| Summary: | In this study, an optimized linear regression controller is proposed for velocity control of DC motor. System is tested under disturbances of different types. Step, sinusoidal and trapezoidal functions are used as reference input. A linear single layer network of weights is used to calculate the necessary armature voltage by giving past measured velocity, past reference and certain amount of future reference as inputs. Training data are generated using Proportional Integral (PI) controller parameters which are optimized using particle swarm optimization (PSO). In this first phase of training, pseudo-inverse solution is used to find the coarse parameters of the network. These parameters give suboptimal results with low performance even lower than that of PSO-optimized PI controller. In the second phase of training, parameters of the network are fine-tuned using PSO algorithm again, this time for optimization of network parameters. Quite encouraging results including up to more than 1,500% increase in performance are obtained and reported.
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