【摘要】：力反饋設備是一種人機交互設備,當操作者通過力反饋設備與虛擬環(huán)境進行交互時,力反饋設備能夠提供虛擬環(huán)境的反饋力給操作者。操作者對力反饋設備進行操作時,操作者對力反饋設備施加的力可以通過測力裝置來進行測量。若要對操作者與力反饋設備之間的力進行控制,則需要采用阻抗控制的方法。本文針對力反饋設備采用自適應阻抗控制方法進行控制仿真與研究。首先,介紹了力反饋設備的機械結(jié)構以及硬件部分的選型設計。對力反饋設備的驅(qū)動電機、關節(jié)傳感器以及數(shù)據(jù)采集卡進行選型,并對其工作原理與性能進行研究。然后,對力反饋設備進行運動學與動力學分析。建立力反饋設備運動學方程,求解力反饋設備的雅各比矩陣,并且得到力反饋設備的工作空間。采用Matlab機器人工具箱進行仿真,驗證運動學分析的正確性。采用拉格朗日法對力反饋設備進行動力學分析,通過編程來求解力反饋設備的動力學方程,為之后的自適應阻抗控制仿真做好了準備。接著,針對力反饋設備進行了自適應阻抗控制仿真,并對仿真結(jié)果進行分析。利用Matlab中的Simmechanics工具對力反饋設備進行建模,在Simulink環(huán)境下搭建了自適應阻抗控制系統(tǒng),選取合適的阻抗控制參數(shù)M,K,B進行仿真。自適應阻抗控制具有不需要精確控制模型的優(yōu)點,通過迭代的方法進行控制。通過仿真得到末端執(zhí)行器的位移與力的跟蹤曲線,分析仿真結(jié)果可以得出,自適應阻抗控制方法可以準確跟蹤期望的阻抗力,具有較好的控制性能,并且對于外界環(huán)境的變化具有一定的魯棒性。接著,采用模糊自整定的方法對阻抗控制參數(shù)M,K,B進行尋優(yōu)。為了避免通過大量試用來確定阻抗控制參數(shù),采用模糊自整定的方法對阻抗控制參數(shù)進行在線尋優(yōu),在仿真過程中,不斷檢測位置的誤差及其變化率,制定模糊規(guī)則,通過模糊推理,最終進行解模糊來得到阻抗控制參數(shù)的最優(yōu)值,節(jié)約了大量時間并且具有良好的控制效果。最后,采用基本的粒子群算法對阻抗控制M,K,B參數(shù)進行尋優(yōu)。將阻抗參數(shù)作為粒子,建立適應度函數(shù),通過不斷的迭代計算來尋找最優(yōu)粒子的位置,最終得到最優(yōu)的阻抗控制參數(shù)。采用離線尋優(yōu)所得到的最優(yōu)阻抗控制參數(shù)進行系統(tǒng)仿真,具有良好的控制效果。
[Abstract]:Force feedback device is a kind of human-computer interactive equipment. When the operator interacts with the virtual environment through the force feedback device, the force feedback device can provide the feedback force of the virtual environment to the operator. When the operator operates the force feedback equipment, the force applied by the operator to the force feedback device can be measured by the force measuring device. In order to control the force between the operator and the force feedback equipment, impedance control method is needed. In this paper, the adaptive impedance control method is used to simulate and study the force feedback equipment. Firstly, the mechanical structure of the force feedback equipment and the selection design of the hardware part are introduced. The driving motor, joint sensor and data acquisition card of the force feedback device are selected, and its working principle and performance are studied. Then, the kinematics and dynamics of the force feedback equipment are analyzed. The kinematics equation of the force feedback equipment is established, the Yakubi matrix of the force feedback device is solved, and the workspace of the force feedback device is obtained. Matlab robot toolbox is used to simulate and verify the correctness of kinematics analysis. Lagrangian method is used to analyze the dynamics of the force feedback device, and the dynamic equation of the force feedback device is solved by programming, which is ready for the simulation of adaptive impedance control. Then, the adaptive impedance control simulation of the force feedback device is carried out, and the simulation results are analyzed. The force feedback equipment is modeled by the Simmechanics tool in Matlab, and the adaptive impedance control system is built in Simulink environment. The appropriate impedance control parameters M, K, B are selected for simulation. Adaptive impedance control has the advantage of no precise control model, and it is controlled by iterative method. The tracking curves of displacement and force of the end actuator are obtained by simulation. The simulation results show that the adaptive impedance control method can accurately track the desired resistance and has better control performance. And it is robust to the change of external environment. Then, fuzzy self-tuning method is used to optimize the impedance control parameters M, K, B. In order to avoid determining the impedance control parameters through a large number of trials, the fuzzy self-tuning method is used to optimize the impedance control parameters on-line. In the simulation process, the error of the position and its changing rate are constantly detected, and the fuzzy rules are formulated. The optimal value of impedance control parameters is obtained by fuzzy reasoning, which saves a lot of time and has a good control effect. Finally, the basic particle swarm optimization algorithm is used to optimize the impedance control parameters M, K, B. The impedance parameter is taken as the particle and the fitness function is established. The optimal position of the particle is found through continuous iterative calculation, and finally the optimal impedance control parameter is obtained. The optimal impedance control parameters obtained from off-line optimization are used to simulate the system, which has a good control effect.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP242

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