本論文針對非線性機械系統之建模、參數鑑別、狀態估測與強健控制設計進行系統性研究，建立一套可於即時運算環境下運作的整合性理論與實驗框架。非線性機械系統普遍存在模型不確定性、外部擾動及量測雜訊，使傳統控制理論難以同時兼顧穩定性與精確度。因此，本研究以物理建模為基礎，結合濾波與最佳化輔助的參數鑑別方法、基於線性矩陣不等式的強健控制設計，以及具參數更新機制的狀態估測策略，最終形成可進行即時健康監測之智慧控制架構。首先，論文針對典型非線性機械系統建立物理模型，包括風扇冷卻系統與麥克納姆輪移動平台。模型以牛頓-歐拉方程與能量平衡原理推導，考慮摩擦、氣動阻力等非線性效應，提供可解析的動態方程式，作為系統鑑別與控制設計之理論基礎。其次，在參數鑑別部分，針對傳統最小平方法易受量測雜訊影響之問題，提出結合濾波技術與粒子群最佳化演算法之參數估測方法。該方法能於受限的量測資訊與雜訊條件下有效提升估測精度與穩定性，並於麥克納姆輪移動系統實驗平台中驗證其可行性與優越性。在控制設計方面，發展基於線性矩陣不等式最佳化之強健控制器。透過將穩定性與時域性能限制轉化為凸化求解問題，可系統化設計比例-積分型控制增益，以確保閉迴路漸進穩定與外擾抑制能力。模擬與實驗結果驗證該設計能於非線性與不確定環境下保持良好動態響應。為獲得平滑且可靠之回授訊號，在狀態估測部分，建立以無跡卡爾曼濾波器為核心之參數更新估測架構，以補償模型不確定性與非高斯雜訊。應用於冷卻風扇轉速估測後，結果顯示可顯著提升估測精度並降低回授抖動。最後，本研究整合建模、鑑別、估測與控制，建立具健康監測與預測維護功能之智慧控制系統。該系統可即時偵測外擾與異常，實現智慧製造環境中高可靠度之機械系統監測與自我診斷。整體而言，本論文提出之統一架構不僅提升模型與參數準確性、估測可靠度及控制強健性，更為非線性機械系統之自主化與智慧化奠定重要基礎。

This dissertation presents a systematic study on modeling, parameter identification, state estimation, and robust control design for nonlinear mechanical systems, aiming to establish an integrated theoretical and experimental framework capable of real-time health monitoring implementation. Nonlinear mechanical systems typically suffer from model uncertainties, external disturbances, and measurement noise, which limit the accuracy and robustness of traditional control methods. To address these challenges, the research develops a physics-based modeling approach using Newton-Euler dynamics and energy balance principles to capture nonlinear effects such as friction and aerodynamic drag. A hybrid parameter identification method combining filtering and particle swarm optimization is proposed to enhance estimation accuracy under noisy conditions and validated on a Mecanum-wheel mobile platform. For control design, a robust proportional-integral type controller based on linear matrix inequalities is formulated, enabling convex optimization of stability and performance constraints. Simulation and experimental results demonstrate effective disturbance rejection and stable responses under uncertainties. In addition, a state estimation framework employing an unscented Kalman filter with parameter update mechanism is developed to mitigate model mismatch and non-Gaussian noise, achieving precise and smooth feedback in cooling fan speed estimation experiments. Finally, by integrating modeling, identification, estimation, and control, a real-time intelligent control system with health monitoring and predictive capabilities is established, providing a unified architecture for reliable and autonomous operation of nonlinear mechanical systems.

[1] M. C. Tsai and C. C. Peng, "Sliding Mode Observer Based Multi-Layer Metal Plates Core Temperature On-Line Estimation for Semiconductor Intelligence Manufacturing," IEEE Access, vol. 8, pp. 194561-194574, 2020, doi: 10.1109/ACCESS.2020.3032601.
[2] C. C. Peng, M. C. Tsai, and Y. C. Huang, "Modeling and Parameter Identification of a Consumer-used Skillet Heating System," in 2022 IEEE International Conference on Consumer Electronics - Taiwan, 6-8 July 2022 2022, pp. 293-294, doi: 10.1109/ICCE-Taiwan55306.2022.9869201.
