Self-tuning PI Controllers via Fuzzy Cognitive Maps

Abstract

Abstract. In this study, a novel self-tuning method based on fuzzy cognitive maps (FCMs) for PI controllers is proposed. The proposed FCM mechanism works in an online manner and is activated when the set-point (reference) value of the closed loop control system changes. Then, FCM tuning mechanism changes the parameters of PI controller according to systems' current and desired new reference value to improve the transient and steady state performance of the systems. The effectiveness of the proposed FCM based selftuning method is shown via simulations on a nonlinear system. The results show that the proposed self-tuning methods performances are satisfactory.

The feedback stabilization of finite-state fuzzy cognitive maps

“…Zhao et al (2020) proposed an FCM model to dynamically adjust the levels of autonomy (LOA) of the human-UAVs team, where the designed situated FCM can be used to model the relations among tasks, situations, human states and LOA. Besides, FCMs have also been utilized in the controls of wheeled mobile robots (WMR) (Amirkhani et al, 2020b), self-tuning PI controllers (Yesil et al, 2013) and manufacturing systems (Stylios and Groumpos, 1999). Not only the applications of FCMs are more widespread, but there are also more types of extensions about FCMs.…”

Generalized fuzzy cognitive maps: a new extension of fuzzy cognitive maps

“…FCM has received special attentions from the scientific community and done many achievements since it can provide a powerful tool to manipulate knowledge imitating human reasoning and thinking. FCM has used to solve many problems like fuzzy control (Stylios and Groumpos 1999), approximate reasoning (Khan and Quaddus 2004), strategic planning (Konar and Chakraborty 2005), data mining analysis (Yang and Peng 2009), virtual worlds and network models (Dickerson and Kosko 1993), and so on Gandhi 2013, 2014;Kandasamy and Indra 2000;Jorge et al 2011;Yesil et al 2013;Ganguli 2014;Papageorgiou and Iakovidis 2013;Salmeron and Papageorgiou 2014;Glykas 2013;Nápoles et al 2013;Gray et al 2014;Stylios and Groumpos 2000). It is noted in the real application that Papageorgiou (2011) presents a novel framework for the construction of augmented FCMs based on fuzzy rule-extraction methods for decisions in medical informatics.…”

Triangular fuzzy number representation of relations in Fuzzy Cognitive Maps

“…One possible reason for the lack of reported investigation into goal-oriented FCM analysis is the difficulty in reversing the matrix multiplication and non-linear transformations involved in computing successive FCM states [4]. As mentioned in [5], there is a vast interest in FCMs and this interest on the part of researchers and industry is increasing, especially in the areas of control [4], [6], [7], business [8], [9], medicine [10]- [12], robotics [13], emotion modeling [14], environmental science [15], [16], education [17], information technology [16] and self-tuning controller design [19].…”