Abstract : A rule-based self-tuning fuzzy controller is proposed and applied to the control of an AC servo system. An online fuzzy inference algorithm is designed to enable real-time online adjustment of the fuzzy control rules. Simulation results show that the performance of the AC servo system based on the rule-based self-tuning fuzzy controller is significantly improved compared with that of the general fuzzy control AC servo system. Keywords : Rule-based self-tuning fuzzy controller; Fuzzy inference; AC servo system. With the development of production and technology, AC servo systems have been increasingly widely used due to their simple structure and ease of maintenance. AC servo systems are composed of AC motors, and their mathematical models have nonlinear, coupled, and time-varying characteristics. Traditional object-model-based control methods cannot achieve satisfactory results in controlling them. In recent years, fuzzy control has been introduced into the control of AC servo systems. By utilizing the characteristics of fuzzy control, which is independent of object models and has strong robustness, the effects of coupling, nonlinearity, and parameter changes in AC servo systems have been overcome, achieving good results. The control performance of fuzzy control depends on its control rules, but control rules summarized from human experience often have certain limitations. Once the control rules of a conventional fuzzy controller are determined, they cannot be changed, and therefore cannot adapt well to changes in the dynamic characteristics of the system or the influence of random disturbances, thus affecting the effect of fuzzy control. Therefore, this paper proposes an AC servo system based on a rule-based self-tuning fuzzy controller, which enables online adjustment of the fuzzy control rules, greatly improving the fuzzy control effect and enhancing system performance. For details, please download the AC servo system based on a rule-based self-tuning fuzzy controller.