, and this control system can also been applied to improve the automation of other ships. Its research results may promote apply in the similar project vessels' automatic work system, also has certain reference value to the related domain's research.Finally, the summary of the dissertation and the future work to be investigated are presented.
Keywords/Search Tags:mooring shifting, project vessel, system modeling, quantum-behaved particle swarm optimization, fuzzy logic control, adaptive neuro-fuzzy inference system, geotextiles-laying vessel
PDF Full Text Request
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