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  3. Vol. 10, No. 4, November 2025
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Vol. 10, No. 4, November 2025

Issue Published : Oct 16, 2025
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Spoon Stabilization for Essential Tremor Patients Using PID Control Optimized by PSO

https://doi.org/10.22219/kinetik.v10i4.2272
Putri Ayu Zartika
Electrical Engineering Department, Universitas Brawijaya Malang
Muhammad Aziz Muslim
Electrical Engineering Department, Universitas Brawijaya Malang
Erni Yudaningtyas
Electrical Engineering Department, Universitas Brawijaya Malang

Corresponding Author(s) : Muhammad Aziz Muslim

muh_aziz@ub.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 4, November 2025
Article Published : Oct 16, 2025

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Abstract

Essential tremor is a neurological disorder that causes involuntary hand tremors, interfering with daily activities such as eating. This study developed a spoon stabilization system controlled by a Proportional-Integral-Derivative (PID) controller, which was tuned using Particle Swarm Optimization (PSO) and the Cohen-Coon method for performance comparison. The system utilized an inertial measurement unit to detect tremors, while a Kalman filter reduced noise before a microcontroller controlled a servo motor to stabilize the spoon. The system was evaluated through simulations and hardware implementation, with performance assessed based on rise time, overshoot, delay time, and settling time. The results showed that the Kalman filter significantly reduced noise, lowering the average pitch angle error deviation from 1.028° to 0.037° and the roll angle error from 0.822° to 0.031°. The PSO-based tuning outperformed the Cohen-Coon method in response speed and system stability, achieving a faster rise time (0.09 s for roll, 0.34 s for pitch), a shorter settling time (0.74 s for roll, 0.59 s for pitch), and a lower delay time (0.1 s for roll, 0.15 s for pitch). However, the Cohen-Coon method resulted in a lower overshoot for the roll angle (6.08%) compared to the PSO-based tuning (11.98%).

