METAHEURISTIC-BASED TUNING OF PROPORTIONAL-DERIVATIVE LEARNING RULES FOR PROPORTIONAL-INTEGRAL FUZZY CONTROLLERS IN TOWER CRANE SYSTEM PAYLOAD POSITION CONTROL

Radu-Emil Precup, Raul-Cristian Roman, Elena-Lorena Hedrea, Emil M. Petriu, Claudia-Adina Bojan-Dragos, Alexandra-Iulia Szedlak-Stinean

DOI Number
10.22190/FUME240914044P
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Abstract


This paper proposes the use of metaheuristic optimization algorithms to tune the Proportional-Derivative (PD) learning rules within the framework of Iterative Learning Control applied to low-cost Takagi-Sugeno Proportional-Integral (PI)-fuzzy controllers for tower crane system payload position control. Four PD learning rules are considered: direct rule with current (in the iteration domain) control error, direct rule with previous control error, indirect rule, and open-closed-loop rule. The fuzzy controllers are tuned by the Extended Symmetrical Optimum method applied to the linear PI controllers, and then by the modal equivalence principle. Set-point filters are included for overshoot reduction. A unified design approach is formulated for all four PD learning rules in terms of optimally computing the gains in the iteration domain using metaheuristic optimization algorithms that solve optimization problems with objective functions expressed as the sum of the squared control error multiplied by time, where the two variables are the parameters of the PD learning rules. Seven popular metaheuristic optimization algorithms are implemented. Real-time experimental results from ten iterations of these optimization algorithms support the performance comparison of the fuzzy control systems.

Keywords

Iterative Learning Control, Metaheuristic algorithms, Proportional-Derivative learning rules, Proportional-Integral-fuzzy controllers, Tower crane systems

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References


Bristow, D.A., Tharayil, M., Alleyne, A.G., 2006, A survey of iterative learning control, IEEE Control Systems Magazine, 26(3), pp. 96-114.

Owens, D.H., Hätönen, J., 2005, Iterative learning control - An optimization paradigm, Annual Reviews in Control, 29(1), pp. 57-70.

Hamidaoui, M., Shao, C., Haouassi, S., 2020, A PD-type iterative learning control algorithm for one-dimension linear wave equation, International Journal of Control, Automation Systems, 18(4), pp. 1045-1052.

Yuan, H., Zhao, X.-M., 2022, Advanced contouring compensation approach via Newton-ILC and adaptive jerk control for biaxial motion system, IEEE Transactions on Industrial Electronics, 69(5), pp. 5081-5090.

Precup, R.-E., Roman, R.-C., Hedrea, E.-L., Petriu, E.M., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., 2024, Supplementary appendix of the paper Metaheuristic-based Tuning of Proportional-Derivative Learning Rules for Proportional-Integral Fuzzy Controllers in Tower Crane System Payload Position Control, [Online]. Available at:

https://www.aut.upt.ro/~rprecup/Supplementary_Appendix_FUME_2024.pdf (last access: 13.09.2024).

Meng, D.-Y., Zhang, J.-Y., 2022, Design and analysis of data-driven learning control: an optimization-based approach, IEEE Transactions on Neural Networks and Learning Systems, 33(10), pp. 5527-5541.

Chi, R.-H., Hui, Y., Hou, Z.-S., 2022, Data-Driven Iterative Learning Control for Discrete-Time Systems, Springer, Singapore.

Li, M., Xiong, J.-X., Cheng, R., Zhu, Y., Yang, K.-M., Sun, F.-M., 2023, Rational feedforward tuning using variance-optimal instrumental variables method based on dual-loop iterative learning control, IEEE Transactions on Industrial Informatics, 19(3), pp. 2585-2595.

Huang, T., Kang, Y.-T., Pi, Y.-J., Li, M., 2024, Combined unfixed-structure and fixed-structure data-driven feedforward control approach for ball screw feed-drive system, IEEE Transactions on Industrial Informatics, 20(2), pp. 2331-2341.

Wu, A.-J., Huo, X., Liu, Q.-Q., Ma, J., 2024, Circularly orthogonal projection-based iterative learning control for rejecting spatially cyclic disturbances, IEEE Transactions on Industrial Electronics, 71(7), pp. 7631-7640.

Wang, C., Chen, W.-H., Zhang, H.-L., Dou, W., Chen, J.-B., 2024, An immune optimization deep reinforcement learning control method used for magnetorheological elastomer vibration absorber, Engineering Applications of Artificial Intelligence, 137, paper 109108.

Wang, Y.-Q., Gao, F.-R., Doyle III, F.J., 2009, Survey on iterative learning control, repetitive control, and run-to-run control, Journal of Process Control, 19(10), pp. 1589–1600.

Precup, R.-E., Preitl, S., Rudas, I.J., Tar, J.K., 2006, On the use of iterative learning control in fuzzy control system structures, Proc. 7th International Symposium on Hungarian Researchers on Computational Intelligence, Budapest, Hungary, pp. 69-82.

