Development of neural fractional order PID controller with emulator

This paper focuses on tuning parameters of fractional order PID controller (FOPID) by using neural networks (NNs). For tuning the coefficients of the controller and orders of fractional derivative and integrator, five exclusive NNs are employed. Moreover, an emulator is used to identify the plant’s behavior. Extended Kalman Filter (EKF) algorithm is used to update the weights of the controller’s NNs, and Back Propagation (BP) algorithm is used for the weight updating procedure of the emulator’s NNs. The proposed neural fractional order PID controller (NFOPID) is capable of being applied to various plants. Thus, two plants with different dynamics are examined. One is vibration damping of a Euler–Bernoulli beam, which has a fast dynamic, and the other is a time-delayed system like temperature control of a tempered glass furnace. The controller could deal appropriately with these tasks and is compared for accuracy and robustness with other controllers. The results were satisfactory for both systems. •Neural network fractional order PID controller is developed.•The controller uses a neural emulator to simulate the plant’s behavior.•The designed controller in this work is plant-independent and can be applied to different nonlinear models.•The controller is checked in the presence of disturbance, uncertainty, and control effort limitation.•The controller results were compared to ADRC and SDC methods.