Estimating Parameter Deviation of DC Motor using Sliding-Mode Observer and Least-Square Algorithm

  • Dzuhri Radityo Utomo Universitas Gadjah Mada
  • Muhammad Faris Chalmers University of Technology
Keywords: Sliding-Mode Observer, Parameter Deviation, Least-Square Algorithm, Linear System, DC Motor


Performing system/plant maintenance is very important as an attempt to avoid any failure during system/plant operation. One of the methods that can be adopted to detect any potential failure inside a plant is by estimating the value of the plant’s parameters. When the plant’s parameters deviate too far from their nominal values, the plant will be more likely to fail. In this paper, an estimation method for estimating the deviation in the parameters of a linear system/plant is proposed as an improvement of the previously proposed method. The main component of this parameter deviation estimator system was an observer block which adopted the sliding-mode observer in combination with an adaptive filter block. The adaptive filter block used in this system adopted the least-square algorithm instead of adopting the gradient-descent algorithm as in the previously proposed method. This method was simulated to estimate the deviation in the parameters of DC motor to verify the effectiveness of the proposed metho. The simulation results showed that this method could successfully estimate the deviation of DC motor parameters with a maximum estimation error of less than 4 %. This method could estimate the deviation in DC motor parameters for both constant deviation value and slowly-changing deviation value as time goes by. In addition, estimating the parameter deviation using this method could produce a good level of accuracy even when using a fairly low-frequency input signal. This method is suitable to be adopted in parameter monitoring process of a linear system so that any fault occurring in the system can be detected and isolated before the plant is fatally damaged.


K.M. Sirvio, “Intelligent Systems in Maintenance Planning and Management,” in Intelligent Techniques in Engineering Management. Intelligent Systems Reference Library, C. Kahraman and S.Ç. Onar, Eds., Cham, Switzerland: Springer, 2015, Vol. 87, pp. 221–245.

V.M. Kalra, T. Thakur, and B.S. Pabla, “Condition Based Maintenance Management System for Improvement in Key Performance Indicators of Mining Haul Trucks-A Case Study,” 2018 IEEE Int. Conf. Innov. Res., Develop. (ICIRD), 2018, pp. 1–6.

C. Chuang, L. Ningyun, J. Bin, and X. Yin, “Condition-Based Maintenance Optimization for Continuously Monitored Degrading Systems under Imperfect Maintenance Actions,” J. Syst. Eng., Electron., Vol. 31, No. 4, pp. 841–851, Aug. 2020.

Y. Chen, W. Gong, D. Xu, and R. Kang, “Imperfect Maintenance Policy Considering Positive and Negative Effects for Deteriorating Systems with Variation of Operating Conditions,” IEEE Trans. Automat. Sci., Eng., Vol. 15, No. 2, pp. 872–878, Apr. 2018.

S. Ambani, L. Li, and J. Ni, “Condition-Based Maintenance Decision Making for Multiple Machines Systems”, J. Manuf. Sci. Eng., Vol. 131, No. 3, pp. 031009-1–031009-9, Jun. 2009.

D. Wang, et al., “A Research on the Monte Carlo Simulation Based On-Condition Maintenance Strategy for Wind Turbines,” 2020 Chin. Control, Decis. Conf. (CCDC), 2020, pp. 4016-4020.

B. Yan, Y. Zhou, and L. Liu, “Condition Based Maintenance of the Yaw Motor in a Wind Turbine Using an Indirect Indicator: A Case Study,” 2018 Progn., Syst. Health Manage. Conf. (PHM-Chongqing), 2018, pp. 860–865.

B.H. Dang, “Applications of Supervised-Learning Approaches for Air Conditioning Plants Condition-Based Maintenance,” 2021 IEEE Conf. Technol. Sustain. (SusTech), 2021, pp. 1–7.

S. Heo and J.H. Lee, “Fault Detection and Classification Using Artificial Neural Networks,” IFAC-PapersOnLine, Vol. 51, No. 18, pp. 470–475, 2018.

