Evaluation-Function-based Model-free Adaptive Fuzzy Control

Agus Naba

doi:10.5614/j.eng.technol.sci.2016.48.6.4

Authors

Agus Naba Study Program of Instrumentation, Department of Physics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Jalan Veteran, Malang 65145,

DOI:

https://doi.org/10.5614/j.eng.technol.sci.2016.48.6.4

Abstract

Designs of adaptive fuzzy controllers (AFC) are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC) using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark veriﬁed the proposed scheme's efficacy.

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