Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

Phichitphon Chotikunnan, Rawiphon Chotikunnan, Panya Minyong

Abstract


This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

Keywords


Robotic; PID Control; ILC; Dynamic Model.

Full Text:

PDF

References


S. K. Mallempati, G. Satheesh, and S. Peddakotla, “Design of optimal PI controller for torque ripple minimization of SVPWM-DTC of BLDC motor,” International Journal of Power Electronics and Drive Systems, vol. 14, no. 1, p. 283, 2023, doi: https://doi.org/10.11591/ijpeds.v14.i1.

L. Fong, M. Islam, and M. Ahmad, “Optimized PID Controller of DC-DC Buck Converter based on Archimedes Optimization Algorithm,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 658-672, 2023, doi: https://doi.org/10.31763/ijrcs.v3i4.1113.

R. P. Borase, D. K. Maghade, S. Y. Sondkar, and S. N. Pawar, “A review of PID control, tuning methods and applications,” International Journal of Dynamics and Control, vol. 9, no. 2, pp. 818-827, 2021, doi: https://doi.org/10.1007/s40435-020-00665-4.

E. Widya Suseno and A. Ma'arif, “Tuning of PID Controller Parameters with Genetic Algorithm Method on DC Motor,” International Journal of Robotics and Control Systems, vol. 1, no. 1, pp. 41-53, 2021, doi: https://doi.org/10.31763/ijrcs.v1i1.249.

D. Tran, N. Hoang, N. Loc, Q. Truong, and N. Nha, “A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height,” Journal of Robotics and Control (JRC), vol. 4, no. 5, pp. 612–620, 2023, doi: https://doi.org/10.18196/jrc.v4i5.19448.

T. Y. Wu, Y. Z. Jiang, Y. Z. Su, and W. C. Yeh, “Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System, “ Applied Sciences, vol. 10, no. 23, p. 8472, 2020, doi: https://doi.org/10.3390/app10238472.

H. Budiarto, V. Triwidyaningrum, F. Umam, and A. Dafid, “Implementation of Automatic DC Motor Braking PID Control System on (Disc Brakes),” Journal of Robotics and Control (JRC), vol. 4, no. 3, pp. 371-387, 2023, doi: https://doi.org/10.18196/jrc.v4i3.18505.

S. Jain and Y. V. Hote, “Design of FOPID Controller Using BBBC via ZN Tuning Approach: Simulation and Experimental Validation,” IETE Journal of Research, vol. 68, no. 5, pp. 3356-3370, 2022, doi: https://doi.org/10.1080/03772063.2020.1756937.

R. Chotikunnan, K. Roongprasert, P. Chotikunnan, P. Imura, M. Sangworasil, and A. Srisiriwat, “Robotic Arm Design and Control Using MATLAB/Simulink,” International Journal of Membrane Science and Technology, vol. 10, no. 3, pp. 2448-2459, 2023, doi: https://doi.org/10.15379/ijmst.v10i3.1974.

P. Warrier and P. Shah, “Design of an Optimal Fractional Complex Order PID Controller for Buck Converter,” Journal of Robotics and Control (JRC), vol. 4, no. 3, pp. 243-262, 2023, doi: https://doi.org/10.18196/jrc.v4i3.17446.

M. Elouni, H. Hamdi, B. Rabaoui, and N. Braiek, “Adaptive PID Fault-Tolerant Tracking Controller for Takagi-Sugeno Fuzzy Systems with Actuator Faults: Application to Single-Link Flexible Joint Robot,” International Journal of Robotics and Control Systems, vol. 2, no. 3, pp. 523-546, 2022, doi: https://doi.org/10.31763/ijrcs.v2i3.762.

