傳統接觸式避震器，因接觸關係造成系統的震動、摩擦及能量耗損，而磁浮系統則是以非接觸方式，控制電磁力使物體具有漂浮、轉動或移動等行為的系統，其優點為有效降低機械式接觸造成的震動、摩擦和損耗，然而，其數學模型中的參數深受永久式磁鐵的形狀與載重平台質量大小等因素影響，為達到較佳響應，本論文提出以循環式類神經網路為控制基礎之磁浮避震器，以取代傳統避震器，並將其應用在六足機器人上。
隨著磁浮避震器應用範圍的不斷擴大，系統的數學模型充滿變數，目前業界採用的傳統磁浮避震控制器，雖具有理論與結構簡單的優點，然已無法適應複雜的環境。在磁浮系統的智慧控制研究方面，不論在理論或應用上，都有可觀的研究成果，然在智慧型避震器方面的研究比較少見。因此本論文提出另一種解決方法，以循環式類神經網路模型為主，傳統比例微分控制為輔的磁浮避震控制器，藉由蒐集輸出入資料對，建立較準確的類神經模型，設計出可以適應環境複雜化的控制器。
為了處理此高度非線性動態系統，本論文採用循環式類神經網路，做為磁浮避震器模型，並以最小均方誤差演算法訓練參數，以符合磁浮避震系統之數學模型。最後，為克服環境的變化，將磁浮避震控制器加入一輔助用的比例微分控制器。為驗證所提方法之有效性，研究過程將此控制器以MATLAB模擬,實驗室的磁浮平台實驗及實作之磁浮避震器雛形在六足機器人之應用，發現在實際硬體設備上是可行的。
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.
