Adaptive Fuzzy Logic Control for a Class of Unknown Nonlinear Dynamic Systems with Guaranteed Stability

被引:8
作者
Chaoui H. [1 ]
Gualous H. [2 ]
机构
[1] Intelligent Robotic and Energy Systems (IRES), Department of Electronics, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, ON
[2] LUSAC Laboratory, University of Caen Basse-Normandie, Esplanade de la Paix, Caen
关键词
Adaptive control; Fuzzy logic; Lyapunov; Stability analysis;
D O I
10.1007/s40313-017-0342-y
中图分类号
学科分类号
摘要
In this paper, a stability analysis is suggested for adaptive fuzzy logic systems (FLSs) without the requirement of states measurement or estimation. Fuzzy logic is viewed as a powerful tool in providing accurate approximation of systems with uncertainties. The proposed methodology exploits the power of adaptive control theory to find a Lyapunov-based adaptation law for FLSs. As such, both stability and tracking problems are addressed for a class of nonlinear dynamic systems. The proposed method yields reduced complexity with respect to many adaptive FLSs available in the literature. In addition, the use of an observer to estimate immeasurable states is not required as in other methods. First, a stability analysis is presented for adaptive control. Then, results are extended to adaptive FLSs with unknown dynamics. A numeric illustrative example highlights the implementation details and the performance of the suggested scheme. © 2017, Brazilian Society for Automatics--SBA.
引用
收藏
页码:727 / 736
页数:9
相关论文
共 48 条
[1]  
Ali M.H., Murata T., Tamura J., Influence of communication delay on the performance of fuzzy logic-controlled braking resistor against transient stability, IEEE Transactions on Control Systems Technology, 16, 6, pp. 1232-1241, (2008)
[2]  
Aliev R.A., Pedrycz W., Fundamentals of a fuzzy-logic-based generalized theory of stability, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39, 4, pp. 971-988, (2009)
[3]  
Armstrong B., de Wit C.C., Friction modeling and compensation. In W. S. Levine (Ed.), The control handbook. Electrical engineering handbook (Vol. 77, pp. 1369–1382). Boca Raton, FL: CRC Press, (1996)
[4]  
Barkat S., Tlemcani A., Nouri H., Noninteracting adaptive control of PMSM using interval type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems, 19, 5, pp. 925-936, (2011)
[5]  
Biglarbegian M., Melek W.W., Mendel J.M., On the stability of interval type-2 TSK fuzzy logic control systems, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 40, 3, pp. 798-818, (2010)
[6]  
Boulkroune A., M'Saad M., Farza M., Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities, Fuzzy Sets and Systems, 164, 1, pp. 45-65, (2011)
[7]  
Chang Y.H., Chang C.W., Taur J.S., Tao C.W., Fuzzy swing-up and fuzzy sliding-mode balance control for a planetary-gear-type inverted pendulum, IEEE Transactions on Industrial Electronics, 59, 9, pp. 3751-3761, (2009)
[8]  
Chaoui H., Gueaieb W., Type-2 fuzzy logic control of a flexible-joint manipulator, Journal of Intelligent and Robotic Systems, 51, 2, pp. 159-186, (2008)
[9]  
Chaoui H., Gueaieb W., Biglarbegian M., Yagoub M., Computationally efficient adaptive type-2 fuzzy control of flexible-joint manipulators, Robotics, 2, 2, pp. 66-91, (2013)
[10]  
Chaoui H., Sicard P., Adaptive control of permanent magnet synchronous machines with disturbance estimation, Journal of Control Theory and Applications, 10, 3, pp. 337-343, (2012)