Soft-computing in the control of electrical drives

The principal constituents of soft computing are the fuzzy logic (FL), artificial neural networks (NN) and probabilistic reasoning (PR). It is generally regarded that FL primarily deals with imprecision, NN with learning, and PR with uncertainty. They have, however, overlapping boundaries and are known to be complementary rather than competitive to each other in many applications. Here, two control algorithms, one implemented by fuzzy logic and the other by a neural network, are used as the basis to highlight salient features of soft computing. A DC motor servo system with the proposed soft computing based algorithms is discussed. The fuzzy logic control employs the principles of fuzzy logic to calculate an optimal output action based on input conditions, and a knowledge base expressed in linguistic forms, thereby performing a parallel operation to control the output with a high degree of robustness against parameter change. In the neural network control, focus is on how neural networks can overcome deadzone-plus-saturation nonlinearity commonly found in the power driver of a DC servo motor. Simulation results have been performed to establish the validity of these control algorithms.