A. Elmogy, Y. Bouteraa, and W. Elawady, “An Adaptive Fuzzy Self-Tuning Inverse Kinematics Approach for Robot Manipulators,” Journal of Control Engineering and Applied Informatics, vol. 22, no. 4, pp. 43-51, 2020, doi: https://doi.org/10.1109/AIC-MITCSA.2016.7759929

M. Kiew-ong-art, P. Chotikunnan, Y. Pititheeraphab, R. Chotikunnan, K. Roongprasert, and M. Sangworasil, “Comparative Performance of Mamdani and Sugeno Fuzzy Logic Control Systems in Governing the Motion of a Robotic Arm,” International Journal of Membrane Science and Technology, vol. 10, no. 2, pp. 3245-3258, 2023, doi: https://doi.org/10.15379/ijmst.v10i2.3100.

A. Shuraiji and S. Shneen, “Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study,” Journal of Robotics and Control (JRC), vol. 3, no. 6, pp. 762-768, 2022, doi: https://doi.org/10.18196/jrc.v3i6.15974.

H. Maghfiroh, M. Ahmad, A. Ramelan, and F. Adriyanto, “Fuzzy-PID in BLDC Motor Speed Control Using MATLAB/Simulink,” Journal of Robotics and Control (JRC), vol. 3, no. 1, pp. 8-13, 2021, doi: https://doi.org/10.18196/jrc.v3i1.10964.

M. F. Masrom, N. M. A Ghani, and M. O. Tokhi, “Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system: A comparative assessment,” Journal of Low Frequency Noise, Vibration and Active Control, vol. 40, no. 1, pp. 367-382, 2021, doi: https://doi.org/10.1177/1461348419873780.

A. A. bin Abdul Razak, A. N. K. bin Nasir, N. M. A. Ghani, S. Mohammad, M. F. M. Jusof, and N. A. M. Rizal, “Hybrid genetic manta ray foraging optimization and its application to interval type 2 fuzzy logic control of an inverted pendulum system,” in IOP Conference Series: Materials Science and Engineering, vol. 917, no. 1, p. 012082, 2020, doi: https://doi.org/10.1088/1757-899X/917/1/012082.

J. Shuping, X. Peng, H. Jie, and Y. Peng, “Dual-Loop Iterative Learning Control of Robot Manipulator for Human-Robot cooperation,” Journal of Physics: Conference Series, vol. 1846, no. 1, 2021, doi: https://doi.org/10.1088/1742-6596/1846/1/012090.

I. Gandarilla, J. Montoya-Cháirez, V. Santibáñez, C. Aguilar-Avelar, and J. Moreno-Valenzuela, “Trajectory tracking control of a self-balancing robot via adaptive neural networks,” Engineering Science and Technology, an International Journal, vol. 35, p. 101259, 2022, doi: https://doi.org/10.1016/j.jestch.2022.101259.

N. Chiem and L. Thang, “Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System,” Journal of Robotics and Control (JRC), vol. 4, no. 2, pp. 141-148, 2023, doi: https://doi.org/10.18196/jrc.v4i2.17537.

O. T. Altinoz and A. E. Yilmaz, “Investigation of the Optimal PID-Like Fuzzy Logic Controller for Ball and Beam System with Improved Quantum Particle Swarm Optimization,” International Journal of Computational Intelligence and Applications, vol. 21, no. 04, p. 2250025, 2022, doi: https://doi.org/10.1142/s1469026822500250.

X. Huang, A. L. Ralescu, H. Gao, and H. Huang, “A survey on the application of fuzzy systems for underactuated systems,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 233, no. 3, pp. 217-244, 2019, doi: https://doi.org/10.1177/0959651818791027.

A. Bounemeur and M. Chemachema, “Adaptive Fuzzy Fault-Tolerant Control for a Class of Nonlinear Systems under Actuator Faults: Application to an Inverted Pendulum,” International Journal of Robotics and Control Systems, vol. 1, no. 2, pp. 102-115, 2021, doi: https://doi.org/10.31763/ijrcs.v1i2.306.