[3] M. C. Tsai and C. C. Peng, "Model-Based Particle Swarm Optimization Filtering Algorithm for Mecanum Wheel Car Parameter Identification With Measurement Noise," IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12, 2025, doi: 10.1109/TIM.2025.3604934.
[4] Y.-C. Huang, T.-C. Wu, W.-Y. Chien, M.-C. Tsai, and C.-C. Peng, "Integrated AGC Approach for Balancing the Thickness Dynamic Response and Shape Condition of a Hot Strip Rolling Control System," Actuators, vol. 13, no. 10, p. 415, 2024. [Online]. Available: https://www.mdpi.com/2076-0825/13/10/415.
[5] C.-C. Peng, M.-C. Tsai, and T.-Y. Chen, "Nonlinearity modeling for online estimation of industrial cooling fan speed subject to model uncertainties and state-dependent measurement noise," Nonlinear Engineering, vol. 13, no. 1, 2024, doi: doi:10.1515/nleng-2024-0049.
[6] C. C. Peng and M. C. Tsai, "Analysis of an Electronic Heating Device: System Modeling, Identification and Control," IEEE Transactions on Industrial Electronics, vol. 71, no. 12, pp. 16463-16472, 2024, doi: 10.1109/TIE.2024.3390709.
[7] C.-C. Peng, N.-J. Cheng, and M.-C. Tsai, "Application of an Output Filtering Method for an Unstable Wheel-Driven Pendulum System Parameter Identification," Electronics, vol. 12, no. 22, p. 4569, 2023. [Online]. Available: https://www.mdpi.com/2079-9292/12/22/4569.
[8] H.-X. Li and H. Si, "Control for Intelligent Manufacturing: A Multiscale Challenge," Engineering, vol. 3, no. 5, pp. 608-615, 2017/10/01/ 2017, doi: https://doi.org/10.1016/J.ENG.2017.05.016.
[9] H. Cao, J. Shen, Z. Li, Q. Ren, and S. Zhao, "Prespecified-Performance Kinematic Tracking Control for Aerial Manipulation," IEEE Robotics and Automation Letters, vol. 10, no. 11, pp. 11086-11093, 2025, doi: 10.1109/LRA.2025.3609098.
[10] A. B. Alhassan, R. Chancharoen, B. B. Muhammad, and G. Phanomchoeng, "Precise automation of rotary flexible link manipulator using hybrid input shaping with single state feedback fuzzy logic and sliding mode controllers," IEEE Access, vol. 11, pp. 86711-86726, 2023.
[11] H. K. Khalil and J. W. Grizzle, Nonlinear systems. Prentice hall Upper Saddle River, NJ, 2002.
[12] J.-J. E. Slotine and W. Li, Applied nonlinear control (no. 1). Prentice hall Englewood Cliffs, NJ, 1991.
[13] M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot modeling and control. Wiley New York, 2006.
[14] R. Featherstone, Rigid body dynamics algorithms. Springer, 2008.
[15] C.-C. Peng and Y.-I. Lin, "Dynamics modeling and parameter identification of a cooling fan system," in 2018 IEEE International Conference on Advanced Manufacturing (ICAM), 2018: IEEE, pp. 257-260.
[16] D. H. Norrie and G. De Vries, The finite element method: fundamentals and applications. Academic Press, 2014.
[17] A. A. Shabana, Dynamics of multibody systems. Cambridge university press, 2020.
[18] Y.-H. Chen and C.-C. Peng, "Physical-informed neural network in modeling and control of a servo mechanical rotor system," Results in Engineering, vol. 27, p. 106130, 2025/09/01/ 2025, doi: https://doi.org/10.1016/j.rineng.2025.106130.
[19] F. Djeumou, C. Neary, E. Goubault, S. Putot, and U. Topcu, "Neural networks with physics-informed architectures and constraints for dynamical systems modeling," in Learning for Dynamics and Control Conference, 2022: PMLR, pp. 263-277.
[20] A. Khan and D. A. Lowther, "Physics Informed Neural Networks for Electromagnetic Analysis," IEEE Transactions on Magnetics, vol. 58, no. 9, pp. 1-4, 2022, doi: 10.1109/TMAG.2022.3161814.
