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This paper proposes a coordinated positioning control network for three direct drive, linear switched reluctance machines. To obtain the optimized network control parameters, the distributed linear quadratic regulator is applied. Different communication topologies are studied for both the dynamic tracking and the coordination performance of the network. Experimental results demonstrate that an absolute static tracking precision of 20~\mu \textm can be achieved from the “cycle” communication topology and an absolute static coordination precision of 11~\mu \textm can be obtained from the “path” communication topology. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed, coordinated motion control network based on multiple direct-drive, linear switched reluctance machines (LSRMs). A hierarchical, two-level synchronization control strategy is proposed for the four LSRMs based motion control network. The high-level, reference signals agreement algorithm is first employed to correct the asynchronous behaviors of the position commands. Then, the low-level tracking synchronization method is applied for the collaborative position control of the four LSRMs. The proposed two-level, fault-tolerant control strategy eliminates the asynchrony of the reference signals and it also guarantees the coordinated tracking control performance of the four LSRMs. Experimental results demonstrate that effective coordinated tracking control can be ensured, based on the successful agreement of reference signals and an absolute tracking error falling within 2 mm can be achieved. [ABSTRACT FROM AUTHOR]

In the advanced manufacturing industry, planar switched reluctance motors (PSRMs) have proved to be a promising candidate due to their advantages of high precision, low cost, low heat loss, and ease of manufacture. However, their inverse force function, which provides vital phase current command for precise motion, is highly nonlinear and hard to be accurately modeled. This paper proposes a novel inverse force function using sparse least squares support vector machines (LS-SVMs) to achieve nonlinear modeling for precise motion of a PSRM. The required training and testing sets of sparse LS-SVMs are first obtained from experimental measurement. A sparse LS-SVMs regression is further developed using training set to accurately model the inverse force function. Accordingly, the function is tested via the testing set to assess its feasibility. Finally, the proposed approach is applied to the PSRM system with dSPACE controller for trajectory tracking, and its effectiveness and superior performance are verified through experimental results. [ABSTRACT FROM PUBLISHER]

In planar switched reluctance motors (PSRMs), the electromagnetic force ripple minimization is important to reduce the noise and vibration for high-precision motions. In this paper, an improved PSRM by optimization design for minimization of the electromagnetic force ripple is proposed. The structure, principle, and mathematical model of the PSRM are clarified. Based on Ansoft Maxwell, optimization design of the PSRM is performed by improved structure with smaller pole pitch and incremental number of mover poles. Experiments of the improved PSRM are implemented based on dSPACE. The experimental results verify that the improved PSRM effectively reduces electromagnetic force ripple with planar trajectory tracking. [ABSTRACT FROM AUTHOR]