Active disturbance rejection control: a guide for design and application

[EN] This tutorial addresses the design of controllers by active disturbance rejection control (ADRC). First, the main blocks in the ADRC loop are described. Next, the formulation of the control problem under the disturbance rejection framework is discussed, as well as the tuning of the gains set which are part of the main loop and a guide on designing of the active disturbance rejection controller is presented. This tutorial aims to offer an introduction to readers about the ADRC and a review of the most significant publications that have contributed to development and advance in the research related to the area. To illustrate the design procedure, two examples are included: thermal control and the multivariable control of a chemical process.[ES] Este tutorial aborda el diseño de controladores lineales por rechazo activo de perturbaciones (ADRC). Se inicia con la descripción de los bloques que componen el lazo ADRC. Seguidamente, se discute la formulación del problema de control en el marco del rechazo de perturbaciones, la sintonización del conjunto de ganancias que hacen parte del lazo y se presenta una guía general para el diseño del controlador lineal por rechazo activo de perturbaciones. Con este tutorial se pretende ofrecer una introducción a los lectores sobre el ADRC y una reseña de los trabajos que indican las tendencias de investigación en el área. Para ilustrar el procedimiento de diseño, se incluyen dos ejemplos: el control de un proceso térmico y el control multivariable de un proceso químico.Martínez, B.; Sanchis, J.; García-Nieto, S.; Martínez, M. (2021). Control por rechazo activo de perturbaciones: guía de diseño y aplicación. Revista Iberoamericana de Automática e Informática industrial. 18(3):201-217. https://doi.org/10.4995/riai.2020.14058OJS201217183Ahi, B., Haeri, M., 2018. Linear active disturbance rejection control from the practical aspects. IEEE/ASME Transactions on Mechatronics 23 (6), 2909-2919. https://doi.org/10.1109/tmech.2018.2871880Ahmad, S., Ali, A., 2019. Active disturbance rejection control of DC-DC boost converter: a review with modifications for improved performance. IET Power Electronics 12 (8), 2095-2107. https://doi.org/10.1049/iet-pel.2018.5767Albertos, P., Garcia, P., Gao, Z., Liu, T., 2014. Disturbance rejection in process control. In: Proceeding of the 11th World Congress on Intelligent Control and Automation. IEEE. https://doi.org/10.1109/wcica.2014.7053408Baquero-Suarez, M., Cortes-Romero, J., Arcos-Legarda, J., Coral-Enriquez, H., 2018. Estabilización automática de una bicicleta sin conductor mediante el enfoque de control por rechazo activo de perturbaciones. Revista Iberoamericana de Automática e Informática industrial 15 (1), 86-100. https://doi.org/10.4995/riai.2017.8832Castillo, A., García, P., Sanz, R., Albertos, P., 2018. Enhanced extended state observer-based control for systems with mismatched uncertainties and disturbances. ISA Transactions 73, 1-10. https://doi.org/10.1016/j.isatra.2017.12.005Chen, W.-H., Yang, J., Guo, L., Li, S., 2016. Disturbance-observer-based control and related methods-an overview. IEEE Transactions on Industrial Electronics 63 (2), 1083-1095. https://doi.org/10.1109/tie.2015.2478397Cheng, Y., Chen, Z., Sun, M., Sun, Q., Aug. 2019. Active disturbance rejection generalized predictive control for a high purity distillation column process with time delay. The Canadian Journal of Chemical Engineering 97 (11), 2941-2951. https://doi.org/10.1002/cjce.23513Chu, Z.,Wu, C., Sepehri, N., 2019. Active disturbance rejection control applied to high-order systems with parametric uncertainties. International Journal of Control, Automation and Systems 17 (6), 1483-1493. https://doi.org/10.1007/s12555-018-0509-8Feng, H., Guo, B.