Keywords

Essential tremor PID Control Kalman Filter Particle Swarm Optimization (PSO) Cohen-Coon Tuning
Zartika, P. A., Muslim, M. A., & Yudaningtyas, E. (2025). Spoon Stabilization for Essential Tremor Patients Using PID Control Optimized by PSO. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(4). https://doi.org/10.22219/kinetik.v10i4.2272
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References
  1. P. Mcgurrin, J. Mcnames, T. Wu, M. Hallett, and D. Haubenberger, “Quantifying Tremor in Essential Tremor Using Inertial Sensors—Validation of an Algorithm,” IEEE J Transl Eng Health Med, vol. 9, pp. 1–10, 2021, doi: 10.1109/JTEHM.2020.3032924.
  2. J. Kim, T. Wichmann, O. T. Inan, and S. P. DeWeerth, “Fitts’ Law Based Performance Metrics to Quantify Tremor in Individuals With Essential Tremor,” IEEE J Biomed Health Inform, vol. 26, no. 5, pp. 2169–2179, May 2022, doi: 10.1109/JBHI.2021.3129989.
  3. S. M. Ali et al., “Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor,” IEEE J Transl Eng Health Med, vol. 12, pp. 194–203, 2024, doi: 10.1109/JTEHM.2023.3329344.
  4. J. Li et al., “A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson’s Disease Versus Essential Tremor,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3397–3406, 2023, doi: 10.1109/TNSRE.2023.3306203.
  5. A. B. Oktay and A. Kocer, “Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks,” Biomed Signal Process Control, vol. 56, p. 101683, Feb. 2020, doi: 10.1016/j.bspc.2019.101683.
  6. J. P. de Moura, J. V. da F. Neto, and P. H. M. Rego, “A Neuro-Fuzzy Model for Online Optimal Tuning of PID Controllers in Industrial System Applications to the Mining Sector,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 8, pp. 1864–1877, Aug. 2020, doi: 10.1109/TFUZZ.2019.2923963.
  7. S. Singh, V. Singh, A. Rani, and J. Yadav, “Optimization of PID controller based on various tuning methods,” in 2023 International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE, Feb. 2023, pp. 1–6. doi: 10.1109/PIECON56912.2023.10085805.
  8. B. Verma and P. K. Padhy, “Robust Fine Tuning of Optimal PID Controller With Guaranteed Robustness,” IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 4911–4920, Jun. 2020, doi: 10.1109/TIE.2019.2924603.
  9. F. Isdaryani, F. Feriyonika, and R. Ferdiansyah, “Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system,” J Phys Conf Ser, vol. 1450, no. 1, p. 012033, Feb. 2020, doi: 10.1088/1742-6596/1450/1/012033.
  10. N. Onasie and S. Sulaiman, “Perancangan Sendok Makan Parkinson dengan Metode PID berbasis Arduino,” Techné : Jurnal Ilmiah Elektroteknika, vol. 22, no. 1, pp. 33–48, Apr. 2023, doi: 10.31358/techne.v22i1.346.
  11. B. Taşar, A. B. Tatar, A. K. Tanyıldızı, and O. Yakut, “FiMec tremor stabilization spoon: design and active stabilization control of two DoF robotic eating devices for hand tremor patients,” Med Biol Eng Comput, vol. 61, no. 10, pp. 2757–2768, Oct. 2023, doi: 10.1007/s11517-023-02886-z.
  12. A. Surana and B. Bhushan, “Designing of PSO Tuned PID Controller for Ball Balancer Arrangement and Comparative Analysis with Classical PID and Fuzzy Logic Controller,” in 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), IEEE, Jul. 2021, pp. 1–5. doi: 10.1109/CONECCT52877.2021.9622355.
  13. R. A. Khan, S. Yang, S. Fahad, S. U. Khan, and Kalimullah, “A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices,” IEEE Access, vol. 9, pp. 99932–99943, 2021, doi: 10.1109/ACCESS.2021.3095403.
  14. A. Surana and B. Bhushan, “Design and Comparison of PSO, SA and GA tuned PID Controller for Ball Balancer Arrangement,” in 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/ICECCT52121.2021.9616686.
  15. R. I. Alfian, A. Ma’arif, and S. Sunardi, “Noise Reduction in the Accelerometer and Gyroscope Sensor with the Kalman Filter Algorithm,” Journal of Robotics and Control (JRC), vol. 2, no. 3, 2021, doi: 10.18196/jrc.2375.
  16. S. Yuliawan, O. Wahyunggoro, and N. Setiawan, “Kalman Filter to Improve Performance of PID Control Systems on DC Motors,” IJITEE (International Journal of Information Technology and Electrical Engineering), vol. 5, no. 3, p. 96, Sep. 2021, doi: 10.22146/ijitee.64511.
  17. M. H. Setiawan, A. Ma’arif, C. Rekik, A. J. Abougarair, and A. M. Mekonnen, “Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 10, no. 1, p. 30, Feb. 2024, doi: 10.26555/jiteki.v10i1.26591.
  18. J. Khodaparast, “A Review of Dynamic Phasor Estimation by Non-Linear Kalman Filters,” IEEE Access, vol. 10, pp. 11090–11109, 2022, doi: 10.1109/ACCESS.2022.3146732.
  19. J. Liu, T. Li, Z. Zhang, and J. Chen, “NARX Prediction-Based Parameters Online Tuning Method of Intelligent PID System,” IEEE Access, vol. 8, pp. 130922–130936, 2020, doi: 10.1109/ACCESS.2020.3007848.
  20. R. N. Jazar, Theory of Applied Robotics: Kinematics, Dynamics, and Control (2nd Edition). Springer US, 2010. [Online]. Available: https://books.google.co.id/books?id=VfFGAAAAQBAJ
  21. R. J. Rajesh and P. Kavitha, “Camera gimbal stabilization using conventional PID controller and evolutionary algorithms,” in 2015 International Conference on Computer, Communication and Control (IC4), IEEE, Sep. 2015, pp. 1–6. doi: 10.1109/IC4.2015.7375580.
  22. E. S. Rahayu, A. Ma’arif, and A. Çakan, “Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor,” International Journal of Robotics and Control Systems, vol. 2, no. 2, pp. 435–447, Jul. 2022, doi: 10.31763/ijrcs.v2i2.476.
  23. K. Suyanto, K. N. Ramadhani, and S. Mandala, “Deep learning modernisasi machine learning untuk big data,” Informatika, 2019.
  24. D. P. Tripathi, M. Nayak, R. Manoj, and S. Sudheer, “Fast parameter free Smart particle swarm optimization (SPSO),” in 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), IEEE, Jan. 2021, pp. 1–7. doi: 10.1109/ICNTE51185.2021.9487738.
  25. M. Xu, “Control of DC adjustable speed electric vehicle based on PSO-PID algorithm optimization research,” in 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), IEEE, Nov. 2022, pp. 616–621. doi: 10.1109/AUTEEE56487.2022.9994335.
  26. J. J, L. D. V. Anand, and H. D, “Comparative Analysis of PID Controller Tuning Methods for Heat Exchanger Control: A MATLAB Simulation Study,” in 2024 Asia Pacific Conference on Innovation in Technology (APCIT), IEEE, Jul. 2024, pp. 1–6. doi: 10.1109/APCIT62007.2024.10673460.
  27. F. Isdaryani, F. Feriyonika, and R. Ferdiansyah, “Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system,” J Phys Conf Ser, vol. 1450, no. 1, p. 012033, Feb. 2020, doi: 10.1088/1742-6596/1450/1/012033.
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References


