| Summary: | 碩士 === 中國文化大學 === 機械工程學系數位機電碩士班 === 104 === There exist mechanical vibration, friction and wearing loss caused by contact operation in the conventional mechanical shock absorbers. Maglev suspension system (MSS), using the electromagnetic force to float, can effectively reduce above drawbacks. However the parameters in the mathematical model are related to the permanent magnet geometry, distance and the total mass of the platform. To achieve better response, a recurrent neural network (RNN) model control architecture for MSS is proposed to replace the conventional shock absorber in this thesis. Finally the proposed MSS is utilized to the six-foot robot.
With the extension of the applications of the MSS, the mathematical model of the system is full of uncertainties. Conventional controllers, currently used in the industry, cannot adapt to the complex environment, although their theory and architecture are simple. Several design methods based on the intelligent control have been announced; however, the researches of smart MSS are relatively rare. To deal with this problem, a RNN model is proposed as main controller and an auxiliary proportional-differential (PD) controller is added in the proposed architecture. By using the gathered data pairs, the more accurate model can be established in the changing environments.
To deal with the highly nonlinear dynamic system, the trained RNN is developed as the model of the MSS and the least-mean-square (LMS) error learning algorithm is proposed to tune the parameters. To further tackle the larger uncertainties, an auxiliary PD control effort is added. Thus, the fast response can be obtained without degrading the tracking performance. Some MATLAB simulated results, experimental results and one implemented MSS prototype are provided to verify the effectiveness of the proposed architecture.
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