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This paper presents the review of design variables optimization and control strategies of a Linear Switched Reluctance Actuator (LSRA). The introduction of various type of linear electromagnetic actuators (LEA) are compared and the advantages of LSRA over other LEA are discussed together with the type of actuator configurations and topologies. The SRA provides an overall efficiency similar to induction actuator of the similar rating, subsequently the friction and windage losses are comparable but force density is better. LSRA has the advantage of low cost, simple construction and high reliability compare to the actuator with permanent magnet. However, LSRA also has some obvious defects which will influence the performance of the actuator such as ripples and acoustic noise which are caused by the highly nonlinear characteristics of the actuator. By researching the design variables of the actuator, the influences of those design variables are introduced and the detail comparisons are analyzed in this paper. In addition, this paper also reviews on the control strategies in order to overcome the weaknesses of LSRA. Keyword:Actuator INTRODUCTIONLinear electromagnetic actuators (LEA) is a mechanism that generate linear motion due to the interactions of the magnetic fields and electromagnetic thrust. The major advantage of electromagnetic actuators over the conventional actuators is that it is almost maintenance free which is due to the absence of mechanical part such as gears The typical design of LEA can be characterized as three topologies: (i) Planar Single Sided; (ii) Planar Double Sided; (iii) Tubular. By comparison, the tubular topology of LEA has greater force density compare to planer topology actuator due to lesser flux leakage and tubular topology actuator minimized the stray magnetic field in the direction of travel along the stator and mover part [5]. Hence, the thrust force and

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Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.