P. Mcgurrin, J. Mcnames, T. Wu, M. Hallett, and D. Haubenberger, “Quantifying Tremor in Essential Tremor Using Inertial Sensors—Validation of an Algorithm,” IEEE J Transl Eng Health Med, vol. 9, pp. 1–10, 2021, doi: 10.1109/JTEHM.2020.3032924.

J. Kim, T. Wichmann, O. T. Inan, and S. P. DeWeerth, “Fitts’ Law Based Performance Metrics to Quantify Tremor in Individuals With Essential Tremor,” IEEE J Biomed Health Inform, vol. 26, no. 5, pp. 2169–2179, May 2022, doi: 10.1109/JBHI.2021.3129989.

S. M. Ali et al., “Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor,” IEEE J Transl Eng Health Med, vol. 12, pp. 194–203, 2024, doi: 10.1109/JTEHM.2023.3329344.

J. Li et al., “A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson’s Disease Versus Essential Tremor,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3397–3406, 2023, doi: 10.1109/TNSRE.2023.3306203.

A. B. Oktay and A. Kocer, “Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks,” Biomed Signal Process Control, vol. 56, p. 101683, Feb. 2020, doi: 10.1016/j.bspc.2019.101683.

J. P. de Moura, J. V. da F. Neto, and P. H. M. Rego, “A Neuro-Fuzzy Model for Online Optimal Tuning of PID Controllers in Industrial System Applications to the Mining Sector,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 8, pp. 1864–1877, Aug. 2020, doi: 10.1109/TFUZZ.2019.2923963.

S. Singh, V. Singh, A. Rani, and J. Yadav, “Optimization of PID controller based on various tuning methods,” in 2023 International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE, Feb. 2023, pp. 1–6. doi: 10.1109/PIECON56912.2023.10085805.

B. Verma and P. K. Padhy, “Robust Fine Tuning of Optimal PID Controller With Guaranteed Robustness,” IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 4911–4920, Jun. 2020, doi: 10.1109/TIE.2019.2924603.

F. Isdaryani, F. Feriyonika, and R. Ferdiansyah, “Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system,” J Phys Conf Ser, vol. 1450, no. 1, p. 012033, Feb. 2020, doi: 10.1088/1742-6596/1450/1/012033.

N. Onasie and S. Sulaiman, “Perancangan Sendok Makan Parkinson dengan Metode PID berbasis Arduino,” Techné : Jurnal Ilmiah Elektroteknika, vol. 22, no. 1, pp. 33–48, Apr. 2023, doi: 10.31358/techne.v22i1.346.

B. Taşar, A. B. Tatar, A. K. Tanyıldızı, and O. Yakut, “FiMec tremor stabilization spoon: design and active stabilization control of two DoF robotic eating devices for hand tremor patients,” Med Biol Eng Comput, vol. 61, no. 10, pp. 2757–2768, Oct. 2023, doi: 10.1007/s11517-023-02886-z.

