this article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (mpc) of a chemical plant. a combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (mimo) process with time delays.  an optimization procedure for a neural mpc algorithm based on this model is then developed. the p...

the control of car following is essential due to its safety and its operational efficiency. for this purpose, this paper builds a model of car following behavior based on armax structure from a real traffic dataset and design a model predictive control (mpc) system. based on the relative distance and relative acceleration of each instant, the mpc predicts the future behavior of the leader vehic...

the efficiency of induction motors decreases at light loads. efficiency optimizer control systemsadjust the motor flux value to achieve the best efficiency in a wide range of load variations. reduced fluxoperation has some other benefits such as power factor improvement and torque ripple reduction. the latter isan important issue in a direct torque controlled induction motor drive. in this pape...

An optimal predictive control approach for the suppression of thermo-acoustic instabilities in the combustor chamber is investigated. The approach uses the combustor pressure fluctuations and fuel modulation data for building internal dynamic input-output models and designing the controller. The knowledge of the physical model of the system is not required. This data-based nature of the approac...