کنترل ارتعاشات سازه‌ها با استفاده از کنترل پیش‌بین مدل بهبودیافته با فیلتر کالمن

نوع مقاله : مقاله پژوهشی

نویسندگان

مرتضی قربانی عوری

امین قلی‌زاد

مونا شعاعی پرچین

گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه محقق اردبیلی، اردبیل.

10.22067/jfcei.2025.94471.1362

چکیده

کنترل پیش‌بین مدل به خانواده‌ای از کنترل‌گرها اطلاق می‌شود که از مدل پیش‌بینی شده پاسخ سیستم در محاسبه‌ی نیروی کنترلی استفاده می‌کنند. حضور عدم‌قطعیت در معادلات و شبکه‌های جمع‌آوری داده در مسائل عملی گریزناپذیر بوده و عملکرد سیستم را در پیش‌بینی رفتار آینده و محاسبات نیروهای کنترل دچار اشکال می‌نماید. برای غلبه بر این مشکل می‌توان از فیلتر کالمن حالت ماندگار در پیش‌بینی پاسخ سیستم دینامیکی استفاده نمود که کارایی آن در کاهش پیچیدگی محاسباتی و کاهش افق پیش‌بین، که انتظار می‌رود منجر به بهبود عملکرد کنترل گردد، تاکنون مورد مطالعه قرار نگرفته است. در پژوهش حاضر با هدف بهبود گام‌های پیش‌بینی کنترل‌گر پیش‌بین مدل، ارتعاشات دینامیکی سازه‌ برشی یازده طبقه مجهز به میراگر تاندون فعال با الگوریتم پیش‌بین مدل ترکیب‌ شده با فیلتر کالمن خطی حالت ماندگار کنترل شده است. نتایج نشان می‌دهد که کنترل پیش‌بین مدل بهبودیافته با فیلتر کالمن حالت ماندگار با گام‌های محاسباتی کم‌تر (105 گام محاسباتی کمتر) نسبت به الگوریتم متعارف پیش‌بین مدل، پاسخ تغییرمکان سازه تحت زلزله کوجاالی را بهبود بخشیده و مقادیر نیروهای کنترلی را تا 57 درصد کاهش داده است.

کلیدواژه‌ها

افق پیش‌بین

الگوریتم پیش‌بین مدل

فیلتر کالمن حالت ماندگار

کنترل ارتعاشات

میراگر تاندون فعال

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عنوان مقاله English

Structural Vibrations Control with Model Predictive Algorithm Improved with Kalman Filter

نویسندگان English

Mortaza Ghorbani ouri

Amin Gholizad

Mona Shoaei parchin

Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.

چکیده English

Model Predictive Control (MPC) refers to a family of controllers that utilize a predicted model of the system response in calculating control forces. The presence of uncertainties in equations and data acquisition networks is inevitable in practical problems and can impair the system’s performance in predicting future behavior and computing control forces accurately. To overcome this issue, a steady-state Kalman filter can be used to predict the response of dynamic systems. However, its effectiveness in reducing computational complexity and decreasing the prediction horizon, which is expected to improve control performance, has not yet been thoroughly investigated. In the present study, aiming to reduce the prediction steps of the MPC, the dynamic vibrations of an eleven-story shear structure equipped with active tendon dampers were controlled using a MPC algorithm combined with a steady-state linear Kalman filter. The results indicate that the improved Model Predictive Controller with the steady-state Kalman filter, with fewer computational steps (105 fewer computational steps), reduce the structural displacement response under the Kocaeli earthquake and reduces control force magnitudes by up to 57% compared with the conventional Model Predictive Control algorithm.

کلیدواژه‌ها English

Active Tendon

Model Predictive Control

Prediction Horizon

Steady-State Kalman Filter

Vibration Control

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