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Alfian Ma'arif
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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 143 Documents
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Design and Optimization of Structural Parameters of Hydraulic Retarder Blades Hossain, Md Shimul; Jiaxin, Wang; Merda, Md Ruqul
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.291

Abstract

Hydraulic retarders are auxiliary braking devices in heavy-duty vehicles where rotor blade structural integrity directly affects system reliability and safety. Under operational conditions, blades experience combined centrifugal and fluid pressure loading, making geometric optimization essential to prevent stress concentration and deformation failure. This study employs finite element analysis to conduct a systematic parametric investigation of rotor blade design. Four key parameters—blade number (32-36), thickness (3-5 mm), wedge angle (35°-50°), and material (structural steel, AISI 4140, aluminum bronze, CFRP)—were evaluated under identical operating conditions (2000 rpm rotational velocity, 0.5 MPa uniform pressure). Equivalent stress, deformation, strain, and safety factor were used as comparative metrics. Results demonstrate that geometric optimization significantly outperforms material addition in improving structural performance. The optimized configuration achieves substantially enhanced safety margins while maintaining deformation within elastic limits. Material comparison identifies AISI 4140 as offering the optimal balance of strength and stiffness. These findings provide quantifiable design guidance for hydraulic retarder development and establish a systematic optimization framework applicable to rotating machinery components.
Wheel Velocity Control for Electric Car with Kalman Filter and PID Optimization Falaah, Fadjar Nur; Ma'arif, Alfian
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.176

Abstract

This research addresses the challenge of achieving precise rotational speed control for DC motors in electric vehicles, a critical factor for ensuring smooth operation, energy efficiency, and safety. The study integrates a Kalman Filter with a PID Controller to mitigate sensor noise and external disturbances while minimizing steady-state errors. The Kalman Filter effectively reduces noise from rotary encoder sensors, enabling accurate speed estimation with multiplier values of countPulseM1 = 20.0 and countPulseM2 = 40.9. Optimal Kalman Filter parameter ratios were identified as R = 10.0, Q = 0.0001 for motor M1 and R = 8.0, Q = 0.0001 for motor M2, which minimized noise but resulted in slower motor responses compared to lower ratio configurations. To address this limitation, the PID controller was fine-tuned, yielding optimal parameters of Kp = 1.1, Ki = 8.1, and Kd = 0.00036 for motor M1 and Kp = 0.9, Ki = 9.4, and Kd = 0.00009 for motor M2. These settings achieved a rise time of 0.13 seconds, overshoot of 8.69%, and steady-state error of -1.19%. Disturbance testing with a hall magnetic rotary encoder revealed motor M1 with a rise time of 0.27 seconds and M2 with 0.16 seconds, both showing robust responses but requiring faster recovery times for stabilization. While the combination of Kalman Filter and PID significantly enhances control accuracy, further improvements are necessary to reduce settling times and ensure greater stability under dynamic conditions. This work contributes valuable insights into advanced control techniques for electric vehicle drivetrains and robotic systems.
Broad Learning System: A Derivation-Based Mathematical Formulation Saputra, Dimas Chaerul Ekty; Rahmawati, Dyah Putri; Pertiwi, Affifah Mutiara; Shafarin, Muhammad Ijaz; Pertiwi, Kharisma Monika Dian; Win, Thinzar Aung; Futri, Irianna; Safitri, Pima Hani
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.

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