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ARTIFICIAL NEURAL NETWORKS APPLICATIONS
ON THE DESIGN OF FAULT TOLERANCE CONTROLLER BASED ON MATHEMATICAL
A MICROSIMULATtON OF A MANMACHINE SYSTEM
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ABSTRACT adaptive control adaptive sampling algorithm application array artificial neural network base block booster closed-loop poles coefficient constant control force control system coordinates curve deburring derived determine developed differential equations elevation axis Engineering environment error feedback flexible manipulator friction fuzzy fuzzy logic gear Genetic Algorithms given IEEE IEEE Trans implementation inertia input instance inverse kinematic KF controller kinematic learning linear mating matrix method minimal motion motion planning motor neural network nodes noise nonlinear obtained operator optimization orthogonal complement output paper parameter estimation performance PID control poles polynomial position problem Proc pseudoinverse ratio response robot arm robot system robotic manipulator root locus rotation sensor shown in Figure signal simulation results solution solved speed stability step response techniques torque torque loop trajectory transfer function transformation transient uoanau values variables vector velocity workcell world model zero