The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect additional savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in automotive industry and academia many years. Their modern vehicles is, however, still lagging behind. While conventional exact non-exact techniques such as Dynamic Programming Model Predic...

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which ...

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which ...

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...

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from inputout...

In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is sol...