[21] J.-N. Juang and R. S. Pappa, "An eigensystem realization algorithm for modal parameter identification and model reduction," Journal of guidance, control, and dynamics, vol. 8, no. 5, pp. 620-627, 1985.
[22] J.-N. Juang, M. Phan, L. G. Horta, and R. W. Longman, "Identification of observer/Kalman filter Markov parameters-Theory and experiments," Journal of Guidance, Control, and Dynamics, vol. 16, no. 2, pp. 320-329, 1993.
[23] A. Simpkins, "System identification: Theory for the user, (ljung, l.; 1999)[on the shelf]," IEEE Robotics & Automation Magazine, vol. 19, no. 2, pp. 95-96, 2012.
[24] S. Chen, S. A. Billings, and W. Luo, "Orthogonal least squares methods and their application to non-linear system identification," International Journal of control, vol. 50, no. 5, pp. 1873-1896, 1989.
[25] T. Chen and L. Ljung, "Implementation of algorithms for tuning parameters in regularized least squares problems in system identification," Automatica, vol. 49, no. 7, pp. 2213-2220, 2013.
[26] I. Markovsky, J. C. Willems, S. Van Huffel, B. De Moor, and R. Pintelon, "Application of structured total least squares for system identification and model reduction," IEEE Transactions on Automatic Control, vol. 50, no. 10, pp. 1490-1500, 2005.
[27] M. Gautier, "Numerical calculation of the base inertial parameters of robots," in Proceedings., IEEE International Conference on Robotics and Automation, 13-18 May 1990 1990, pp. 1020-1025 vol.2, doi: 10.1109/ROBOT.1990.126126.
[28] T. Lee, B. D. Lee, and F. C. Park, "Optimal excitation trajectories for mechanical systems identification," Automatica, vol. 131, p. 109773, 2021/09/01/ 2021, doi: https://doi.org/10.1016/j.automatica.2021.109773.
[29] D. Guého, P. Singla, M. Majji, and R. G. Melton, "Filtered integral formulation of the sparse model identification problem," Journal of Guidance, Control, and Dynamics, vol. 45, no. 2, pp. 232-247, 2022.
[30] Y.-R. Li and C.-C. Peng, "Encoder position feedback based indirect integral method for motor parameter identification subject to asymmetric friction," International Journal of Non-Linear Mechanics, vol. 152, p. 104386, 2023/06/01/ 2023, doi: https://doi.org/10.1016/j.ijnonlinmec.2023.104386.
[31] J. SungEun and K. Sang Woo, "Consistent normalized least mean square filtering with noisy data matrix," IEEE Transactions on Signal Processing, vol. 53, no. 6, pp. 2112-2123, 2005, doi: 10.1109/TSP.2005.847845.
[32] M. Verhaegen and V. Verdult, Filtering and system identification: a least squares approach. Cambridge university press, 2007.
[33] Z. Hendzel and M. Kołodziej, "Parametric Identification of the Mathematical Model of a Mobile Robot with Mecanum Wheels," in Automation 2023: Key Challenges in Automation, Robotics and Measurement Techniques, Cham, R. Szewczyk, C. Zieliński, M. Kaliczyńska, and V. Bučinskas, Eds., 2023// 2023: Springer Nature Switzerland, pp. 107-117.
[34] C.-C. Peng and T.-Y. Chen, "A recursive low-pass filtering method for a commercial cooling fan tray parameter online estimation with measurement noise," Measurement, vol. 205, p. 112193, 2022/12/01/ 2022, doi: https://doi.org/10.1016/j.measurement.2022.112193.
[35] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95 - International Conference on Neural Networks, 27 Nov.-1 Dec. 1995 1995, vol. 4, pp. 1942-1948 vol.4, doi: 10.1109/ICNN.1995.488968.
[36] T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, "Particle Swarm Optimization: A Comprehensive Survey," IEEE Access, vol. 10, pp. 10031-10061, 2022, doi: 10.1109/ACCESS.2022.3142859.
[37] K. Kristinsson and G. A. Dumont, "System identification and control using genetic algorithms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 5, pp. 1033-1046, 2002.
