Showing papers by "Mojtaba Ahmadieh Khanesar published in 2011"

Analysis of the Noise Reduction Property of Type-2 Fuzzy Logic Systems Using a Novel Type-2 Membership Function

Abstract: In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.

Direct Model Reference Takagi–Sugeno Fuzzy Control of SISO Nonlinear Systems

Abstract: This study presents a novel direct model reference fuzzy controller. It relaxes the special conditions on the reference model that is required by some of the approaches described in the literature, as well as covering a more general class of Takagi-Sugeno (T-S) systems. The stability of the proposed method is proved using a proper Lyapunov function. In addition, the effects of modeling errors on the proposed controller are considered, and a robust modification algorithm to alleviate this problem is introduced and analyzed. The proposed method is then simulated on a flexible joint robot in a feedback linearization form and on Chua's chaotic electrical circuit. Finally, it is implemented and tested on a nonlinear dc motor with nonlinear state-dependent disturbance.

Levenberg marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function

Abstract: A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.

An online training algorithm based on the fusion of sliding mode control theory and fuzzy neural networks with triangular membership functions

Abstract: This paper proposes an online tuning method for the parameters of a fuzzy neural network using variable structure systems theory. The proposed learning algorithm establishes a sliding motion in terms of the fuzzy neuro controller parameters, and it leads the error towards zero. The Lyapunov function approach is used to analyze the convergence of the weights for the case of triangular membership functions. Sufficient conditions to guarantee the convergence of the weights are derived. In the simulation studies, the approach presented has been tested on the velocity control of an electro hydraulic servo system in presence of flow nonlinearities and internal friction.

Model Reference Fuzzy Control of Nonlinear Dynamical Systems Using an Optimal Observer

Abstract: This paper proposes a novel indirect model reference fuzzy control approach for nonlinear systems, expressed in the form of a Takagi Sugeno (TS) fuzzy model based on an optimal observer. In contrast to what is seen in the literature on adaptive observer-based TS fuzzy control systems, the proposed method is capable of tracking a reference signal rather than just regulation. Additionally the proposed algorithm benefits from an adaptation algorithm which estimates the parameters of observer optimally. The stability analysis of the adaptation law and the controller is done using an appropriate Lyapunov function. The proposed method is then simulated on the control of Chua's circuit and it is shown that it is capable of controlling this chaotic system with high performance.

A novel training method based on variable structure systems theory for fuzzy neural networks

Abstract: Uncertainty is an inevitable problem in real-time industrial control systems and, to handle this problem and the additional one of possible variations in the parameters of the system, the use of sliding mode control theory-based approaches is frequently suggested. In this paper, instead of using a conventional sliding mode controller, a sliding mode control theory-based learning algorithm is proposed to train the fuzzy neural networks in a feedback-error-learning structure. The parameters of the fuzzy neural network are tuned by the proposed algorithm not to minimize the error function but to ensure that the error satisfies a stable equation. The parameter update rules of the fuzzy neural network are derived, and the proof of the learning algorithm is verified by using the Lyapunov stability method. The proposed method is tested on a real-time servo system with time-varying and nonlinear load conditions.