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In this article, the iterative learning control (ILC) problem is investigated for a class of stochastic time-varying systems with variable pass lengths. The randomness of the pass lengths is described by the recursive interval Gaussian distribution, and a modified iteration-average operator is developed to construct the novel ILC scheme for overcoming the limitation of conventional ILC algorithms that every pass must end in a fixed time of duration throughout the repetition. The proposed ILC approach works effectively to guarantee the boundedness of the tracking errors, which is demonstrated by a practical case study on a type of robot manipulator with two joints. [ABSTRACT FROM AUTHOR]

In this article, a position estimation method is proposed to realize sensorless control of planar switched reluctance motor (PSRM) with core loss. First, a square-wave voltage generated through bipolar pulsewidth modulation (PWM) is injected into the idle phase of a PSRM, thereby generating core loss. The corresponding core-loss average power (CLAP), which contains position information, is calculated in real time. Then, depending on the six-phase CLAP, the initial position can be estimated for the PSRM startup. To stabilize the position estimation in different regions of pole pitches, position data measured in a long stroke are utilized to determine a function mapping from the CLAP with respect to position. Furthermore, a sensorless control system and commutation strategy are designed. Finally, the capability of different methods for position estimation and sensorless control of a PSRM system are experimentally investigated. The feasibility and effectiveness of the proposed method are verified through the experimental results. [ABSTRACT FROM AUTHOR]

An accurate mover position is important for a planar switched reluctance motor (PSRM) to achieve precision positioning; however, position sensors are susceptible to the influence from harsh conditions and are relatively expensive. In this article, an assistant-mover-based position estimation method is proposed to increase the reliability and reduce the cost of a PSRM system. The method is based on a mapping relationship between the flux density and position of the assistant mover. First, the configuration of the assistant movers is presented. Then, an analytical model of the flux density of the assistant mover is developed by using an equivalent magnetic circuit method. Based on the analytical model, the parameters of the assistant mover are determined. According to the flux density characteristics, a position estimation algorithm with the advantages of not requiring previous experimental data, simple computation, and small memory consumption is developed. Moreover, considering the deviation in the characteristics of each assistant mover, a compensation strategy is proposed to obtain better position estimation accuracy. Finally, the proposed method is implemented with the PSRM system. The effectiveness of the proposed position estimation method is verified experimentally. [ABSTRACT FROM AUTHOR]