Precup, R.-E., Preitl, S., Tar, J.K., Tomescu, M.L., Takács, M., Korondi, P., Baranyi, P., 2008, Fuzzy control system performance enhancement by iterative learning control, IEEE Transactions on Industrial Electronics, 55(9), pp. 3461-3475.

Yu, Q.-X., Hou, Z.-S., 2021, Adaptive fuzzy iterative learning control for high-speed trains with both randomly varying operation lengths and system constraints, IEEE Transactions on Fuzzy Systems, 29(8), pp. 2408-2418.

Liu, J.-N., Sun, W.-C., 2022, A positioning method of permanent magnet synchronous linear motor based on fuzzy iterative learning control, Proc. 2022 China Automation Congress, Xiamen, China, pp. 1-6.

Liang, M.-D., Li, J.-M., 2023, Iterative learning consensus for nonstrict feedback multiagent systems with unknown control direction and saturation input, IEEE Systems Journal, 17(3), pp. 4234-4244.

Chen, J.-X., Xie, J., Li, J.-M., Chen, W.-S., 2024, Human-in-the-loop fuzzy iterative learning control of consensus for unknown mixed-order nonlinear multi-agent systems, IEEE Transactions on Fuzzy Systems, 32(1), pp. 255-265.

Hui, Y., Meng, D.-Y., Chi, R.-H., Cai, K.-Q., 2024, Sampled-data adaptive iterative learning control for uncertain nonlinear systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(8), pp. 4568-5478.

Precup, R.-E., Hedrea, E.-L., Roman, R.-C., Petriu, E.M., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., 2022, GWO-based performance improvement of PD-type iterative learning fuzzy control of tower crane systems, Proc. 2022 IEEE 31st International Symposium on Industrial Electronics, Anchorage, AK, USA, pp. 1041-1046.

Precup, R.-E., Roman, R.-C., Hedrea, E.-L., Bojan-Dragos, C.-A., Damian, M.-M., Nedelcea, M.-L., 2022, Performance improvement of low-cost iterative learning-based fuzzy control systems for tower crane systems, International Journal of Computers Communications & Control, 17(1), paper 4623.

Precup, R.-E., Roman, R.-C., Hedrea, E.-L., Petriu, E.M., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., 2023, Slime mold algorithm-based performance improvement of PD-type indirect iterative learning fuzzy control of tower crane systems, Proc. 57th Annual Conference on Information Sciences and Systems, Baltimore, MD, USA, pp. 1-6.

Yu, S.-J., Wu, J.-H., Yan, X.-W., 2022, A PD-type open-closed-loop iterative learning control and its convergence for discrete systems, Proc. First International Conference on Machine Learning and Cybernetics, Beijing, China, pp. 659-662.

Kennedy, J., Eberhart, R.C., 1995, Particle swarm optimization, Proc. 1995 IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948.

Rashedi, E., Nezamabadi-pour, H., Saryazdi, S., 2009, GSA: a gravitational search algorithm, Information Sciences, 179(13), pp. 2232-2248.

Kaveh, A., Talatahari, S., 2010, A novel heuristic optimization method: charged system search, Acta Mechanica, 213, pp. 267-289.

Mirjalili, S., Lewis, A., 2016, The whale optimization algorithm, Advances in Engineering Software, 95, pp. 51-67.

Abdollahzadeh, B., Gharehchopogh, F.S., Mirjalili, S., 2021, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Computers and Industrial Engineering, 158, paper 107408.

Preitl, S., Precup, R.-E., 1996, On the algorithmic design of a class of control systems based on providing the symmetry of open-loop Bode plots, Scientific Bulletin of UPT, Transactions on Automatic Control and Computer Science, 41(2), pp. 47-55.

Preitl, S., Precup, R.-E., 1999, An extension of tuning relations after symmetrical optimum method for PI and PID controllers, Automatica, 35(10), pp. 1731-1736.

Galichet, S., Foulloy, L., 1995, Fuzzy controllers: Synthesis and equivalences, IEEE Transactions on Fuzzy Systems, 3(2), pp. 140-148.

Precup, R.-E., David, R.-C., 2019, Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems, Butterworth-Heinemann, Elsevier, Oxford, UK.

Precup, R.-E., Radac, M.-B., Tomescu, M.L., Petriu, E.M., Preitl, S., 2013, Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems, Expert Systems with Applications, 40(1), pp. 188-199.

Song, J., Chen, Y., Liu, Y., Zhang, L., 2023, Fixed-time fuzzy adaptive fault-tolerant control for strict-feedback nonlinear systems with input delay, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(11), pp. 6999-7010.

Inteco, 2012, Tower Crane, User’s Manual, Inteco Ltd., Krakow, Poland.

Precup, R.-E., Roman, R.-C., Safaei, A., 2021, Data-Driven Model-Free Controllers, 1st Edition, CRC Press, Taylor & Francis, Boca Raton, FL, USA.