S. Alaswad and Y. Xiang, “A Review on Condition-Based Maintenance Optimization Models for Stochastically Deteriorating System”, Rel. Eng., Syst. Safety, Vol. 157, pp. 54-63, Jan. 2017.

R.d.P. Monteiro, et al., “A Hybrid Prototype Selection-Based Deep Learning Approach for Anomaly Detection in Industrial Machines,” Expert Syst. Appl., Vol. 204, pp. 1–10, Oct. 2022.

Y. Zhao and C. Smidts, “Reinforcement Learning for Adaptive Maintenance Policy Optimization under Imperfect Knowledge of the System Degradation Model and Partial Observability of System States,” Rel. Eng., Syst. Safety, Vol. 224, pp. 1-13, Aug. 2022.

S. Herdjunanto, A. Susanto, and O. Wahyunggoro, “Robust Residual Generation for Actuator Fault Isolation,” The 5th Int. Conf. Inf. Technol., Elect. Eng. (ICITEE), 2013, pp. 470–475.

N. Meskin and K. Khorasani, “Actuator Fault Detection and Isolation for a Network of Unmanned Vehicles,” IEEE Trans. Automat. Control, Vol. 54, No. 4, pp. 835–840, Apr. 2009.

S. Herdjunanto, “Pembangkitan Decoupled Residual untuk Isolasi Kesalahan Aktuator Pesawat Terbang Bergerak Lateral,” J. Nas. Tek. Elekt., Teknol. Inf. (JNTETI), Vol. 5, No. 3, pp. 239–243, Aug. 2016.

A. Herdjunanto, A. Cahyadi, dan B.R. Dewangga, “Actuator Fault Decoupled Residual Generation on Lateral Moving Aircraft,” Telkomnika, Vol. 16, No. 4, pp. 1886–1893, Aug. 2018.

S. Herdjunanto, A. Susanto, and O. Wahyunggoro, “Robust Residual Generation for Sensor Fault Isolation in Systems with Structured Uncertainty: A Case Study: MIMO Web Winding System,” The 6th Int. Conf. Inf. Technol., Elect. Eng. (ICITEE), 2014, pp. 405–410.

V. Reppa, M.M. Polycarpou, and C.G. Panayiotou, “Adaptive Approximation for Multiple Sensor Fault Detection and Isolation of Nonlinear Uncertain Systems,” IEEE Trans. Neural Netw. Learn. Syst., Vol. 25, No. 1, pp. 137–153, Jan. 2014.

X. Zhang, “Sensor Bias Fault Detection and Isolation in a Class of Nonlinear Uncertain Systems Using Adaptive Estimation,” IEEE Trans. Autom. Control, Vol. 56, No. 5, pp. 1220–1226, May 2011.

L. Chen, C. Edwards, and H. Alwi, “Sensor Fault Estimation Using LPV Sliding Mode Observers with Erroneous Scheduling Parameters,” Automatica, Vol. 101, pp. 66–77, Mar. 2019.

D.R. Utomo, S. Herdjunanto, and P. Nugroho, “Estimation of Parameter Deviation in Model Based Electronic Circuits using Sliding Mode Observer,” Undergraduate thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2013.

D.G. Luenberger, “Observing the State of a Linear System,” IEEE Trans. Mil. Electron., Vol. 8, No. 2, pp. 74–80, Apr. 1964.

S. Herdjunanto, “Unknown Input Observer untuk Robust Detection Sinyal Kesalahan terhadap Disturbance Menggunakan LMI,” J. Nas. Tek. Elek., Teknol. Inf. (JNTETI), Vol. 7, No. 2, pp. 197–203, May 2018.

“LSK D.C. Motors2 to 750 kW” Leroy-Somer, Charente, France.

How to Cite
Dzuhri Radityo Utomo, & Muhammad Faris. (2022). Estimating Parameter Deviation of DC Motor using Sliding-Mode Observer and Least-Square Algorithm. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 11(4), 281-288.