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Control System Design
An Adjustable Speed Brushless DC MotorSystem
System Control Robotics Neural Networks A SteadyState Model For Parameter Design
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according active adaptive algorithm analysis application approach assumed block calculated circuit coefficients components condition considered constant control system converter corresponding defined described detection determined developed disturbance dynamic effective electric electronic element equation error estimation example experiments Figure filter frequency function given IEEE implemented increase initial input integration Introduction layer learning linear load manipulator matrix means measurement method mode module motion motor neural network nonlinear object obtained operation optimal output parameters path performance phase plant position prediction presented problem proposed range reduced reference region regulation represented respectively response robot robust sampling scheme selected sensor shown shows signal simulation space speed stability step structure switching Table temperature tion Trans unit University variable vector voltage weight