Abstract
Advanced control systems are increasingly employed for intelligent factories. Fuzzy logic control (FLC) and backward propagation neural network (BPNN) control are investigated in this paper to realize position control for a linear switched reluctance motor (LSRM) against its nonlinear characteristics. Principles for FLC and BPNN control are introduced elaborately. Simulation results via BPNN show that dynamic position errors for the LSRM can be limited to 0.1 mm. Experimental results on FLC suggest that point-to-point position tracking for the motor can achieve 0.01 mm, constraining dynamic position error in 0.1 mm. By experiments, FLC for the LSRM performs better than traditional proportional-integral-derivative (PID) control, proving the effectiveness of the alleviation of the nonlinearity for the LSRM.
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