[38] A. Lambora, K. Gupta, and K. Chopra, "Genetic algorithm-A literature review," in 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon), 2019: IEEE, pp. 380-384.
[39] Q. Zhang, Y. Dong, Y. Peng, J. Luo, S. Xie, and H. Pu, "Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm," Journal of Intelligent Material Systems and Structures, vol. 30, no. 8, pp. 1263-1275, 2019.
[40] G. Welch and G. Bishop, "An introduction to the Kalman filter," 1995.
[41] Z. Zhao, H. Chen, G. Chen, C. Kwan, and X. R. Li, "Comparison of several ballistic target tracking filters," in 2006 American Control Conference, 2006: IEEE, p. 6 pp.
[42] R. Huang, S. C. Patwardhan, and L. T. Biegler, "Robust extended kalman filter based nonlinear model predictive control formulation," in Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009: IEEE, pp. 8046-8051.
[43] J. H. Lee and N. L. Ricker, "Extended Kalman filter based nonlinear model predictive control," Industrial & Engineering Chemistry Research, vol. 33, no. 6, pp. 1530-1541, 1994.
[44] M. I. Ribeiro, "Kalman and extended kalman filters: Concept, derivation and properties," Institute for Systems and Robotics, vol. 43, no. 46, pp. 3736-3741, 2004.
[45] R. Kandepu, B. Foss, and L. Imsland, "Applying the unscented Kalman filter for nonlinear state estimation," Journal of process control, vol. 18, no. 7-8, pp. 753-768, 2008.
[46] E. A. Wan and R. Van Der Merwe, "The unscented Kalman filter for nonlinear estimation," in Proceedings of the IEEE 2000 adaptive systems for signal processing, communications, and control symposium (Cat. No. 00EX373), 2000: Ieee, pp. 153-158.
[47] N. M. Kwok, G. Fang, and W. Zhou, "Evolutionary particle filter: re-sampling from the genetic algorithm perspective," in 2005 IEEE/RSJ international conference on intelligent robots and systems, 2005: IEEE, pp. 2935-2940.
[48] A. Wigren, L. Murray, and F. Lindsten, "Improving the particle filter in high dimensions using conjugate artificial process noise," IFAC-PapersOnLine, vol. 51, no. 15, pp. 670-675, 2018.
[49] S. J. Julier and J. K. Uhlmann, "New extension of the Kalman filter to nonlinear systems," in Signal processing, sensor fusion, and target recognition VI, 1997, vol. 3068: Spie, pp. 182-193.
[50] S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
[51] X. Lyu, B. Hu, K. Li, and L. Chang, "An adaptive and robust UKF approach based on Gaussian process regression-aided variational Bayesian," IEEE Sensors Journal, vol. 21, no. 7, pp. 9500-9514, 2021.
[52] F. Zhu and J. Fu, "A novel state-of-health estimation for lithium-ion battery via unscented Kalman filter and improved unscented particle filter," IEEE Sensors Journal, vol. 21, no. 22, pp. 25449-25456, 2021.
[53] F. Deng, H.-L. Yang, and L.-J. Wang, "Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties," International Journal of Control, Automation and Systems, vol. 17, no. 3, pp. 667-678, 2019/03/01 2019, doi: 10.1007/s12555-018-9503-4.
[54] J. O. A. Limaverde Filho, E. L. F. Fortaleza, J. G. Silva, and M. C. M. M. de Campos, "Adaptive Kalman filtering for closed-loop systems based on the observation vector covariance," International Journal of Control, vol. 95, no. 7, pp. 1731-1746, 2022/07/03 2022, doi: 10.1080/00207179.2020.1870158.
[55] R. P. Borase, D. Maghade, S. Sondkar, and S. Pawar, "A review of PID control, tuning methods and applications," International Journal of Dynamics and Control, vol. 9, no. 2, pp. 818-827, 2021.
[56] P. Shah and S. Agashe, "Review of fractional PID controller," Mechatronics, vol. 38, pp. 29-41, 2016.
[57] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in system and control theory. SIAM, 1994.
