Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

Fuzzy logic controller optimized by particle swarm optimization for DC motor speed control

In this paper, we presented an optimized fuzzy logic controller using particle swarm optimization for DC motor speed control. The controller model is simulated using MATLAB software and also experimentally tested on a laboratory DC motor. A comparison of the performance of different controllers such as PID controller, fuzzy logic controller and optimized fuzzy logic controller is presented as well. With reference to the results of digital simulations and experiment, the designed FLC-PSO speed controller obtains much better dynamic behavior compared to PID and the normal FLC designed. Moreover, it can acquire superior performance of the DC motor, and also perfect speed tracking with no overshoot. The optimized membership functions (MFs) are obviously proved to be able to provide a better performance and higher robustness in comparison with a regular fuzzy model, when the MFs were heuristically defined. Besides, experimental results verify the ability of proposed FLC under sudden change of the load torque which leads to speed variances.