Session Program

Abstract - This paper develops a fuzzy adaptive control system consisting of a new type of fuzzy neural network and a robust controller for uncertain nonlinear systems. The new designed neural network contains the key mechanisms of a typical fuzzy CMAC network and a brain emotional learning controller network. First, the input values of the new network are delivered to a receptive field structure that is inspired from the fuzzy CMAC. Then, the values are divided into a sensory and an emotional channels; and the two channels interact with each other to generate the final outputs of the proposed network. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. In the experimentation, a ``Duffing-Holmes'' chaotic system and a simulated mobile robot are applied to verify the effectiveness and feasibility of the proposed control system. By comparing with the performances of other neural network based control systems, we believe our proposed network is capable of producing better control performances of complex uncertain nonlinear systems control.
Abstract - While the T-S fuzzy model is widely used as a model to design a controller for a system to be controlled, there is a gap, which is referred to as uncertainty, between the T-S fuzzy model and the system. The uncertainty is considered is this paper in an effort to improve the control system performance. Though the basic idea to tackle the uncertainty is to employ a frequently used fuzzy approximator approach, the relevant parameters are tuned by novel adaptive laws with the help of the Nussbaum-type function. Consequently, the closed-loop control system is able to be asymptotically stable.
Abstract - In this work we present a sufficient condition to guarantee the existence of stable models of residuated logic programs with constrain. Such condition is based on another result already published in the literature that guarantees the existence of stable models independently of the syntax. Specifically, it states that if the set of truth values is the unit interval [0,1] and the connectives continuous, then every logic program without constrains has stable models.
Abstract - This paper is considered with the stability problem of Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delay. The delay is assumed to be differential with interval bounds, in which the delay derivative is also bounded in an interval. By utilizing some delay-dependent integral inequalities, a stability criterion is derived by employing the convex optimization method, which is addressed in terms of LMI. The result has advantage over existing ones since the upper bound of the derivative in quadratic terms are fully estimated and can work when upper bound of delay derivative is greater than one. Finally, an example is given to show the effectiveness of the proposed approach.
Abstract - The paper deals with the new specific class of continuous-time T-S fuzzy systems, subject to conditions of positiveness and superstability simultaneously. The peculiarities of the behavior of the fuzzy system with and without bounded external disturbances are studied. Two mutually complementary approaches to the solution of the problem of positive superstabilization of T-S fuzzy systems, reduced to the solution of the LP problems are revealed. The paper contains examples, demonstrating the ways of studying the conditions of existence and finding the fuzzy regulator, ensuring required behavior of the closed-loop T-S fuzzy system.