M. Kiew-ong-art et al., “Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 643-657, 2023, doi: https://doi.org/10.31763/ijrcs.v3i4.1154.

A. Baharuddin and M. Basri, “Self-Tuning PID Controller for Quadcopter using Fuzzy Logic,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 728-748, 2023, doi: https://doi.org/10.31763/ijrcs.v3i4.1127.

R. Chotikunnan, P. Chotikunnan, A. Ma'arif, N. Thongpance, Y. Pititheeraphab, and A. Srisiriwat, “Ball and Beam Control: Evaluating Type-1 and Interval Type-2 Fuzzy Techniques with Root Locus Optimization,” International Journal of Robotics and Control Systems, vol. 3, no. 2, pp. 286–303, 2023, doi: https://doi.org/10.31763/ijrcs.v3i2.997.

L. Biagiotti, L. Moriello, and C. Melchiorri, “Improving the accuracy of industrial robots via iterative reference trajectory modification,” IEEE Transactions on Control Systems Technology, vol. 28, no. 3, pp. 831-843, 2019, doi: https://doi.org/10.1109/TCST.2019.2892929.

M. I. Fale, “Dr. Flynxz–A First Aid Mamdani-Sugeno-type fuzzy expert system for differential symptoms-based diagnosis,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 4, pp. 1138-1149, 2022, doi: https://doi.org/10.1016/j.jksuci.2020.04.016.

Y. Xie, X. Tang, W. Meng, B. Ye, B. Song, J. Tao, and S. Q. Xie, “Iterative data-driven fractional model reference control of industrial robot for repetitive precise speed tracking,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 3, pp. 1041-1053, 2019, doi: https://doi.org/10.1109/TMECH.2019.2906643.

G. Sebastian et al., “Input and output constraints in iterative learning control design for robotic manipulators,” Unmanned Systems, vol. 6, no. 3, pp. 197-208, 2018, doi: https://doi.org/10.1142/S2301385018400095.

C. Wang et al., “Nonparametric statistical learning control of robot manipulators for trajectory or contour tracking,” Robotics and Computer-Integrated Manufacturing, vol. 35, pp. 96-103, 2015. , doi: https://doi.org/10.1016/j.rcim.2015.03.002.

H. Ernesto and J. O. Pedro, “Iterative learning control with desired gravity compensation under saturation for a robotic machining manipulator,” Mathematical Problems in Engineering, vol. 2015, 2015, doi: https://doi.org/10.1155/2015/187948.

Q. Zhu, F. Song, J. X. Xu, and Y. Liu, “An internal model based iterative learning control for wafer scanner systems,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 5, pp. 2073-2084, 2019, doi: https://doi.org/10.1109/TMECH.2019.2929565.

D. Wang, Y. Ye, and B. Zhang. Practical iterative learning control with frequency domain design and sampled data implementation. Springer Singapore, 2014, doi: https://doi.org/10.1007/978-981-4585-60-6.

D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control,” IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96-114, 2006, doi: https://doi.org/10.1109/MCS.2006.1636313.

J. A. Butterworth, L. Y. Pao, and D. Y. Abramovitch, “A comparison of ILC architectures for nanopositioners with applications to AFM raster tracking,” in Proceedings of the 2011 American Control Conference, pp. 2266-2271, 2011, doi: https://doi.org/10.1109/ACC.2011.5991164.

M. Tomizuka, “Zero phase error tracking algorithm for digital control,” J. Dyn. Sys., Meas., Control., vol. 109, no. 1, pp. 65-68, 1987.

A. Hock and A. P. Schoellig, “Distributed iterative learning control for multi-agent systems,” Autonomous Robots, vol. 43, no. 8, pp. 1989-2010, 2019, doi: https://doi.org/10.1007/s10514-019-09845-4.

