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In this decade, Internet of Things (IoT) technologies are motivating nations for digital transformation. This transformation is part of Fourth industrial revolution (Industry 4.0). Several challenges are obstacle in the digitalization, one of them is talent in this field. There are not many available automation or control labs equipped with advance automation technologies in the educational institutions. To produce more force for IoT, engineering intuitions need to improve their curriculum and engineering lab facilities. In this paper, a technology-based learning system is proposed for learning IoT. The design of this system purposely developed for control lab for undergraduates and postgraduate students. This system offers a low-cost development using industrial standard controller, which is suitable for industrial and enterprise applications prototyping. Three modules are prepared to train the students; 1)

This paper presents the finding on designing the fuzzy logic controller and analysis of the step input test on the model and control design. The PI-type fuzzy logic controller (FLC) was designed to control the velocity of the rolling spherical robot. The spherical robot model was tested with step input signal to analysis the effectiveness of the designed Fuzzy logic controller with 25-membership rule being constructed in Fuzzy toolbox MATLAB using triangular membership function and combination of gaussian and sigmoidal membership function. The output scaling factor (output gain) of FLC was tuned using Response Optimization toolbox and Particle Swarm Optimization to improve the system performance. Optimization using Matlab toolbox is done by specified the desired step response characteristics while in PSO, minimizing the Integral Absolute Error (IAE) is used as on objective of the optimization. The combined membership function shows better performance with less 8% overshoot, rise time less than 2s and settle at less than 3s after the response optimization process. Meanwhile, the PSO manage to tune the gain to reduce the IAE but contain large overshoot and longer settling time.

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