-Z., 2017. Active disturbance rejection control: Old and new results. Annual Reviews in Control 44, 238-248. https://doi.org/10.1016/j.arcontrol.2017.05.003Fu, C., Tan, W., 2016. Tuning of linear ADRC with known plant information. ISA Transactions 65, 384-393. https://doi.org/10.1016/j.isatra.2016.06.016Gao, Z., 2003. Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the 2003 American Control Conference, 2003. IEEE. https://doi.org/10.1109/acc.2003.1242516Gao, Z., 2014. On the centrality of disturbance rejection in automatic control. ISA Transactions 53 (4), 850-857. https://doi.org/10.1016/j.isatra.2013.09.012Guerrero-Ramírez, E. O., Martínez-Barbosa, A., Ramírez, E.-G., Linares-Flores, J., Sira-Ramírez, H., 2018. Control del convertidor CD/CD reductor-paralelo implementado en FPGA. Revista Iberoamericana de Automática e Informática industrial 15 (3), 309-316. https://doi.org/10.4995/riai.2018.8925Guo, B.-Z., Zhao, Z.-L., 2016. Active Disturbance Rejection Control for Nonlinear Systems. John Wiley & Sons Singapore Pte. Ltd. https://doi.org/10.1002/9781119239932Han, J., 2009. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics 56 (3), 900-906. https://doi.org/10.1109/tie.2008.2011621He, T., Wu, Z., Li, D., Wang, J., 2020. A tuning method of active disturbance rejection control for a class of high-order processes. IEEE Transactions on Industrial Electronics 67 (4), 3191-3201. https://doi.org/10.1109/tie.2019.2908592Herbst, G., 2013. A simulative study on active disturbance rejection control (ADRC) as a control tool for practitioners. Electronics 2 (4), 246-279. https://doi.org/10.3390/electronics2030246Herbst, G., 2016. Practical active disturbance rejection control: Bumpless transfer, rate limitation, and incremental algorithm. IEEE Transactions on Industrial Electronics 63 (3), 1754-1762. https://doi.org/10.1109/tie.2015.2499168Huang, C., Du, B., 2016. Dierentially flatness active disturbance rejection control approach via algebraic parameter identification to double tank problem. In: 2016 35th Chinese Control Conference (CCC). IEEE. https://doi.org/10.1109/chicc.2016.7553678Huang, Y., Xue, W., 2014. Active disturbance rejection control: Methodology and theoretical analysis. ISA Transactions 53 (4), 963-976. https://doi.org/10.1016/j.isatra.2014.03.003Huilcapi, V., Herrero, J. M., Blasco, X., Martínez-Iranzo, M., 2017. Non-linear identification of a peltier cell model using evolutionary multi-objective optimization. IFAC-PapersOnLine 50 (1), 4448-4453. https://doi.org/10.1016/j.ifacol.2017.08.372Inoue, S., Ishida, Y., 2016. Design of a model-following controller using a decoupling active disturbance rejection control method. Journal of Electrical & Electronic Systems 05 (01). https://doi.org/10.4172/2332-0796.1000174Li, D., Chen, X., Zhang, J., Jin, Q., 2020. On parameter stability region of LADRC for time-delay analysis with a coupled tank application. Processes 8 (2), 223. https://doi.org/10.3390/pr8020223Li, J., Qi, X. H., Wan, H., Xia, Y. Q., 2017a. Active disturbance rejection control: theoretical results summary and future researches. Kongzhi Lilun Yu Yingyong/Control Theory and Applications 34, 281-295. https://doi.org/10.7641/CTA.2017.60363Li, J., Xia, Y., Qi, X., Gao, Z., 2017b. On the necessity, scheme, and basis of the linear-nonlinear switching in active disturbance rejection control. IEEE Transactions on Industrial Electronics 64 (2), 1425-1435. https://doi.org/10.1109/tie.2016.2611573Li, S., Yang, J., Chen,W.-H., Chen, X., 2012. Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Transactions on Industrial Electronics 59 (12), 4792-4802. https://doi.org/10.1109/tie.2011.2182011Liang, Q., Wang, C. B., Pan, J. W., Wei, Y. H., Wang, Y., 2015. Parameter identification of b0 and parameter tuning law in linear active disturbance rejection control. Kongzhi yu Juece/Control and Decision 30, 1691-1695. https://doi.org/10.13195/j.kzyjc.2014.0943Luyben, W. L., 1990. Process Modeling, Simulation, and Control for Chemical Engineers. McGraw-Hill.Madonski, R., Gao, Z., Lakomy, K., 2015. Towards a turnkey solution of industrial control under the active disturbance rejection paradigm. In: 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE. https://doi.org/10.1109/sice.2015.7285478Madonski, R., Piosik, A., Herman, P., 2013. High-gain disturbance observer tuning seen as a multicriteria optimization problem. In: 21st Mediterranean Conference on Control and Automation. IEEE. https://doi.org/10.1109/med.2013.6608905Madonski, R., Shao, S., Zhang, H., Gao, Z., Yang, J., Li, S., 2019. General error-based active disturbance rejection control for swift industrial implementations. Control Engineering Practice 84, 218-229. https://doi.org/10.1016/j.conengprac.2018.11.021Marlin, T., 2000. Process Control: Designing Processes and Control Systems for Dynamic Performance. McGraw-Hill.Martínez, B. V., Jul 2020. Active Disturbance Rejection Control-implementation examples. Version 1.0.0. url: https://www.mathworks.com/matlabcentral/fileexchange/78459.Maxim, A., Copot, D., Copot, C., Ionescu, C. M., 2019. The 5w's for control as part of industry 4.0: Why, what, where, who, and when-a PID and MPC control perspective. Inventions 4 (1), 10. https://doi.org/10.3390/inventions4010010Nowicki, M., Madonski, R., Kozlowski, K., 2015. First look at conditions on applicability of ADRC. In: 2015 10th International Workshop on Robot Motion and Control (RoMoCo). IEEE. https://doi.org/10.1109/romoco.2015.7219750Parvathy, R., Daniel, A. E., 2013. A survey on active disturbance rejection control. In: 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s). IEEE. https://doi.org/10.1109/imac4s.2013.6526432Pérez-Polo, M., Albertos, P., 2007. Nonisothermal stirred-tank reactor with irreversible exothermic reaction a ! b: 2. nonlinear phenomena. In: Selected Topics in Dynamics and Control of Chemical and Biological Processes. Springer Berlin Heidelberg, pp. 243-279. https://doi.org/10.1007/978-3-540-73187_8Reynoso, G., Blasco, X., Sanchis, J., Herrero, J. M., 2017. Controller Tuning with Evolutionary Multiobjective Optimization. Springer International Publishing. https://doi.org/10.1007/978-3-319-41301-3Sanz, R., Garcia, P., Albertos, P., 2015. Active disturbance rejection by state feedback: Experimental validation in a 3-dof quadrotor platform. In: 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). pp. 794-799. https://doi.org/10.1109/SICE.2015.7285349Sira-Ramírez, H., 2018. From flatness, GPI observers, GPI control and flat filters to observer-based ADRC. Control Theory and Technology 16 (4), 249-260. https://doi.org/10.1007/s11768-018-8134-xSun, L., Li, D., Gao, Z., Yang, Z., Zhao, S., 2016. Combined feedforward and model-assisted active disturbance rejection control for non-minimum phase system. ISA Transactions 64, 24-33. https://doi.org/10.1016/j.isatra.2016.04.020Sun, L., Zhang, Y., Li, D., Lee, K. Y., 2019. Tuning of active disturbance rejection control with application to power plant furnace regulation. Control Engineering Practice 92, 104122. https://doi.org/10.1016/j.conengprac.2019.104122Tan,W., Fu, C., 2016. Linear active disturbance-rejection control: Analysis and tuning via