比例積分微分控制器於各領域被廣泛的使用，根據誤差進行比例、積分及微分的調整可提升控制系統的穩定性與可靠性。現今已有許多學者發展出不同的PID參數調整方法使調參容易、系統效率提升，但如何更有效的調整參數仍有待研究。
本文提出使用粒子群演算法及基因演算法來最佳化比例積分控制參數，主要目的為使用粒子群演算法及基因演算法搜索三相永磁同步電動機向量控制之轉速及電流迴路PI控制參數。本文使用MATLAB實現粒子群演算法及基因演算法並使用Simulink模擬軟體建立三相永磁同步電動機向量控制平台，透過演算法可得到使目標函數最小化之PI參數組合。本文將粒子群演算法及基因演算法與其他參數調整法之模擬結果進行比較，結果顯示粒子群最佳化參數法相較於工程設計法、PID Tuner設計法、齊格勒調整法及基因最佳化參數法，所得之最佳化PI參數於平方誤差積分可以分別改善96%、69%、99%及12%。

The Proportional-Integral-Derivative (PID) controller is widely used in various fields, and adjusting the proportional, integral, and derivative terms based on the error can improve the stability and reliability of control systems. Many scholars have developed different PID parameter tuning methods to make parameter adjustment easier and improve system efficiency. However, there is still a need for research on more effective parameter tuning methods.
This thesis proposes the use of particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the PID control parameters. The main objective is to use PSO and GA to search for the optimal proportional and integral (PI) control parameters for speed and current loops in the vector control of a three-phase permanent magnet synchronous motor (PMSM). MATLAB is used to implement the PSO and GA algorithms, and Simulink simulation software is used to build a platform for vector control of a three-phase PMSM. Through the algorithms, the PI parameter combination that minimizes the objective function can be obtained.
The simulation results of the PSO and GA algorithms are compared with other parameter tuning methods. The results show that the PSO-based optimization method improves the Integral of Squared Error (ISE) by 96% compared to the engineering design method, 69% compared to the PID Tuner design method, 99% compared to the Ziegler-Nichols tuning method, and 12% compared to the GA method.

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