An Efficient Model Predictive Control method for Real-time complex nonlinear systems

This paper examines the use of Model Predictive Control (MPC) to control complex nonlinear systems in real time. In addition to its numerous parameters, the importance of the MPC paradigm for systems with complicated dynamics is analyzed. MPC’s most recent research works in application aspects were taken into account, and an investigation was carried out based on the field of application by emphasizing the key features of predictive strategy for optimal control. Because real-time requirements are regarded as a major challenge, predictive tactics for each application in real time are more focused on inquiry. However, recent advances in Machine Learning (ML) techniques have enabled its concepts to be applied to control systems. The main purpose of this research is to provide information on recent improvements in MPC and its integration with Machine Learning concepts.