Showing papers by "Mojtaba Ahmadieh Khanesar published in 2012"

Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation

Abstract: In this paper, the use of extended Kalman filter for the optimization of the parameters of type-2 fuzzy logic systems is proposed. The type-2 fuzzy logic system considered in this study benefits from a novel type-2 fuzzy membership function which has certain values on both ends of the support and the kernel, and uncertain values on other parts of the support. To have a comparison of the extended Kalman filter with other existing methods in the literature, particle swarm optimization and gradient descent-based methods are used. The proposed type-2 fuzzy neuro structure is tested on different noisy input-output data sets, and it is shown that extended Kalman filter has a better performance as compared to the gradient descent-based methods. Although the performance of the proposed method is comparable with the particle swarm optimization method, it is faster and more efficient than the particle swarm optimization method. Moreover, the simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property. Kalman filter is also used to train the parameters of type-2 fuzzy logic system in a feedback error learning scheme. Then, it is used to control a real-time laboratory setup ABS and satisfactory results are obtained.

Control and synchronization of chaotic systems using a novel indirect model reference fuzzy controller

Abstract: This paper presents a robust indirect model reference fuzzy control scheme for control and synchronization of chaotic nonlinear systems subject to uncertainties and external disturbances. The chaotic system with disturbance is modeled as a Takagi–Sugeno fuzzy system. Using a Lyapunov function, stable adaptation laws for the estimation of the parameters of the Takagi–Sugeno fuzzy model are derived as well as what the control signal should be to compensate for the uncertainties. The synchronization of chaotic systems is also considered in the paper. It is shown that by the use of an appropriate reference signal, it is possible to make the reference model follow the master chaotic system. Then, using the proposed model reference fuzzy controller, it is possible to force the slave to act as the reference system. In this way, the chaotic master and the slave systems are synchronized. It is shown that not only can the initial values of the master and the slave be different, but also there can be parametric differences between them. The proposed control scheme is simulated on the control and the synchronization of Duffing oscillators and Genesio–Tesi systems.

Statistical results to show the superiority of type two fuzzy logic systems over type one counterparts under noisy conditions

Abstract: In this paper, some statistical quantities of the distortion caused by noise in the rule base of type-2 fuzzy logic systems are calculated. In many papers, the justification of better performance of type-2 fuzzy logic systems over type-1 counterparts in noisy conditions are studied only for some specific cases. In this paper, a simple type-2 fuzzy logic system with the ellipsoidal membership function is considered. Using this fuzzy system, it is possible to statistically study the effect of noise in the rule base of fuzzy systems. Using this statistical analysis, it is shown that the type-2 fuzzy logic systems with ellipsoidal membership function is less influenced in the presence of high level of noise when compared to its type-1 counterparts.

Training fuzzy neural networks using sliding mode theory with adaptive learning rate

Abstract: This paper proposes an online training method for the parameters of a fuzzy neural network (FNN) using sliding mode systems theory with an adaptive learning rate. The implemented control structure consists of a conventional controller in parallel with a FNN. The former is provided both to guarantee global asymptotic stability in compact space and acts as a sliding surface to guide the states of the system towards zero. The output of the conventional controller is used to update the parameters of the FNN. The output of the FNN gradually replaces the conventional controller. The adaptive learning rate makes it possible to control the system without priori knowledge about the upper bound of the states of the system and their derivatives. An appropriate Lyapunov function approach is used to analyze the stability of the adaptation law of parameters of FNN. Sufficient conditions to guarantee the boundedness of the parameters are derived. The proposed approach is tested on the velocity control of an electro hydraulic servo system in presence of flow nonlinearities and internal friction.