Adaptive fuzzy control of DC motors using state and output feedback

Conventional PID of state feedback controllers for DC motors have poor performance when changes of the motor or load dynamics take place. To handle this shortcoming adaptive fuzzy control of DC motors is proposed. Neuro-fuzzy networks are used to approximate the unknown motor dynamics. The information needed to generate the control signal comes from feedback of the full state vector or from feedback of only the system’s output. In the latter case a state observer is used to estimate the parameters of the state vector. The stability of the closed-loop system is proved with the use of Lyapunov analysis. The performance of the proposed control approach is evaluated through simulation tests.