Abstract
In this chapter, the advantages of using this methodology to design the tracking control for nonlinear processes is first summarized, and later on, some issues and critical drawbacks are discussed. As always in discrete time (DT) control, sampling period selection is crucial and should be decided as a trade-off between computational load and control effort also affecting the transient errors. A discussion about the simplicity of the models and the performance of the controlled plant is also included. The model constraints are outlined, the main limitation being the requirements of being affine in the control and minimum phase. The full state feedback can be overcome by using nonlinear state observers. Some simple guidelines to implement the designed controller are provided.
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References
Besançon, G. (2007). Nonlinear observers and applications. In M. Thoma & M. Morari (Eds.), Lecture notes in control and information sciences (Vol. 363). New York: Springer.
Das, T., & Kar, I. N. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. Control Systems Technology, IEEE Transactions on, 14(3), 501–510.
Fernández, M. C., Nadia Pantano, M., Rossomando, F. G., Ortiz, O. A., & Scaglia, G. J. E. (2019). State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system. Brazilian Journal of Chemical Engineering, 36(1), 421–437. https://doi.org/10.1590/0104-6632.20190361s20170379.
Li, T. H. S., Chang, S. J., & Tong, W. (2004). Fuzzy target tracking control of autonomous mobile robots by using infrared sensors. Fuzzy Systems, IEEE Transactions on, 12(4), 491–501.
Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., & Bastos-Filho, T. F. (2008). An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice, 16(11), 1354–1363.
Pantano, M. N., Serrano, M. E., Fernández, M. C., Rossomando, F. G., Ortiz, O. A., & Scaglia, G. J. (2017). Multivariable control for tracking optimal profiles in a nonlinear fed-batch bioprocess integrated with state estimation. Industrial & Engineering Chemistry Research, 56(20), 6043–6056.
Pérez M., & Albertos, P. (2004). Self-oscillating and chaotic behaviour of a PI-controlled CSTR with control valve saturation, Journal of Process Control, 14 5159.
Resende, C. Z., Carelli, R., & Sarcinelli-Filho, M. (2013). A nonlinear trajectory tracking controller for mobile robots with velocity limitation via fuzzy gains. Control Engineering Practice, 21(10), 1302–1309.
Rómoli, S., Amicarelli, A., Ortiz, O. A., Scaglia, G. J. E., & di Sciascio, F. (2016). Nonlinear control of the dissolved oxygen concentration integrated with a biomass estimator for production of Bacillus thuringiensis β-endotoxins. Computers & Chemical Engineering, 93, 13–24.
Rómoli, S., Serrano, M., Rossomando, F., Vega, J., Ortiz, O., & Scaglia, G. (2017). Neural network-based state estimation for a closed-loop control strategy applied to a fed-batch bioreactor. Complexity, 2017, 9391879. https://doi.org/10.1155/2017/9391879.
Sun, S., & Cui, P. (2004). Path tracking and a practical point stabilization of mobile robot. Robotics and Computer-Integrated Manufacturing, 20, 29–34.
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Scaglia, G., Serrano, M.E., Albertos, P. (2020). Linear Algebra-Based Controller Implementation Issues. In: Linear Algebra Based Controllers. Springer, Cham. https://doi.org/10.1007/978-3-030-42818-1_8
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DOI: https://doi.org/10.1007/978-3-030-42818-1_8
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