A. Surana and B. Bhushan, “Designing of PSO Tuned PID Controller for Ball Balancer Arrangement and Comparative Analysis with Classical PID and Fuzzy Logic Controller,” in 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), IEEE, Jul. 2021, pp. 1–5. doi: 10.1109/CONECCT52877.2021.9622355.

R. A. Khan, S. Yang, S. Fahad, S. U. Khan, and Kalimullah, “A Modified Particle Swarm Optimization With a Smart Particle for Inverse Problems in Electromagnetic Devices,” IEEE Access, vol. 9, pp. 99932–99943, 2021, doi: 10.1109/ACCESS.2021.3095403.

A. Surana and B. Bhushan, “Design and Comparison of PSO, SA and GA tuned PID Controller for Ball Balancer Arrangement,” in 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/ICECCT52121.2021.9616686.

R. I. Alfian, A. Ma’arif, and S. Sunardi, “Noise Reduction in the Accelerometer and Gyroscope Sensor with the Kalman Filter Algorithm,” Journal of Robotics and Control (JRC), vol. 2, no. 3, 2021, doi: 10.18196/jrc.2375.

S. Yuliawan, O. Wahyunggoro, and N. Setiawan, “Kalman Filter to Improve Performance of PID Control Systems on DC Motors,” IJITEE (International Journal of Information Technology and Electrical Engineering), vol. 5, no. 3, p. 96, Sep. 2021, doi: 10.22146/ijitee.64511.

M. H. Setiawan, A. Ma’arif, C. Rekik, A. J. Abougarair, and A. M. Mekonnen, “Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 10, no. 1, p. 30, Feb. 2024, doi: 10.26555/jiteki.v10i1.26591.

J. Khodaparast, “A Review of Dynamic Phasor Estimation by Non-Linear Kalman Filters,” IEEE Access, vol. 10, pp. 11090–11109, 2022, doi: 10.1109/ACCESS.2022.3146732.

J. Liu, T. Li, Z. Zhang, and J. Chen, “NARX Prediction-Based Parameters Online Tuning Method of Intelligent PID System,” IEEE Access, vol. 8, pp. 130922–130936, 2020, doi: 10.1109/ACCESS.2020.3007848.

R. N. Jazar, Theory of Applied Robotics: Kinematics, Dynamics, and Control (2nd Edition). Springer US, 2010. [Online]. Available: https://books.google.co.id/books?id=VfFGAAAAQBAJ

R. J. Rajesh and P. Kavitha, “Camera gimbal stabilization using conventional PID controller and evolutionary algorithms,” in 2015 International Conference on Computer, Communication and Control (IC4), IEEE, Sep. 2015, pp. 1–6. doi: 10.1109/IC4.2015.7375580.

E. S. Rahayu, A. Ma’arif, and A. Çakan, “Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor,” International Journal of Robotics and Control Systems, vol. 2, no. 2, pp. 435–447, Jul. 2022, doi: 10.31763/ijrcs.v2i2.476.

K. Suyanto, K. N. Ramadhani, and S. Mandala, “Deep learning modernisasi machine learning untuk big data,” Informatika, 2019.

D. P. Tripathi, M. Nayak, R. Manoj, and S. Sudheer, “Fast parameter free Smart particle swarm optimization (SPSO),” in 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), IEEE, Jan. 2021, pp. 1–7. doi: 10.1109/ICNTE51185.2021.9487738.

M. Xu, “Control of DC adjustable speed electric vehicle based on PSO-PID algorithm optimization research,” in 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), IEEE, Nov. 2022, pp. 616–621. doi: 10.1109/AUTEEE56487.2022.9994335.

J. J, L. D. V. Anand, and H. D, “Comparative Analysis of PID Controller Tuning Methods for Heat Exchanger Control: A MATLAB Simulation Study,” in 2024 Asia Pacific Conference on Innovation in Technology (APCIT), IEEE, Jul. 2024, pp. 1–6. doi: 10.1109/APCIT62007.2024.10673460.

F. Isdaryani, F. Feriyonika, and R. Ferdiansyah, “Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system,” J Phys Conf Ser, vol. 1450, no. 1, p. 012033, Feb. 2020, doi: 10.1088/1742-6596/1450/1/012033.

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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