龍門式定位平台之同動控制系統設計與實現

Abstract

本論文主要是以個人電腦端為基礎，針對龍門式定位平台發展具高精密度與高強健性之控制系統以達到平台之同動控制。本論文採用的龍門式定位平台是由兩台互相平行的永磁線型同步馬達耦合一台垂直的永磁線型同步馬達所組成的。因此雙平行線型馬達間的同動控制成為目前龍門式定位平台控制的重大課題。本論文首先針對單軸進行精密控制，1.提出機率型模糊類神經網路控制系統。雖然此控制器經由參數學習能有效進行控制，但由於未知的系統動態，參數須經由好幾次迭代學習才能得到正確之控制效果。因此本論文進一步提出2.自我演化機率型模糊類神經網路控制系統，使用參數學習與架構學習同時進行，目的為加速系統追隨誤差之收斂速度。接著本論文以滑動模式控制為基礎，3.提出分數階積分型滑動模式控制系統進行改良傳統滑動模式暫態響應。為了進一步提升強健性，本論文最後提出4.智慧型分數階積分滑動模式控制系統，使用機率型模糊類神經網路直接對系統的不確定項進行估測。基於各種良好之控制基礎，在同動控制方面，本論文採用以Lagrange方程式推導三自由度龍門動態模型，並以此模型為基礎，發展智慧型分數階積分滑動模式控制系統達到同動控制。上述控制系統均實現在個人電腦，最後由實驗證明設計之控制理論有效性與可行性。

The objective of this thesis is to develop and implement personal computer (PC) based high precision and robust synchronous control systems for a gantry position stage. The adopted gantry position stage is composed of two parallel permanent magnet linear synchronous motors (PMLSMs) in X-axis and one PMLSM in Y-axis. Currently, the synchronous control of the parallel linear motors has become a challenge in the gantry position stages. In this thesis, a probabilistic fuzzy neural network control (PFNN) is proposed to control the position of a single PMLSM first. Though the PFNN can control the PMLSM accurately, the learning ability is incapable of dealing with the serious uncertainties in the control process. Thus, a self-organizing probabilistic fuzzy neural network control (SOPFNN), which consists of the structure learning and parameter learning algorithms, is further proposed to improve the control performance and convergence speed of PFNN. Moreover, a fractional order integral sliding-mode control (FISMC) using fractional operator is developed to perform better transient response compared with the conventional sliding-mode control (SMC). Furthermore, to improve the robustness of the FISMC system, an intelligent fractional order integral sliding-mode control (IFISMC) with PFNN based online uncertainty estimation is proposed. With regard to the synchronous control of parallel PMLSMs, a Lagrange’s equation based three-degree-of-freedom (3-DOF) dynamic model for gantry position stage is derived first. Then, the developed IFISMC system is applied to achieve high performance synchronous control based on 3-DOF dynamic model. All the proposed control strategies are realized via a PC. Finally, some experimental results are illustrated to show the effectiveness and validity of the proposed control approaches.