The Model-free Learning Adaptive Control for DC Motor Rotate Speed Systems

The model-free learning adaptive control (MFLAC) approach of a class of SISO nonlinear discrete-time systems based on linearization of tight format is applied to DC motor rotate speed systems . The design of controller is model-free, based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion system using novel parameter estimation algorithms. Hardware system is implemented by data acquisition (DAQ) to control DC motor. Conjuncted general PC to DAQ and controlled DAQ to produce pulse-width modulation (PWM) wave which drive the IGBT circuit to control DC motor. The effectiveness and the robustness are demonstrated for the DC motor nonlinear systems which are known to be difficult to model and control by simulation and real-time application experiment examples .