[58] P. Gahinet, A. Nemirovskii, A. J. Laub, and M. Chilali, "The LMI control toolbox," in Proceedings of 1994 33rd IEEE Conference on Decision and Control, 14-16 Dec. 1994 1994, vol. 3, pp. 2038-2041 vol.3, doi: 10.1109/CDC.1994.411440.
[59] Advances in linear matrix inequality methods in control: advances in design and control. Society for Industrial and Applied Mathematics, 1999.
[60] M. Chilali and P. Gahinet, "H/sub /spl infin// design with pole placement constraints: an LMI approach," IEEE Transactions on Automatic Control, vol. 41, no. 3, pp. 358-367, 1996, doi: 10.1109/9.486637.
[61] S. Weiland and C. Scherer, "Linear Matrix Inequality in Control," 01/01 1994, doi: 10.1201/b10384-28.
[62] H. Gritli, A. Zemouche, and S. Belghith, "On LMI conditions to design robust static output feedback controller for continuous-time linear systems subject to norm-bounded uncertainties," International Journal of Systems Science, vol. 52, no. 1, pp. 12-46, 2021/01/02 2021, doi: 10.1080/00207721.2020.1818145.
[63] E. Assunção, M. C. M. Teixeira, F. A. Faria, N. A. P. D. Silva, and R. Cardim, "Robust state-derivative feedback LMI-based designs for multivariable linear systems," International Journal of Control, vol. 80, no. 8, pp. 1260-1270, 2007/08/01 2007, doi: 10.1080/00207170701283899.
[64] P. Ojaghi, N. Bigdeli, and M. Rahmani, "An LMI approach to robust model predictive control of nonlinear systems with state-dependent uncertainties," Journal of Process Control, vol. 47, pp. 1-10, 2016/11/01/ 2016, doi: https://doi.org/10.1016/j.jprocont.2016.08.012.
[65] E. Ostertag, Mono- and Multivariable Control and Estimation: Linear, Quadratic and LMI Methods. 2011.
[66] M. Armin, P. Roy, S. Sarker, and S. Das, "LMI Based Robust PID Controller Design for Voltage Control of Islanded Microgrid," Asian Journal of Control, vol. 20, 09/01 2017, doi: 10.1002/asjc.1710.
[67] M. Ge, M.-S. Chiu, and Q.-G. Wang, "Robust PID controller design via LMI approach," Journal of Process Control, vol. 12, no. 1, pp. 3-13, 2002/01/01/ 2002, doi: https://doi.org/10.1016/S0959-1524(00)00057-3.
[68] B. Huang, B. Lu, R. Nagamune, and Q. Li, "LMI-based linear parameter varying PID control design and its application to an aircraft control system," Aerospace Systems, vol. 5, no. 2, pp. 309-321, 2022/06/01 2022, doi: 10.1007/s42401-021-00104-y.
[69] H. Najafizadegan, F. Merrikh-Bayat, and A. Jalilvand, "IMC-PID controller design based on loop shaping via LMI approach," Chemical Engineering Research and Design, vol. 124, pp. 170-180, 2017/08/01/ 2017, doi: https://doi.org/10.1016/j.cherd.2017.06.007.
[70] F. Zheng, Q.-G. Wang, and T. H. Lee, "On the design of multivariable PID controllers via LMI approach," Automatica, vol. 38, no. 3, pp. 517-526, 2002/03/01/ 2002, doi: https://doi.org/10.1016/S0005-1098(01)00237-0.
[71] V. I. Utkin, "Sliding Modes in Control and Optimization," in Communications and Control Engineering Series, 1992.
[72] K. D. Young, V. I. Utkin, and U. Ozguner, "A control engineer's guide to sliding mode control," IEEE Transactions on Control Systems Technology, vol. 7, no. 3, pp. 328-342, 1999, doi: 10.1109/87.761053.
[73] V. Utkin and L. Hoon, "Chattering Problem in Sliding Mode Control Systems," in International Workshop on Variable Structure Systems, 2006. VSS'06., 5-7 June 2006 2006, pp. 346-350, doi: 10.1109/VSS.2006.1644542.
[74] Y. Shtessel, C. Edwards, L. Fridman, and A. Levant, Sliding Mode Control and Observation. Springer New York, 2013.
