In this paper, several well-known design methods, PID control, Smith predictor, inferential control, internal model control, Dahlin controller, deadbeat control, and predictive control, are studied.
A robust internal model control (IMC) controller was designed based on a nominal linear model assuming model error to account for process nonlinearity.
In this paper, a new adaptive internal model control (IMC) controller is proposed based on just-in-time learning (JITL) technique for nonlinear process control.
The paper outlines issues regarding compressor design, compressor string modeling, control algorithms, controller performance, and surge protection.
Simulation results showed that the general use of the genetic algorithm based internal model control (GAIMC) strategy automated controller tuning that improved control performance, while its use with nonlinear compensators for the severely nonlinear reactor achieved tighter pH control for multiple operating regimes.
A feedback controller equivalent to internal model control is obtained and then PID controller is derived by an approximate frequency response matching at two low frequency points.
A direct adaptive output feedback CMAC (cerebellar model articulation controller) control based on only output measurements is proposed with unknown control gain function for a class of affine nonlinear systems.
By applying the internal model control (IMC) method, the controllers with equivalent models are designed.
In this paper, a new internal model control (IMC -based sIMC -based controller design is proposed.