imc. IEEE Transactions on Industrial Electronics 63 (4), 2350-2359.Teppa-Garran, P., Garcia, G., 2014. ADRC tuning employing the LQR approach for decoupling uncertain MIMO systems. Information Technology And Control 43 (2). https://doi.org/10.5755/j01.itc.43.2.4059Wu, X., Chen, Z., Zhao, Y., Sun, L., Sun, M., 2018. A comprehensive decoupling control strategy for a gas flow facility based on active disturbance rejection generalized predictive control. The Canadian Journal of Chemical Engineering 97 (3), 762-776. https://doi.org/10.1002/cjce.23215Xue,W., Huang, Y., 2015. Performance analysis of active disturbance rejection tracking control for a class of uncertain LTI systems. ISA Transactions 58, 133-154. https://doi.org/10.1016/j.isatra.2015.05.001Xue, W., Huang, Y., Gao, Z., 2016. On ADRC for non-minimum phase systems: canonical form selection and stability conditions. Control Theory and Technology 14 (3), 199-208. https://doi.org/10.1007/s11768-016-6041-6Zhang, B., Tan, W., Li, J., 2019. Tuning of linear active disturbance rejection controller with robustness specification. ISA Transactions 85, 237-246. https://doi.org/10.1016/j.isatra.2018.10.018Zhao, C., Li, D., 2014. Control design for the SISO system with the unknown order and the unknown relative degree. ISA Transactions 53 (4), 858-872. https://doi.org/10.1016/j.isatra.2013.10.001Zhao, C., Li, D., Cui, J., Tian, L., 2018. Decentralized low-order ADRC design for MIMO system with unknown order and relative degree. Personal and Ubiquitous Computing 22 (5-6), 987-1004. https://doi.org/10.1007/s00779-018-1158-xZhao, S., Gao, Z., 2010. Active disturbance rejection control for non-minimum phase systems. In: Proceedings of the 29th Chinese Control Conference. pp. 6066-6070.Zhao, S., Gao, Z., 2014. Modified active disturbance rejection control for time delay systems. ISA Transactions 53 (4), 882-888. https://doi.org/10.1016/j.isatra.2013.09.013Zhao, S., Xue, W., Gao, Z., 2013. Achieving minimum settling time subject to undershoot constraint in systems with one or two real right half plane zeros. Journal of Dynamic Systems, Measurement, and Control 135 (3). https://doi.org/10.1115/1.4023211Zheng, Q., Chen, Z., Gao, Z., 2009. A practical approach to disturbance decoupling control. Control Engineering Practice 17 (9), 1016-1025. https://doi.org/10.1016/j.conengprac.2009.03.005Zheng, Q., Gao, L. Q., Gao, Z., 2012. On validation of extended state observer through analysis and experimentation. Journal of Dynamic Systems, Measurement, and Control 134 (2). https://doi.org/10.1115/1.4005364Zheng, Q., Gao, Z., 2010. On practical applications of active disturbance rejection control. In: Proceedings of the 29th Chinese Control Conference. pp. 6095-6100.Zheng, Q., Gao, Z., 2016. Active disturbance rejection control: between the formulation in time and the understanding in frequency. Control Theory and echnology 14 (3), 250-259. https://doi.org/10.1007/s11768-016-6059-9Zheng, Q., Gao, Z., 2018. Active disturbance rejection control: some recent experimental and industrial case studies. Control Theory and Technology 16 (4), 301-313. https://doi.org/10.1007/s11768-018-8142-xZheng, Q., Gaol, L. Q., Gao, Z., 2007. On stability analysis of active disturbance rejection control for nonlinear time-varying plants with unknown dynamics. In: 2007 46th IEEE Conference on Decision and Control. IEEE. https://doi.org/10.1109/cdc.2007.4434676Zhou, R., Tan,W., 2019. Analysis and tuning of general linear active disturbance rejection controllers. IEEE Transactions on Industrial Electronics 66 (7), 5497-5507. https://doi.org/10.1109/tie.2018.286934

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