[75] D. Hernández, E. A. Vázquez, and L. Fridman, "Higher-Order Sliding-Mode Control for Linear Time-Varying Systems," in 2018 IEEE Conference on Decision and Control (CDC), 17-19 Dec. 2018 2018, pp. 7083-7088, doi: 10.1109/CDC.2018.8619272.
[76] A. Levant, "Higher-order sliding modes, differentiation and output-feedback control," International Journal of Control, vol. 76, no. 9-10, pp. 924-941, 2003/01/01 2003, doi: 10.1080/0020717031000099029.
[77] W. Perruquetti and J. P. Barbot, Sliding Mode Control in Engineering. Marcel Dekker, Inc., 2002.
[78] S. Mobayen, D. Baleanu, and F. Tchier, "Second-order fast terminal sliding mode control design based on LMI for a class of non-linear uncertain systems and its application to chaotic systems," Journal of Vibration and Control, vol. 23, pp. 2912-2925, 02/16 2016, doi: 10.1177/1077546315623887.
[79] J. M. Andrade and C. Edwards, "LMI-Based Sliding Mode Controller Design for an Uncertain Single-Link Flexible Robot Manipulator," in CONTROLO 2020, Cham, J. A. Gonçalves, M. Braz-César, and J. P. Coelho, Eds., 2021// 2021: Springer International Publishing, pp. 696-706.
[80] P. H. Coutinho, I. Bessa, V. H. Pereira Rodrigues, and T. Roux Oliveira, A Systematic LMI Approach to Design Multivariable Sliding Mode Controllers. 2024.
[81] M. N. Aydin and R. Coban, "LMI-Based Sliding Mode Control Design and Experimental Application to an Electromechanical Plant," in 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), 16-17 April 2019 2019, pp. 69-72, doi: 10.1109/ICEEE2019.2019.00021.
[82] C. C. Peng and Y. T. Su, "Sliding controller design for servo drive systems subject to mechanical and electrical perturbations," in 2016 IEEE International Conference on Industrial Technology (ICIT), 14-17 March 2016 2016, pp. 107-112, doi: 10.1109/ICIT.2016.7474734.
[83] C.-C. Peng, Y.-Z. Li, and C.-L. Chen, "A robust integral type backstepping controller design for control of uncertain nonlinear systems subject to disturbance," International Journal of Innovative Computing, Information and Control, vol. 7, 05/01 2011.
[84] C.-C. Peng, "Nonlinear Integral Type Observer Design for State Estimation and Unknown Input Reconstruction," Applied Sciences, vol. 7, no. 1, p. 67, 2017. [Online]. Available: https://www.mdpi.com/2076-3417/7/1/67.
[85] H. Madani and E. Roccatello, "A comprehensive study on the important faults in heat pump system during the warranty period," International Journal of Refrigeration, vol. 48, 08/01 2014, doi: 10.1016/j.ijrefrig.2014.08.007.
[86] C. Li, M. Afshar, and B. Akin, "Fault Detection in Small Fan Motors Using MCSA," in 2023 IEEE International Electric Machines & Drives Conference (IEMDC), 15-18 May 2023 2023, pp. 1-7, doi: 10.1109/IEMDC55163.2023.10238848.
[87] X. Jin, E. W. M. Ma, L. L. Cheng, and M. Pecht, "Health Monitoring of Cooling Fans Based on Mahalanobis Distance With mRMR Feature Selection," IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 8, pp. 2222-2229, 2012, doi: 10.1109/TIM.2012.2187240.
[88] L. Wang, W. Zuo, Z. X. Yang, J. Zhang, and Z. Cai, "A Method for Fans’ Potential Malfunction Detection of ONAF Transformer Using Top-Oil Temperature Monitoring," IEEE Access, vol. 9, pp. 129881-129889, 2021, doi: 10.1109/ACCESS.2021.3114301.
[89] R. Nematirad, M. Behrang, and A. Pahwa, "Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques," IEEE Access, vol. 12, pp. 26384-26400, 2024, doi: 10.1109/ACCESS.2024.3366807.
