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In many real applications, building and updating adaptive neuro-fuzzy inference system (ANFIS) based on noisy measuring data sources need to be performed such that the filtering impulse noise (IN) from the initial datasets (IDSs) and establishing the ANFIS via the filtered IDS are carried out simultaneously. Focused on this purpose, in this paper, a novel recurrent mechanism as well as a solution for filtering IN based on Lyapunov stability theory is proposed to establish an adaptive online IN filter (AOINF). Using the AOINF, kernel fuzzy-C-means clustering method, and the least mean squares method, a cluster data space deriving from the filtered IDS is created to which the ANFIS is then formed. The recurrent mechanism executes filtering IN to build ANFIS and using the ANFIS as an updated-filter to filter IN synchronously until either the ANFIS converges to the desired accuracy or a stop condition is satisfied. Surveys, including identifying dynamic response of a magnetorheological damper via measuring datasets, are performed to evaluate the proposed method. [ABSTRACT FROM PUBLISHER]

It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. [ABSTRACT FROM AUTHOR]

In this paper, the design of an adaptive optimal fuzzy sliding controller (AOFSC) for semi-active magnetorheological damper (MRD) vehicle suspension system subjected to uncertainty and disturbance (UAD) whose time-varying rate may be high but bounded is presented. This is a combination of an adaptive optimal fuzzy sliding mode controller (FSMC op ), a nonlinear disturbance observer (NDO) and an inverse MRD model to create the desired control force, including the compensation part for UAD. An adaptive control law is proposed to establish a fuzzy-gain update law for the AOFSC and a parameter tie for the NDO such that the convergence of system's dynamic response is a Lyapunov asymptotically stable process. Surveys via simulations along with via an experimental apparatus showed that the high capability to exterminate vibration, robust stability, and the economic efficiency are the main advantages of the proposed AOFSC. [ABSTRACT FROM AUTHOR]