পদ্ধতির তুলনা করুন

	সক্রিয় প্রতিবন্ধকতা প্রত্যাখ্যান নিয়ন্ত্রণ	অ্যাডাপ্টিভ কন্ট্রোল	মডেল প্রেডিকটিভ কন্ট্রোল
ক্ষেত্র	নিয়ন্ত্রণ তত্ত্ব	নিয়ন্ত্রণ তত্ত্ব	নিয়ন্ত্রণ তত্ত্ব
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2009	1983	1978
প্রবর্তক	Jingquan Han	Karl J. Astrom	Jacques Richalet
ধরন	algorithm	algorithm	algorithm
মৌলিক উৎস	Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906.	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
অপর নাম	ADRC, Disturbance Rejection Control	Self-Tuning Control, Parameter Estimation Control	MPC, Receding Horizon Control
সম্পর্কিত	2	3	5
সারসংক্ষেপ	Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requiring precise plant models, making it practical for real-world systems with significant uncertainty and disturbances.	Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from aircraft with fuel depletion to industrial systems with aging components.	Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously.