Advances in Process Control with Real Applications

Advances in Process Control with Real Applications presents advanced models for the control of nonlinear complex processes, including first principle, data driven, and artificial intelligence models, along with inferential state estimation & stochastic and evolutionary optimization techniques. The book highlights the significance and importance of advanced controllers with several real applications concerning chemical and biochemical processes. It covers control approaches such as generalized predictive control (GPC) with and without constraints, linear & nonlinear model predictive control (MPC), dynamic matrix control (DMC), nonlinear control such as generic model control (GMC), and more. Additional sections shine a light on globally linearizing control (GLC) and nonlinear internal model control (NIMC), optimal & optimizing control, inferential control, intelligent control based on fuzzy reasoning, neural network, machine learning, and evolutionary computation.