This work shows how to use inverted pendulum hardware in the teaching of intelligent control techniques. The concepts of a fuzzy logic based controller are applied on an inverted pendulum. The design process is explained in detail, offering a practical guide for the reproduction of both the experiment and simulation. Last but not least, the results are commented and the performance achieved with this controller is compared to that of the original one, which was delivered with the hardware. This work also suggests future developments for new undergraduate teams.

@article{zanma_2016,
title = {Optimal Input and Quantization Interval for Quantized Feedback System with Variable Quantizer},
author = {Zanma, T.; Azegami, M.; Liu, K.-z.},
journal = {IEEE Transactions on Industrial Electronics},
year = {2016},
institution = {Chiba University, Chiba, Japan},
abstract = {Networked control systems (NCSs) have received much attention in the field of robot teleoperation, telesurgery and other applications. In an NCS, it is important to compress data transmitted over a limited communication network while preserving the data required for control. We address a quantized feedback system in which the output of a plant is quantized with a variable quantization interval. For such a system, we present a novel optimal quantized feedback control. The proposed approach derives not only an optimal input, but also an optimal quantization interval in the quantizer simultaneously using the model predictive control. The approach also satisfies constraints on the state, input and quantization interval. The effectiveness of the proposed approach is validated through cart-positioning experiments. },
issn = {0278-0046},
keywords = {Quantized feedback control, Optimal control, Model predictive control},
language = {English},
publisher = {IEEE}
}

Networked control systems (NCSs) have received much attention in the field of robot teleoperation, telesurgery and other applications. In an NCS, it is important to compress data transmitted over a limited communication network while preserving the data required for control. We address a quantized feedback system in which the output of a plant is quantized with a variable quantization interval. For such a system, we present a novel optimal quantized feedback control. The proposed approach derives not only an optimal input, but also an optimal quantization interval in the quantizer simultaneously using the model predictive control. The approach also satisfies constraints on the state, input and quantization interval. The effectiveness of the proposed approach is validated through cart-positioning experiments.