| Gearing Motor is a new kind of reluctance motor which combines the reducer and servo motor. Compared with conventional motors, The Gearing motor has many individual features. This dissertation is focused on the nonlinear modeling, torque ripple suppression and control methods.Building accurate flux-linkage model of motor is the key to improve the performance of the control system. After measuring the accurate flux-linkage data, based on the magnetization curve obtained by fitting non-linear analytic flux, using principle of virtual displacement calculation meshing motor torque-angle characteristic, and adaptive fuzzy neural network used for meshing motor modeling, obtained good results.To overcome the disadvantage of higher torque ripple in Gearing Motor, one method of instantaneous torque control by optimum profiling of the phase currents is presented. The torque ripple minimization can be achieved by optimum profiling of the phase currents based on the BPNN torque reverse model. Simulation results verify the feasibility of this torque ripple minimization technique.Reduce the torque ripple is usually to reduce the cost of efficiency of the system, this paper presents both reduce the torque ripple can optimize the system efficiency of the mating motor high-performance system design, By selecting the commutation points to make the system run in the most efficient point, and torque distribution function optimization with the current waveform, simulation results verify the effectiveness of the program.A high degree of Gearing Motor magnetic circuit saturation, as well as a serious non-linear variable led to the traditional control methods can not achieve the desired dynamic performance. This article will introduce fuzzy control gear motor control, to improve the system dynamic performance of conventional fuzzy control for the existence of steady-state error problem, a method of variable Analects of Confucius, suitable for high-precision motor control gear.
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