Neural net-based robust controller design for brushless DC motor drives

A nonlinear neuro-controller is developed for controlling the speed of brushless dc motors operating in a high performance drives environment. The control inputs and the identification parameters of the system are adjusted simultaneously in real time using a system composed of three hidden-layer dynamic neural networks while the system is in operation. The control architecture adapts and generalizes its learning to a wide range of operating conditions and provides the necessary abstraction when measurements are contaminated with noise. The problem of persistently spanning excitation faced with the use of an online neuro-controller is addressed. In particular, the ability of the neuro-controller to “remember” previously trained reference tracks when confronted with an input excitation that is markedly different from what it was trained with is investigated. The intent is to capture the nonlinear dynamics of a brushless dc motor over any arbitrary time interval in its range of operation. The sensitivity of real time neuro-controllers to random changes in the load torque also is investigated and very promising results are observed