D. Saputra, A. Ma'arif, H. Maghfiroh, P. Chotikunnan, and S. Rahmadhia, “Design and Application of PLC-based Speed Control for DC Motor Using PID with Identification System and MATLAB Tuner,” Int. J. Robotics and Control Systems, vol. 3, no. 2, pp. 233-244, 2023, doi: https://doi.org/10.31763/ijrcs.v3i2.775.

D. A. Bristow, A. Alleyne, and M. Tharayil, “A time-varying q-filter design for iterative learning control,” in 2007 American Control Conference, pp. 5503-5508, 2007, doi: https://doi.org/10.1109/ACC.2007.4282553.

Z. Feng, J. Ling, M. Ming, and X. H. Xiao, “High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner,” Review of Scientific Instruments, vol. 88, no. 8, 2017, doi: https://doi.org/10.1063/1.4998303.

C. Y. Lin, L. Sun, and M. Tomizuka, “Matrix factorization for design of Q-filter in iterative learning control,” in 2015 54th IEEE Conference on Decision and Control (CDC), pp. 6076-6082, 2015, doi: : https://doi.org/10.1109/CDC.2015.7403175.

Z. Kuang, L. Sun, H. Gao, and M. Tomizuka, “Practical Fractional-Order Variable-Gain Supertwisting Control With Application to Wafer Stages of Photolithography Systems,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 214-224, 2021, doi: https://doi.org/10.1109/TMECH.2021.3060731.

M. Volckaert, M. Diehl, and J. Swevers, “Generalization of norm optimal ILC for nonlinear systems with constraints,” Mechanical Systems and Signal Processing, vol. 39, no. 2, pp. 280-296, 2013, doi: https://doi.org/10.1016/j.ymssp.2013.03.009.

M. Zhu, L. Ye, and X. Ma, “Estimation-based quadratic iterative learning control for trajectory tracking of robotic manipulator with uncertain parameters,” IEEE Access, vol. 8, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2977687.

S. V. Johansen, M. R. Jensen, B. Chu, J. D. Bendtsen, J. Mogensen, and E. Rogers, “Broiler FCR optimization using norm optimal terminal iterative learning control,” IEEE Transactions on Control Systems Technology, vol. 29, no. 2, pp. 580-592, 2019, doi: https://doi.org/10.1109/TCST.2019.2954300.

D. Allahverdy, A. Fakharian, and M. B. Menhaj, “Application of PID and Norm Optimal Iterative Learning Control to Swash Mass Helicopter,” in 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA), pp. 1-6, 2021, doi: https://doi.org/10.1109/ICCIA52082.2021.9403592.

V. K. Jonnalagadda and V. K. Elumalai, “Norm Optimal Iterative Learning Control for Non-Repetitive Trajectory Tracking of Servo System,” IEEE Transactions on Control Systems Technology, vol. 14, no. 3, pp. 153-161, 2021, doi: https://doi.org/10.15866/ireaco.v14i3.20692.

B. Panomruttanarug and R. W. Longman, “Converting repetitive control into stable learning control by iterative adjustment of initial state,” Advances in the Astronautical Sciences, vol. 124, pp. 667-686, 2006.

B. Panomruttanarug, “Position control of robotic manipulator using repetitive control based on inverse frequency response design,” International Journal of Control, Automation and Systems, vol. 18, no. 11, pp. 2830-2841, 2020, doi: https://doi.org/10.1007/s12555-019-0518-2.

M. Zheng, C. Wang, L. Sun, and M. Tomizuka, “Design of arbitrary-order robust iterative learning control based on robust control theory,” Mechatronics, vol. 47, pp. 67-76, 2017, doi: https://doi.org/10.1016/j.mechatronics.2017.08.009.

B. Panomruttanarug, R. W. Longman, and M. Q. Phan, “Multiple model robustification of iterative learning and repetitive control laws including design from frequency response data,” Advances in the Astronautical Sciences, vol. 134, no. 1, pp. 2259-2278, 2009.

