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Sliding Mode And Neuro Networks Control Of Flexible Satellite Attitude Maneuvering

Posted on:2011-10-09Degree:MasterType:Thesis
Country:ChinaCandidate:Y CaoFull Text:PDF
GTID:2132330338980021Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
Satellite attitude control system is a coupled uncertain nonlinear system. For any on-orbit satellite, it is inevitable to be influenced by some kinds of uncertain parameters and disturbance torques, which makes the attitude control problem further complicated. Therefore, to accomplish attitude control mission, it is necessary to design attitude control laws with high robustness. On this background, this thesis investigated attitude control algorithms for satellite attitude control system in detail, from both theoretical and applicable aspects, and applied the proposed control schemes to certain satellite control system. The main contents of this thesis are as follows.First, Sliding mode control has many advantages, such as high robustness, easy calculation, good real-time characteristic, quick response characteristic. The paper use this theory to realize the attitude maneuver control of flexible satellite,using saturation function replace sign function to Eliminate Buffeting Phenomenon.Second, the paper use sliding mode to control the satellite, using neural networks to compensate the uncertainty, which is proved by Lyapunov stability theory.The paper use tradition CMAC neural network method to approach uncertain function, which can solve the local minimum phenomenon. However, the tradition CMAC has bad real-time and generalization characteristic, soGaussian function CMAC, which can quantified as a continuous gaussian function, is proposed to replace the tradition CMAC. Despite of this, gaussian function CMAC is complex to calculation, a fast algorithm CMAC is a new way to improve computing speed.All of the above neural networks, the range of the input signal must be awared of in advance, which will limit the application of the network. However, it is not necessaryto know the range of the input signal using self-organization CMAC neural network, which can update of structure and weights automatically. It is more convenient and intelligent.So, for the changes in parameters, this flexible satellite attitude control system can be designed to meet the accuracy requirements.
Keywords/Search Tags:flexible satellite, attitude control, CMAC neural network, sliding mode control
PDF Full Text Request
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