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Abstract
In this paper, a computationally efficient robust predictive control method is proposed for continuous-time under-actuated SISO systems in the presence of actuator saturation and state-dependent uncertainties. The proposition of this research is to employ the idea of model prediction together with the Adaptive Fuzzy Sliding-Mode Control (AFSMC) for tuning the sliding surface parameters by predicting the anticipated effects of uncertainties. In the proposed scheme, only after the trigger conditions are met, the coefficients of the sliding surface are updated and the AFSMC is applied. Hence, computational complexity can be controlled by adjusting the switching rule. In the AFSMC, a fuzzy system is used to approximate a nonlinear function, and a robust term to compensate for any possible mismatches. An adaptively tuned gain is also applied to the control signal to prevent instability caused by the actuator saturation. Based on the updating sliding surface, fuzzy singletons, the upper bound of the fuzzy approximation error, and the saturation gain are adaptively tuned. Closed-loop stability is shown to be guaranteed using the multiple Lyapunov functions theorem and the Barbalat’s lemma. Finally, the method is applied for the depth control of an Autonomous Underwater Vehicle (AUV), depicting the excellent performance of the proposed method.
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No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
Additional information
Notes on contributors
Alireza Mousavi
Alireza Mousavi received his B.Sc. and M.Sc. degrees in mechanical engineering from Iran University of Science and Technology, Tehran, Iran, in 2013 and 2015, respectively. He is currently working toward the Ph.D. degree in the School of Mechanical Engineering, Iran University of Science and Technology. His current research interests include adaptive fuzzy sliding control of nonlinear systems and consensus control of multi-agent dynamical systems.
Amir H. D. Markazi
Amir H. D. Markazi received his Ph.D. from Mechanical Engineering Department, McGill University, Montréal, QC, Canada, in 1995. He is currently a Professor at the School of Mechanical Engineering, Iran University of Science and Technology (IUST). Dr Markazi has served as the former Chair of the Iranian Society for Mechatronics, and is currently the vice chancellor for research and technology at IUST. His research interests include digital and hybrid control of dynamic systems, adaptive fuzzy sliding control of nonlinear systems, networked control systems, and Robotics. He has published numerous journal and conference papers, two books in Persian and one book chapter in English.