M. M. Namazi Isfahani, A. Rashidi, S. M. Saghaiannejad,
Volume 8, Issue 1 (March 2012)
Abstract
Torque ripple minimization of switched reluctance motor drives is a major subject based on these drives’ extensive use in the industry. In this paper, by using a well-known cascaded torque control structure and taking the machine physical structure characteristics into account, the proposed energy-based (passivity-based) adaptive sliding algorithm derived from the view point of energy dissipation, control stability and algorithm robustness. First, a nonlinear dynamic model is developed and decomposed into separate slow and fast passive subsystems which are interconnected by negative feedbacks. Then, an outer loop speed control is employed by adaptive sliding controller to determine the appropriate torque command. Finally, to reduce torque ripple in switched reluctance motor a high-performance passivity-based current controller is proposed. It can overcome the inherent nonlinear characteristics of the system and make the whole system robust to uncertainties and bounded disturbances. The performance of the proposed controller algorithm has been demonstrated in simulation, and experimental using a 4KW, four-phase, 8/6 pole SRM DSP-based drive system.
M. R. Mosavi, A. Rashidinia,
Volume 13, Issue 3 (September 2017)
Abstract
Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Function (RBF) has been developed. In many previous works all parameter of RBF NN are optimizing by evolutionary algorithm such as Particle Swarm Optimization (PSO), but in our approach shape parameter and centers of RBF NN are calculated in better way, in addition, search space for PSO algorithm will be reduced which cause more accurate and faster approach. The obtained results show that RMS has been reduced about 0.13 meter. Moreover, results are tabulated in the tables which verify the accuracy and faster convergence nature of our approach in both on-line and off-line training methods.
