Adaptive Fuzzy Sliding Mode Control of Under-actuated Nonlinear Systems
A new extension of the conventional adaptive fuzzy sliding mode control (AFSMC) scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output (MIMO) systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.
KeywordsAdaptive fuzzy sliding mode control (AFSMC) nonlinear systems uncertain systems under-actuated systems remote environmental monitoring units (REMUS)
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- H. G. Zhang, L. L. Cui, X. Zhang, Y. H. Luo. Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2226–2236, 2011. DOI: 10.1109/TNN.2011.2168538.CrossRefGoogle Scholar
- Y. C. Hsu, H. A. Malki. Fuzzy variable structure control for MIMO systems. In Proceedings of IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence, IEEE, Anchorage, AK, USA, pp. 280–285, 1998.Google Scholar
- D. Pelusi. Optimization of a fuzzy logic controller using genetic algorithms. In Proceedings of IEEE International Conference on Intelligent Human-machine Systems and Cybernetics, IEEE, Zhejiang, China, pp. 143–146, 2011.Google Scholar
- A. Ishigame, T. Furukawa, S. Kawamoto, T. Taniguchi. Sliding mode controller design based on fuzzy inference for non-linear systems. In Proceedings of International Conference on Industrial Electronics, Control and Instrumentation, IEEE, Kobe, Japan, pp. 64–70, 1993.Google Scholar
- C. M. Lin, T. Y. Chen, W. Z. Fan, Y. F. Lee. Adaptive fuzzy sliding mode control for a two-link robot. In Proceedings of IEEE International Conference on Robotics and Biomimetics, IEEE, Shatin, China, pp. 581–586, 2005.Google Scholar
- S. Moussaoui, A. Boulkroune. Stable adaptive fuzzy sliding-mode controller for a class of underactuated dynamic systems. Recent Advances in Electrical Engineering and Control Applications, M. Chadli, S. Bououden, I. Zelinka, Eds., Cham: Springer, 2017.Google Scholar
- Y. T. Bai, P. Li. Adaptive fuzzy sliding mode control for electro-hydraulic position servo system. In Proceedings of Chinese Control and Decision Conference, IEEE, Xuzhou, China, pp. 3249–3253, 2010.Google Scholar
- J. F. Wang, C. G. Wang, B. Feng, Y. X. Sun, J. Liu. Robust adaptive fuzzy sliding mode control of PM synchronous servo motor. In Proceedings of Chinese Control and Decision Conference, IEEE, Xuzhou, China, pp. 3419–3422, 2010.Google Scholar
- S. M. Liu, L. Ding. Application of adaptive fuzzy sliding mode controller in PMSM servo system. In Proceedings of International Conference on Computing, Control and Industrial Engineering, IEEE, Wuhan, China, pp. 95–98, 2010.Google Scholar
- D. P. Brutzman. A Virtual World for an Autonomous Underwater Vehicle, Ph. D. Dissertation, Naval Postgraduate School, USA, 1994.Google Scholar
- T. I. Fossen. Guidance and Control of Ocean Vehicles, Chichester, UK: John Wiley and Sons, 1994.Google Scholar