Modeling torque in a switched reluctance motor for adaptive control purposes using self-organizing neural networks

Training paradigms for topology-preserving Kohonen neural networks are introduced for the purpose of identifying and controlling nonlinear systems. A procedure for locking neuron weights at specific locations in a region is presented. It exploits prior knowledge about the system of interest. As a result, superior representations of an arbitrary multivariable nonlinear mapping can be achieved. In addition, the common problem of twisted meshes in these neural networks is eliminated. The strategy introduced for preferentially training these networks at region boundaries overcomes the limitation of boundary contraction. As an example, a one-dimensional neural network is used to approximate a nonlinear function, although in general an n-dimensional mapping can be used to approximate an m-dimensional system for n⩽m. As a practical implementation, the modeling of the theoretical torque of a switched reluctance motor (SRM) as a function of position and current is presented. The topological torque representation is suitable for adaptive control of SRMs in high-performance applications