Neural approximation of fuzzy gain-scheduled PID control for a dual-crank strandbeest walking mechanism

Basit Öğe Kaydı
dc.authorid0009-0008-0959-311X
dc.authorid0000-0001-9720-9852
dc.contributor.authorCoşkun, Yusuf
dc.contributor.authorÖzyılmaz, Lale
dc.date.accessioned2026-11-11T11:19:22Z
dc.date.available2026-11-11T11:19:22Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümü
dc.description.abstractThis study presents a simulation-based comparison of PID, Fuzzy-PID, and artificial neural network (ANN)-assisted gain-scheduled PID controllers for a modular dual-motor Strandbeest walking mechanism. The mechanism was designed in SolidWorks and transferred to MATLAB/Simscape Multibody, where each leg group is driven by an independent crank and the crank angle is controlled through a joint torque actuator. The ANN is not used as a stand-alone controller; instead, it provides a smooth approximation of the fuzzy gain-scheduling map used in the Fuzzy-PID controller. The controllers are evaluated under step, sinusoidal, disturbance-rejection, and square-wave reference scenarios. The ANN approximated the fuzzy gain map with coefficients of determination of 0.99, 0.92, and 0.83 for the proportional, integral, and derivative gains, respectively. The ANN-assisted controller reduced tracking error in the sinusoidal, square-wave, and disturbance cases, while the Fuzzy-PID controller achieved the lowest stepresponse overshoot. The results show that neural approximation can provide a computationally simple and smooth gain-scheduling structure for simulation-based crank-angle control, although it should not be interpreted as an exact closed-loop reproduction of the original Fuzzy-PID controller.
dc.identifier.citationCoşkun, Y., & Özyılmaz, L. (2026, 9-12 Eylül). Neural approximation of fuzzy gain-scheduled PID control for a dual-crank strandbeest walking mechanism. 2026 MIPRO Robotics Technologies and Applications Conference, Zadar, Croatia. pp. 94-99. https://doi.org/10.1109/MIPRORobotics70521.2026.11761738
dc.identifier.doi10.1109/MIPRORobotics70521.2026.11761738
dc.identifier.endpage99
dc.identifier.isbn9798319500809
dc.identifier.isbn9798319500786
dc.identifier.isbn9798319500793
dc.identifier.startpage94
dc.identifier.urihttps://doi.org/10.1109/MIPRORobotics70521.2026.11761738
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1707
dc.institutionauthorCoşkun, Yusuf
dc.institutionauthorid0009-0008-0959-311X
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2026 MIPRO Robotics Technologies and Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectStrandbeest Mechanism
dc.subjectJansen Linkage
dc.subjectSimscape Multibody
dc.subjectPID Control
dc.subjectFuzzy Logic
dc.subjectNeural Networks
dc.subjectGain Scheduling
dc.subjectCrank Angle Tracking
dc.titleNeural approximation of fuzzy gain-scheduled PID control for a dual-crank strandbeest walking mechanism
dc.typeConference Object
dspace.entity.typePublication

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