多軸機械手臂學習式運動控制器之開發

Development of the Learning Based Multi-Axis Motion Controller for Robotic Manipulators

林欣緯(Shin-Wei Lin)

指導教授 ： 黃漢邦

國立臺灣大學/工學院/機械工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.01762

摘要

本論文的主要目的為設計一個學習式運動控制器，使得此控制器具有線上估測機制，補償機械手臂的非線性與耦合項，藉此縮小手臂作動時的追跡誤差並最終達成零追跡誤差。
在學習式控制器領域之中，適應性類神經控制器具有線上學習的能力。此外小腦模型控制器（CMAC）有快速學習、低計算複雜度、區域類化等能力，使其演算法能嵌入在微控制器中做即時運算。為了兼具快速學習與避免局部最小值，本論文提出新的CMAC控制器。此控制器包含兩種方法：灰色學習與修正型追跡誤差。灰色學習以灰關聯度分析為基礎，可用來動態調整學習率；修正型追跡誤差考慮到多軸的同步性和追蹤控制的關係，能對各軸運動做出及時的修正和補償。
為了驗證控制器性能，我們先透過結合ADAMS/Control和MATLAB/Simulink的動態模擬器來實現運動規劃和系統模擬。實作方面，採用NI公司的sbRIO-9642嵌入式控制與擷取介面卡來實現學習式控制器，並實際應用於本實驗室所發展的六軸機械手臂之上。經模擬與實驗結果發現，相對於傳統的控制架構，新的學習式控制器架構可得到較好的追跡效果。

關鍵字

小腦模型控制器 ； 灰色學習率 ； 追跡控制 ； 學習式控制 ； 同動控制

並列摘要

The main purpose of the thesis is to design a learning-based controller for robotic manipulators. This controller can estimate the nonlinear system dynamics to minimize tracking errors in motion and eventually achieve a zero-tracking-error performance.
Among learning-based control techniques, the adaptive neural network control has an on-line learning ability, and the cerebellar model articulation controller (CMAC) has the properties of rapid convergence, lower computational complexity, and local generalization, which are advantageous to allow the microcontroller to execute the control algorithm in real-time. In order to prevent from getting stuck in local minima and have faster learning convergence, a new CMAC controller is proposed. The controller consists of two main approaches: a grey learning rate and a modified tracking error. The grey learning rate, which is based on a grey relational analysis, is utilized to adjust the learning rate on-line. The modified tracking error, which is defined according to synchronization control, can achieve asymptotic convergence of both tracking errors and synchronization errors simultaneously.
To demonstrate the performance of the proposed controller, ADMAS and MATLAB/Simulink are used for simulation. The NI sbRIO-9642 is employed to realize the control algorithm on the NTU arm, which is developed by our laboratory. In comparison with conventional controllers, the proposed learning-based controller can provide better tracking performance.

並列關鍵字

CMAC ； grey learning rate ； tracking control ； learning-based control ； synchronization control

參考文獻

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