An IMC based fuzzy self-tuning mechanism for fuzzy PID controllers

Savran, Akin Ilker; Beke, Aykut; Kumbasar, Tufan; Yeşil, Engin

doi:10.1109/inista.2015.7276771

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The FVR is constructed with a Takagi-Sugeno (TS) FLS that uses and employs the product implication and the center of sets defuzzification method (Mendel, 2017). The inputs are normalized into the universe of discourse of the antecedent Membership Functions (MFs) as follows (Mann et al, 1999; Savran et al, 2015)…”

Section: Fuzzy Logic-based Car Control Systemmentioning

confidence: 99%

A fuzzy logic-based autonomous car control system for the JavaScript Racer game

Etlik

Korkmaz

Beke

et al. 2019

Transactions of the Institute of Measurement and Control

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In this study, we propose a fuzzy logic-based autonomous car control system and its deployment into the JavaScript Racer game. The main design goal of the self-driving car control system is to end up with an intelligent vehicle control system that is capable to keep the car finish race in the shortest time as possible while keeping the car on the road course regardless the road conditions. To accomplish such a design goal, the proposed intelligent driving system takes up and merges the frameworks of control theory, fuzzy logic and image processing. The proposed structure is composed of vision-based lane detection system, fuzzy logic-based position reference (FPR) generator, fuzzy logic-based velocity reference (FVR) generator, low level position and velocity controllers. The vision-based lane detection system solves the road curve estimation problem in real time and provides this estimation feedback for the FPR and FVR to generate appropriate position and velocity reference signals. The design of the intelligent FPR and FVR generators is accomplished by converting expert knowledge into fuzzy rules. The generated reference signals from the FPR and FVR are then tracked by low level position and velocity controllers. The proposed structure is capable to percept the environment (i.e. road tracking), make decisions (reference signal generation) and control car dynamics (i.e. speed and position) as expected from an autonomous system. The paper presents the structure and design of the intelligent driving system by providing all the design steps in detail. The efficiency of the fuzzy logic-based car control system is shown with results collected from the game environment. It is revealed that the developed intelligent system can successfully operate in various racetracks and different operating points.

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Section: Fuzzy Logic-based Car Control Systemmentioning

confidence: 99%

A fuzzy logic-based autonomous car control system for the JavaScript Racer game

Etlik

Korkmaz

Beke

et al. 2019

Transactions of the Institute of Measurement and Control

Self Cite

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“…The internal model control algorithm(IMC) can effectively improve the motion error precision and enhance the anti-interference of control system, which has been widely concerned by many scholars. In Savran A I, Beke A, Kumbasar T, et al(2015), a simple and self-adjusting IMC-API controller was proposed for small industrial water tank. The experimental result showed that the proposed method can enhance the robustness of the system and restrain the parameter disturbance.…”

Section: Introductionmentioning

confidence: 99%

Two-degree-of-freedom internal model position control and fuzzy fractional force control of nonlinear parallel robot

Wen

Zhang

et al. 2019

International Journal of Systems Science

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“…Internal model control (IMC) method is proposed by Garcia and Morari formally in 1982 [8]. IMC method is used widely in many industry environments.…”

Section: Introductionmentioning

confidence: 99%