P. Chotikunnan and B. Panomruttanarug, “Practical design of a time-varying iterative learning control law using fuzzy logic,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2419-2434, 2022, doi: https://doi.org/10.3233/JIFS-213082.

M. Thor and P. Manoonpong, “Error-based learning mechanism for fast online adaptation in robot motor control,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 2042-2051, 2019, doi: https://doi.org/10.1109/TNNLS.2019.2927737.

M. Li, T. Chen, R. Cheng, K. Yang, Y. Zhu, and C. Mao, “Dual-loop iterative learning control with application to an ultraprecision wafer stage,” IEEE Transactions on Industrial Electronics, vol. 69, no. 11, pp. 11590-11599, 2021, doi: https://doi.org/10.1109/TIE.2021.3120481.

J. Shi, “Structure Analysis of General Type-2 Fuzzy Controller and Its Application,” International Journal of Fuzzy System Applications (IJFSA), vol. 12, no. 1, pp. 1–20, 2023, doi: https://doi.org/10.4018/ijfsa.319813.

W. He, T. Meng, S. Zhang, J. K. Liu, G. Li, and C. Sun, “Dual-loop adaptive iterative learning control for a Timoshenko beam with output constraint and input backlash,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 5, pp. 1027-1038, 2017, doi: https://doi.org/10.1109/TSMC.2017.2692529.

Q. Zhu and J. X. Xu, “Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference,” IET Control Theory and Applications, vol. 12, no. 1, pp. 129-139, 2018, doi: https://doi.org/10.1049/iet-cta.2017.0592.

P. Chotikunnan, B. Panomruttanarug, and P. Manoonpong, “Dual design iterative learning controller for robotic manipulator application,” Journal of Control Engineering and Applied Informatics, vol. 24, no. 3, pp. 76-85, 2022.

A. K. Singholi and D. Agarwal, “Review of Expert System and its Application in Robotics,” in Intelligent Communication, Control and Devices: Proceedings of ICICCD 2017, pp. 1253-1265, 2018, doi: https://doi.org/10.1007/978-981-10-5903-2_131.

S. S. Saab, D. Shen, M. Orabi, D. Kors, and R. H. Jaafar, “Iterative learning control: Practical implementation and automation,” IEEE Transactions on Industrial Electronics, vol. 69, no. 2, pp. 1858-1866, 2021, doi: https://doi.org/10.1109/TIE.2021.3063866.

Z. Jing, “Application and Study of Expert PID Intelligent Control,” IOP Conference Series: Materials Science and Engineering, vol. 563, no. 4, p. 042084, 2019, doi: https://doi.org/10.1088/1757-899X/563/4/042084.

A. Anand, E. Mamatha, C.S. Reddy, and M. Prabha, “Design of neural network based expert system for automated lime kiln system,” Journal Européen des Systèmes Automatisés, vol. 52, no. 4, pp. 369-376, 2019, doi: https://doi.org/10.18280/jesa.520406.

M. Tavana and V. Hajipour, “A practical review and taxonomy of fuzzy expert systems: methods and applications,” Benchmarking: An International Journal, vol. 27, no. 1, pp. 81-136, 2020, doi: https://doi.org/ 10.1108/bij-04-2019-0178.

D. Huang et al., “Current-cycle iterative learning control for high-precision position tracking of piezoelectric actuator system via active disturbance rejection control for hysteresis compensation,” IEEE Transactions on Industrial Electronics, vol. 67, no. 10, pp. 8680-8690, 2019, doi: https://doi.org/10.1109/TIE.2019.2946554.

S. Thaker and V. Nagori, “Analysis of fuzzification process in fuzzy expert system,” in Procedia Computer Science, vol. 132, pp. 1308-1316, 2018, https://doi.org/10.1016/j.procs.2018.05.047.

N. Amann, D. H. Owens, and E. Rogers, “Iterative learning control using optimal feedback and feedforward actions,” International Journal of Control, vol. 65, no. 2, pp. 277-293, 1996, doi: https://doi.org/10.1080/00207179608921697.

