Rana J. Masood, Daobo Wang, and Muhammad F. Manzoor
Permanent magnet DC motor (PMDC), model free adaptive control(MFAC), virtual reference feedback tuning (VRFT), pseudo partialderivative (PPD)
The significance of mathematical modelling in the classical control theory cannot be denied. However, the nonlinear system modelling is more complex than linear modelling and sometimes it is chal- lenging to produce a nonlinear mathematical model of the system. The proposed work is mainly focused on data-driven virtual refer- ence feedback tuning (VRFT) control combined with a model free adaptive control (MFAC) algorithm. The basic control structure of the VRFT system uses a close loop model as a reference. However, the input and output data model of the closed loop linear system is linearized in tight format. The reference model output error and the system expected output error are used as a control input. The estimated value of the pseudo partial derivative (PPD) in the past time is introduced to the new control law to improve the utilization rate of PPD. The whole performance of the controller design is es- sentially data driven and it does not demand any prior information about the system model. The VRFT-based MFA control system is applied to speed control of the permanent magnet DC motor in the Simulink platform. Moreover, the simulation results show that 8.6% speed tracking error is reduced using the proposed control algorithm as compared to VRFT and 4.7% is reduced as compared to MFA-based algorithms.
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