[90] Y. Yu, X. Du, J. Zhou, and H. Ren, "Condition Monitoring Method for Heatsink of Traction Inverter Using ANN and OWD," IEEE Transactions on Power Electronics, vol. 38, no. 2, pp. 2552-2564, 2023, doi: 10.1109/TPEL.2022.3217044.
[91] M. Afshar, C. Li, and B. Akin, "Real-Time Current-Based Distributed Bearing Faults Detection in Small Cooling Fan Motors," IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 3188-3199, 2024, doi: 10.1109/TIA.2023.3338595.
[92] H. Kim et al., "Self-powered triboelectric sensor for cooling fan monitoring," Functional Composites and Structures, vol. 4, no. 3, p. 035003, 2022/08/19 2022, doi: 10.1088/2631-6331/ac871b.
[93] J.-J. Bae and N. Kang, "Design Optimization of a Mecanum Wheel to Reduce Vertical Vibrations by the Consideration of Equivalent Stiffness," Shock and Vibration, vol. 2016, pp. 1-8, 01/01 2016, doi: 10.1155/2016/5892784.
[94] M. F. Fazdi and P.-W. Hsueh, "Parameters Identification of a Permanent Magnet DC Motor: A Review," Electronics, vol. 12, no. 12, p. 2559, 2023. [Online]. Available: https://www.mdpi.com/2079-9292/12/12/2559.
[95] J. N. Juang, Applied System Identification. Prentice Hall, 1994.
[96] Y. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 6-9 July 1999 1999, vol. 3, pp. 1945-1950 Vol. 3, doi: 10.1109/CEC.1999.785511.
[97] L. Zhang, H. Yu, and S. Hu, "A New Approach to Improve Particle Swarm Optimization," in Genetic and Evolutionary Computation — GECCO 2003, Berlin, Heidelberg, E. Cantú-Paz et al., Eds., 2003// 2003: Springer Berlin Heidelberg, pp. 134-139.
[98] M. Clerc, The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. 1999, p. 1957 Vol. 3.
[99] R. C. Eberhart and Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization," in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 16-19 July 2000 2000, vol. 1, pp. 84-88 vol.1, doi: 10.1109/CEC.2000.870279.
[100] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 4-9 May 1998 1998, pp. 69-73, doi: 10.1109/ICEC.1998.699146.
[101] E. E. Yaz, "Linear Matrix Inequalities In System And Control Theory," Proceedings of the IEEE, vol. 86, no. 12, pp. 2473-2474, 1998, doi: 10.1109/JPROC.1998.735454.
[102] A. Zemouche and M. Boutayeb, "On LMI conditions to design observers for Lipschitz nonlinear systems," Automatica, vol. 49, no. 2, pp. 585-591, 2013/02/01/ 2013, doi: https://doi.org/10.1016/j.automatica.2012.11.029.
[103] G. Phanomchoeng and R. Rajamani, "Observer design for Lipschitz nonlinear systems using Riccati equations," in Proceedings of the 2010 American Control Conference, 30 June-2 July 2010 2010, pp. 6060-6065, doi: 10.1109/ACC.2010.5531294.
[104] M. Chilali, P. Gahinet, and P. Apkarian, "Robust pole placement in LMI regions," IEEE Transactions on Automatic Control, vol. 44, no. 12, pp. 2257-2270, 1999, doi: 10.1109/9.811208.
[105] Y.-R. Li, C.-C. Peng, and J.-N. Juang, "An integral method for parameter identification of a nonlinear robot subject to quantization error," Nonlinear Dynamics, vol. 111, no. 24, pp. 22419-22441, 2023/12/01 2023, doi: 10.1007/s11071-023-09027-z.
[106] L.-H. Chen and C.-C. Peng, "Extended Backstepping Sliding Controller Design for Chattering Attenuation and Its Application for Servo Motor Control," Applied Sciences, vol. 7, no. 3, p. 220, 2017. [Online]. Available: https://www.mdpi.com/2076-3417/7/3/220.
[107] C. C. Peng, "Low-Cost Embedded Real-Time Handheld Vibration Smart Sensor for Industrial Equipment Onsite Defect Detection," IEEE Open Journal of the Industrial Electronics Society, vol. 2, pp. 469-478, 2021, doi: 10.1109/OJIES.2021.3109906.