本論文在閉迴路輸入誤差的概念之下，提出了一種基於演化式演算法的工業用機械手臂動態參數鑑別方法。該方法主要是利用實際機器人與平行估計模型之間的關節轉矩殘差來對動態參數進行估計。實際機器人與估計模型皆使用相同的參考軌跡與調整到相同增益的相同控制律架構。估計模型的狀態是透過模型狀態生成器對量測信號進行濾波而產生的，因此藉由調整濾波器的截止頻率，可以創建出不同的估計模型。透過演化式演算法，我們可以在模型狀態生成器相對應的解空間內，搜尋出一個與實際機器人具有最小關節轉矩殘差的估計模型。另外，演化式演算法會自動調整對量測信號的濾波程度，為估計模型提供無雜訊的信號，使鑑別結果能夠更加地準確。透過最佳化過程所得出估計模型的動態參數，即為實際機器人最佳的參數鑑別結果。此外，本論文還提供了一個觀測矩陣的近似算法，能夠有效地降低估計模型構建的計算成本，使演化式演算法的搜尋更有效率。為了能夠進一步地提升動態模型的鑑別精度，本論文另外提出了一種基於遞迴神經網路的算法，可用於處理基本動態參數的物理一致性問題。首先，將慣性張量、傳動鏈慣性與摩擦力等相關的物理約束合併到線性矩陣不等式的公式中，以檢驗基本動態參數的物理一致性。接著，透過矩陣導向的梯度型遞迴神經網路來解決線性矩陣不等式的最佳化問題。由於此類遞迴神經網路的網路結構簡單，且求解線性矩陣不等式的過程是以並行分佈的方式運算。因此相較於目前常用的半正定規劃技術，可以更快速地完成物理一致性的驗證。此外，所提出梯度型遞迴神經網路的穩定性也已經利用Lyapunov穩定性定理進行證明。本論文所提出之物理一致性算法由於具有運算成本低的特點，非常適合應用在一些需要快速一致性評估的鑑別方法，例如線上鑑別方法或基於最佳化的鑑別方法。同時，這項物理一致評估技術也相當適合應用在本論文所提出的鑑別方法上，以確保所鑑別出的動態參數在物理上是一致的。最後，在六自由度工業用機械手臂上進行了幾項實驗，以驗證本論文所提出鑑別方法與物理一致性算法的有效性。

By exploiting the concept of closed-loop input error (CLIE), this dissertation proposes an identification approach for the dynamic parameters of industrial robot manipulator based on evolutionary algorithms (EAs). The proposed approach estimates the dynamic parameters by using joint torque residuals between the actual robot and a parallel estimated model. Both the actual robot and the estimated model use the same reference trajectories and the same control law structure tuned with the same gain. The state of the estimated model is generated by filtering the measured signal through a model state generator, so by adjusting the cutoff frequency of the filter, different estimated models can be generated. By using EA, one can search for an estimated model in the solution space corresponding to the model state generator so that the joint torque difference between the actual robot and the estimated model is minimized. In addition, EA will automatically adjust the filtering strength of the measured signals to provide a noise-free signal for the estimated model so that the identification result can be more accurate. The dynamic parameters of the estimated model obtained through the optimization process are the optimal identification results of the actual robot. Moreover, to reduce the computation cost of the estimated model, this dissertation also provides an approximation algorithm for the observation matrix so as to enhance the search efficiency of EA. In addition, to further improve the identification accuracy of dynamic models, this dissertation proposes an algorithm based on recurrent neural network (RNN) to deal with the physical consistency problem of base dynamic parameters. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into the formulation of linear matrix inequality (LMI) to examine the physical consistency of the base dynamic parameters. The optimization problem of LMI is then solved by a matrix-oriented gradient-type RNN. Since the network structure of this type of RNN is simple and the process for solving the optimization problem of LMI is parallel distributed, the physical consistency verification can be completed more quickly than when utilizing commonly used semi-definite programming techniques. Moreover, the stability of the proposed gradient-type RNN has been proved by using the Lyapunov stability theorem. By taking advantage of highly efficient computation capabilities, the proposed physical consistency algorithm is particularly suitable for identification approaches that require rapid consistency assessment such as on-line identification methods or optimization-based identification methods. Meanwhile, this physical consistency assessment technique is also quite suitable for the identification approaches proposed in this dissertation to ensure that the identified dynamic parameters are physically consistent. Several experiments have been conducted on a 6-DOF industrial robot manipulator to verify the effectiveness of the proposed identification approach and the proposed physical consistency algorithm.

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