Roman, R.-C., Precup, R.-E., Petriu, E.M., 2021, Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems, European Journal of Control, 58, pp. 373-387.

Vázquez, C., Collado, J., Friedman, L., 2013, Control of a parametrically excited crane: a vector Lyapunov approach, IEEE Transactions on Control Systems Technology, 21(6), pp. 2332-2340.

David, R.-C., Precup, R.-E., Preitl, S., Szedlak-Stinean, A.-I., Roman, R.-C., Petriu, E.M., 2020, Design of low-cost fuzzy controllers with reduced parametric sensitivity based on whale optimization algorithm, Proc. 2020 IEEE International Conference on Fuzzy Systems, Glasgow, UK, pp. 1-6.

Precup, R.-E., Hedrea, E.-L., Roman, R.-C., Petriu, E. M., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., Paulescu, F.-C., 2022, AVOA-based tuning of low-cost fuzzy controllers for tower crane systems, Proc. 2022 IEEE International Conference on Fuzzy Systems, Padova, Italy, pp. 1-8.

Precup, R.-E., David, R.-C., Roman, R.-C., Szedlak-Stinean, A.-I., Petriu, E.M., 2023, Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using slime mould algorithm, International Journal of Systems Science, 54(15), pp. 2941-2956.

Precup, R.-E., Roman, R.-C., Hedrea, E.-L., Petriu, E.M., Bojan-Dragos, C.-A., Szedlak-Stinean, A.-I., 2024, Data obtained by 30 independent runs of all algorithms, [Online]. Available at: http://www.aut.upt.ro/~rprecup/Data_FUME_2.m (last access: 01.06.2024).

Milić, P., Marinković, D., Klinge, S., Ćojbašić, Ž., 2023, Reissner-Mindlin based isogeometric finite element formulation for piezoelectric active laminated shells, Tehnički Vjesnik, 30(2), pp. 416-425.

Nestorović T., Marinković D., Shabadi S., Trajkov M., 2014, User defined finite element for modeling and analysis of active piezoelectric shell structures, Meccanica, 49(8), pp. 1763-1774.

Bejinariu, S.I., Costin, H., Rotaru, F., Niţă, C., Luca, R., Lazăr, C., 2014, Parallel processing and bio-inspired computing for biomedical image registration, Computer Science Journal of Moldova, 22(2), pp. 253-277.

Chen, B., Peng, W.-M., Song, J.-H., 2023, A frequent construction mining scheme based on syntax tree, Romanian Journal of Information Science and Technology, 26(1), pp. 3-20.

Păun, G., Pérez-Jiménez, M.J., Rozenberg, G., 2023, Infinite spike trains in spiking neural P systems, Romanian Journal of Information Science and Technology, 26(3-4), pp. 251-275.

Ćojbašić, Ž.M., Nikolić, V.D., Ćirić, I.T., Ćojbašić, L.R., 2011, Computationally intelligent modeling and control of fluidized bed combustion process, Thermal Science, 15(2), pp. 321-338.

Filip, F.G., 2021, Automation and computers and their contribution to human well-being and resilience, Studies in Informatics and Control, 30(4), pp. 5-18.

Vrcan, Ž., Troha, S., Marković, K., Marinković, D., 2024, Analysis of complex planetary gearboxes, Spectrum of Mechanical Engineering and Operational Research, 1(1), pp. 227-249.

Romero, S.V., Osaba, E., Villar-Rodriguez, E., Oregi, I., Ban, Y., 2023, Hybrid approach for solving real-world bin packing problem instances using quantum annealers, Scientific Reports, 13(1), paper 11777.

Precup, R.-E., Haidegger, T., Preitl, S., Benyo, B., Paul, A.S., Kovacs, L., 2012, Fuzzy control solution for telesurgical applications, Applied and Computational Mathematics, 11(3), pp. 378-397.

Boucetta, S.I., Johanyák, Z.C., Pokorádi, L.K., 2017, Survey on software defined VANETs, Gradus, 4(1), pp. 272-283.

Škrjanc, I., Blažič, S., Angelov, P., 2014, Robust evolving cloud-based PID control adjusted by gradient learning method, Proc. 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, Linz, Austria, pp. 1-6.

Vaščák, J., Hvizdoš, J., Puheim, M., 2016, Agent-based cloud computing systems for traffic management, Proc. 2016 International Conference on Intelligent Networking and Collaborative Systems, Ostrava, Czech Republic, pp. 73-79.

Pozna, C., Precup, R.-E., 2012, Aspects concerning the observation process modelling in the framework of cognition processes, Acta Polytechnica Hungarica, 9(1), pp. 203-223.

Ando, N., Szemes, T., Korondi, P., Hashimoto, H., 2002, Friction compensation for 6DOF Cartesian coordinate haptic interface, Proc. 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, pp. 2893-2898.


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