The speed of the Induction motor can be adjusted to a great extent so as to provide easy control and high performance. In this paper an effort is made to develop a prototype model to control the speed of an induction motor using PSO-ANFIS hybrid technique. We describe the use of Particle Swarm Optimization (PSO) and ANFIS for designing an optimal fuzzy logic controller of an Induction Motor. We have used two input parameters like speed, torque and output is firing angle. PSO-ANFIS speed controller obtains better dynamic behavior and superior performance of the Induction motor speed control. Similar approach can be correlated to the control of plasma column and which can be implemented in fusion reactor to control the plasma column. Plasma position and shape control is very crucial for the overall performance of the fusion reactor such as tokamak. The quality of the discharge in the Saskatchewan TORus-Modified (STOR-M) tokamak is strongly related to the position of the plasma column within the discharge vessel. If the plasma column approaches too near the wall, then either minor or complete disruption occurs. Consequently it is necessary to be able to control dynamically the position of the plasma column throughout the entire discharge. A comparison analysis of PSO-ANFIS and Fuzzy Back Propagation algorithm is taken in our work to control the speed of induction motor, where the PSO-ANFIS gives better result in terms of fast computing.

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Major disruptions in tokamak plasmas need to be identified well before their occurrence and appropriately mitigated. Otherwise, it may dump the heat and electromagnetic load to the vessel and its surrounding plasma-facing components. A predictor system based on precursor diagnostics may help in forecasting the disruptive events in tokamak plasma and raise the alert beforehand to take necessary actions to prevent the major damages inside the vacuum vessel. This paper describes a predictor system built with a few selected diagnostic signals from the ADITYA tokamak and trained on a time-sequence long short-term memory network to predict the occurrence of disruption to 7-20 ms in advance with an accuracy of 89% on the testing set of 36 disruptive and 6 non-disruptive shots. This real-time network can infer to one time-step results under 170 µs on an Intel Xeon processor running python, suggesting minimal computation cost and best suited for the real-time plasma control applications.

Uplink Digital is a communication system developed for a Califomia based flight simulation company. This paper covers the implementation of an embedded microcontroller within the system. In order to familiarize the reader with the Uplink Digital system a general overview of operation is covered first, along with a discussion of the flight simulation company's existing analog communication system. The hardware and software design details are presented next. Finally a comparison of Uplink Digital with the analog system is discussed.