This paper develops a GA ANFIS controller design method for temperature control in plastic extrusion system. Temperature control of plastic extrusion system suffers problems related to longer settling time, couple effects, large time constants, and undesirable overshoot. The system is generally nonlinear and the temperature of the plastic extrusion system may vary over a wide range of disturbances. The system is designed with three controllers. The proposed GA ANFIS controller is the most powerful approach to retrieve the adaptiveness in the case of nonlinear system. In this research the control methods are simulated using simulink. Relatively the methodology and efficiency of the proposed method are compared with those of the traditional methods and the results obtained from GA ANFIS controller give improved performance in terms of time domain specification, set point tracking, and disturbance rejection with optimum stability.

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In this paper, a robust control system with the sliding mode fuzzy controller and the additional PID compensator (PIDSMFC) is presented. The additional compensator relaying on the sliding mode theory is used to improve the dynamical characteristics of the drive system. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. In this method, using high control gain to overcome uncertainties lead to chattering phenomena in control law which can excite unmodeled dynamics and may harm the plant. Diff erent approaches, such as intelligent methods are used to abate these drawbacks. In order to enhance the sliding mode controller performance, fuzzy logic is used here. For this purpose, a PID outer loop is used in the control law then the gains of the sliding term and PID term are tuned online by afuzzy system. Hence, the chattering is avoided and response of the system is improved against external load torque here. Presented simulation results confirm the above claims and demonstrate the performance improvement in this case.

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This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.