This paper introduces a novel sliding mode controller design that guarantees robust closed-loop pole placement within a defined vertical strip and efficiently rejects disturbances in uncertain linear systems. The sliding mode controller (SMC) is developed using an optimal approach that employs Linear Quadratic Regulator (LQR) techniques. The system's sliding surface is designed using stable eigenvectors (EV) combined with a scalar sign function (SSF). The controller gain parameter is determined to ensure robust pole clustering. This paper discusses the control design and stability analysis of the new optimal sliding mode controller. The simulation results show that the proposed approach is able to grant robust trajectory following with high precision.

This article presents a new scheme of fuzzy sliding-mode control for a class of Takagi-Sugeno fuzzy models based on a parallel distributed compensator and using a scalar sign function method. First, a fuzzy model is constructed to represent the local dynamic behaviors of the given nonlinear system. A global controller is then constructed by combining all local state feedback controllers and a global supervisory sliding mode controller. The globally asymptotic stability of the closed-loop system is mathematically proved. Finally, the validity of the proposed design strategy is demonstrated through the simulation of an inverted pendulum on a cart.

This paper studies the problem of the parameter identification based on fuzzy c-regression models for nonlinear systems. The novel procedure combines the possibilistic c-means procedure with fuzzy c-regression models (FCRM) in order to reduce the effects of noisy data. In comparison to the existing algorithms in the literature, the proposed method utilizes a generalized objective function that reduces the errors of partitioning data sets contaminated by noise and as a consequence an accurate model is obtained. The results of this study demonstrate the effectiveness of proposed method compared with other extended versions of FCRM algorithm.