Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

This paper explores the recent advancements in Model Predictive Control (MPC) for nonlinear systems, particularly in industrial instrumentation and control engineering. The focus is on improving stability, robustness, and computational efficiency in real-time applications. The paper reviews several modified MPC algorithms, highlighting their impact on performance in controlling complex nonlinear processes. Several case studies in chemical, automotive, and aerospace industries are used to demonstrate the effectiveness of these advanced MPC methods. The importance of sensor fusion, real-time data processing, and optimization techniques is also discussed to address future challenges in nonlinear system control.

Keywords: Nonlinear Systems, Model Predictive Control, Real-Time Optimization, Robustness, Industrial Applications