পদ্ধতির তুলনা করুন

	মডেল প্রেডিকটিভ কন্ট্রোল	হ্যামিল্টন-জ্যাকোবি-বেলম্যান সমীকরণ
ক্ষেত্র	নিয়ন্ত্রণ তত্ত্ব	নিয়ন্ত্রণ তত্ত্ব
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1978	1957
প্রবর্তক	Jacques Richalet	Richard Bellman
ধরন	algorithm	algorithm
মৌলিক উৎস	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Bellman, R. (1957). Dynamic Programming. Princeton University Press.
অপর নাম	MPC, Receding Horizon Control	HJB Equation, Bellman Equation, Dynamic Programming
সম্পর্কিত	5	3
সারসংক্ষেপ	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.	The Hamilton-Jacobi-Bellman (HJB) equation is a partial differential equation characterizing the optimal cost-to-go function in dynamic programming. Developed by Bellman in 1957, HJB provides both necessary and sufficient conditions for optimality, enabling elegant theoretical analysis and numerical solutions for optimal control problems. HJB is fundamental to reinforcement learning, approximate dynamic programming, and real-time control.