Abstract
The focus of this paper is the design and implementation of adaptive network based fuzzy inference (ANFIS) controller by using the training data obtained from a system controlled by Linear Quadratic Regulator (LQR). This work is motivated by the need to remove stochastic observer required for LQR in noisy environments while at the same time to have optimal performance. This theory is validated by taking the well investigated case of Active Suspension System of a Quarter Vehicle. The performance of the obtained ANFIS controller is tested using \({Quanser}^{\copyright }\) Active Suspension Plant. It is observed that the ANFIS controller gives good close loop performance, while removing the requirement of a stochastic observer.
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© 2016 Springer International Publishing Switzerland
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Rashid, U., Jamil, M., Gilani, S.O., Niazi, I.K. (2016). LQR Based Training of Adaptive Neuro-Fuzzy Controller. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_31
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DOI: https://doi.org/10.1007/978-3-319-33747-0_31
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