K. H .D. Tang, S. Z. M. Dawal, and E. U. Olugu, “Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia,” Journal of Loss Prevention in the Process Industries, vol. 56, pp. 32-45, 2018, doi: https://doi.org/10.1016/j.jlp.2018.08.005.

B. Panomruttanarug, R. W. Longman, and M. Q. Phan, “Steady state frequency response design of finite time iterative learning control,” The Journal of the Astronautical Sciences, vol. 67, no. 2, pp. 571-594, 2020, Doi:10. 1007/s40295-019-00198-9.

P. Chotikunnan and R. Chotikunnan, “Dual design PID controller for robotic manipulator application,” Journal of Robotics and Control (JRC), vol. 4, no. 1, pp. 23-34, 2023. doi: 10.18196/jrc.v4i1.16990.

C. Guo, L. Zhong, J. Zhao, G. Gao, and Y. Huang, “First-order and high-order repetitive control for single-phase grid-connected inverter,” Complexity, vol. 2020, 2020. doi: 10. 1155/2020/1094386.

X. Wang and J. Wang, “Iterative learning control for one-sided lipschitz nonlinear singular conformable differential equations,” International Journal of Robust and Nonlinear Control, vol. 30, no. 17, pp. 7791-7805, 2020. doi: 10. 1002/rnc. 5191.

P. Chotikunnan, and Y. Pititheeraphab, “Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application,” Journal of Robotics and Control (JRC), vol. 4, no. 2, pp. 217-226, 2023, doi: https://doi.org/10.18196/jrc.v4i2.17757.

P. Chotikunnan, R. Chotikunnan, A. Nirapai, A. Wongkamhang, P. Imura, and M. Sangworasil, “Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques,” Journal of Robotics and Control (JRC), vol. 4, no. 2, pp. 128-140, 2023, doi: https://doi.org/10.18196/jrc.v4i2.18108.

Y. Chen, W. Jiang, and T. Charalambous, “Machine learning based iterative learning control for non‐repetitive time‐varying systems,” International Journal of Robust and Nonlinear Control, vol. 33, no. 7, pp. 4098-4116, 2023, doi: https://doi.org/10.1002/rnc.6272.

Y. Yu, C. Zhang, W. Cao, X. Huang, X. Zhang, and M. Zhou, “Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties,” Mechanical Systems and Signal Processing, vol. 187, p. 109950, 2023, doi: https://doi.org/10.1016/j.ymssp.2022.109950.

Z. Kou and J. Sun, “Test-based model-free adaptive iterative learning control with strong robustness,” International Journal of Systems Science, vol. 54, no. 6, pp. 1213-1228, 2023, doi: https://doi.org/10.1080/00207721.2023.2169057.

Z. Afkhami, D. J. Hoelzle, and K. Barton, “Robust higher-order spatial iterative learning control for additive manufacturing systems,” IEEE Transactions on Control Systems Technology, vol. 31, no. 4, pp. 1692-1707, 2023, doi: https://doi.org/10.1109/TCST.2023.3243397.

C. M. Verrelli and P. Tomei, “Adaptive learning control for nonlinear systems: A single learning estimation scheme is enough,” Automatica, vol. 149, p. 110833, 2023, doi: https://doi.org/10.1016/j.automatica.2022.110833.

S. Kerrouche, A. Djerioui, S. Zeghlache, A. Houari, A. Saim, H. Rezk, and M. F. Benkhoris, “Integral backstepping-ILC controller for suppressing circulating currents in parallel-connected photovoltaic inverters,” Simulation Modelling Practice and Theory, vol. 123, p. 102706, 2023, doi: https://doi.org/10.1016/j.simpat.2022.102706.




DOI: https://doi.org/10.18196/jrc.v5i1.20890

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Phichitphon Chotikunnan, Rawiphon Chotikunnan, Panya Minyong

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik