PID Controller Tuning Based on Particle Swarm Optimization (PSO) for Stewart Platform

[[abstract]]本研究中試著將粒子群聚演算法(Particle Swarm Optimization，PSO)運用在搜尋PID控制器的參數上面。在搜尋的過程中，使用不同的誤差準則作為適應函數，當作性能優劣的判斷與依據。在性能指標方面採用三種: (1) 平方誤差的積分準則(Integral Square Error Criterion)，簡稱ISE、(2) 絕對誤差的積分準則(Integral Absolute Error Criterion)，簡稱IAE、(3) 絕對誤差與時間乘積的積分準則(Integral Time Absolute Error Criterion)，簡稱ITAE。 在論文中包含使用系統建模中的開迴路測試法和嘗試錯誤法(Trial and Error Task)去近似出六軸動感平台的轉移函數，並利用粒子群聚演算法去搜尋PID控制器的參數，並且與傳統調校的Z-N經驗法則來作比較。最後實驗結果證明粒子群聚演算法所搜尋出來的PID參數，不但保留其原本的穩定度，還可以改善其過大的超越量，並獲得較好的控制響應。[[abstract]]In this study we used Particle Swarm Optimization (PSO) to tune the parameters of PID controller Some error-criteria are the fitness function the performance index are used to judge the performances The performance index have three functions: (1) The integral square error criterion (ISE) (2) The integral absolute error criterion (IAE) (3) The integral time absolute error criterion (ITAE) Otherwise the “open loop testing method” and “trial and error method” are employed to obtain the transfer function of Stewart platform In order to take optimum parameters of PID controller the PSO algorithm be used and results are compared with traditional Z-N tuning rule Finally the experimental results verify the PID parameters based on PSO algorithm not only keep system stable but also decrease overshoo