【摘要】：壓電材料作為一種智能材料，同時具備傳感器與作動器的功能，由于其能夠減輕結(jié)構(gòu)質(zhì)量，所以廣泛應(yīng)用于結(jié)構(gòu)振動控制中。本文使用模糊控制，模糊神經(jīng)網(wǎng)絡(luò)（FNN）控制以及基于可變學習效率的FNN控制作為控制算法，針對粘貼有壓電材料的懸臂梁進行主動振動控制研究。 首先本文利用歐拉伯努利梁理論推導了壓電懸臂梁的運動微分方程，對梁在自由狀態(tài)下受到?jīng)_擊激勵時的時間位移曲線進行了仿真。然后，，闡述了模糊控制的基本理論和模糊控制器的設(shè)計方法由此設(shè)計了模糊控制器。由于模糊控制器不需要被控對象的精確數(shù)學模型，所以廣泛應(yīng)用于各種工業(yè)控制器中。但是模糊控制器不具備自學習能力，不能自動適應(yīng)外界環(huán)境的改變，所以本文提出了模糊神經(jīng)網(wǎng)絡(luò)控制算法。模糊神經(jīng)網(wǎng)絡(luò)結(jié)合了模糊系統(tǒng)不需要被控對象的精確數(shù)學模型和神經(jīng)網(wǎng)絡(luò)具備自學的優(yōu)點，在智能控制中受到技術(shù)人員的關(guān)注。 然而，模糊神經(jīng)網(wǎng)絡(luò)收斂速度常常不理想，導致訓練時間過長，不適用于實時自動控制系統(tǒng)�；诖藛栴}，本文提出了一種基于可變學習速率的模糊神經(jīng)網(wǎng)絡(luò)優(yōu)化方法。優(yōu)化后的FNN控制系統(tǒng)在每次訓練中都選擇最優(yōu)的學習速率，所以能夠在系統(tǒng)允許最大誤差前提下達到提高神經(jīng)網(wǎng)絡(luò)的收斂速度的效果。 本文采用MATLAB來測試模糊系統(tǒng)，F(xiàn)NN系統(tǒng)，和優(yōu)化后的FNN控制系統(tǒng)對壓電懸臂梁的振動控制效果，通過仿真，可以得到各算法在振動抑制中的控制效果。同時仿真說明基于可變學習速率優(yōu)化方法能夠加快網(wǎng)絡(luò)收斂從而提高控制算法性能。
[Abstract]:As a kind of intelligent material, piezoelectric material has the functions of sensors and actuators. Because of its ability to reduce the structure quality, piezoelectric materials are widely used in structural vibration control. In this paper, fuzzy control, fuzzy neural network (FNN) control and FNN control based on variable learning efficiency are used as control algorithms to study active vibration control of cantilever beams with piezoelectric materials. In this paper, the differential equation of motion of piezoelectric cantilever beam is derived by using Eulerian Bernoulli beam theory, and the time displacement curve of piezoelectric cantilever beam subjected to shock excitation is simulated. Then, the basic theory of fuzzy control and the design method of fuzzy controller are expounded. Fuzzy controller is widely used in various industrial controllers because it does not need the precise mathematical model of the controlled object. However, the fuzzy controller does not have the ability of self-learning and can not adapt to the change of the external environment automatically. Therefore, a fuzzy neural network control algorithm is proposed in this paper. The fuzzy neural network (FNN) combines the advantages of the precise mathematical model of the fuzzy system without the object under control and the neural network with the advantages of self-study, so it has attracted the attention of technicians in intelligent control. However, the convergence speed of fuzzy neural networks is often not ideal, which leads to long training time and is not suitable for real-time automatic control systems. Based on this problem, a fuzzy neural network optimization method based on variable learning rate is proposed. The optimized FNN control system selects the optimal learning rate in each training, so it can improve the convergence speed of neural network under the premise of the maximum error allowed by the system. In this paper, MATLAB is used to test the vibration control effect of the fuzzy system and the optimized FNN control system for piezoelectric cantilever beam. Through simulation, the control effect of each algorithm in vibration suppression can be obtained. At the same time, the simulation shows that the algorithm based on variable learning rate optimization can accelerate the convergence of the network and improve the performance of the control algorithm.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB535

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