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This article aims to propose a more efficient control algorithm for uncertain nonlinear systems. An intelligent self-organizing double function-link fuzzy brain emotional control system is proposed which comprises a self-organizing double function-link fuzzy brain emotional controller (SDFLFBEC) and a compensation controller. The proposed SDFLFBEC consists of four substructures and a fuzzy inference system. The substructures are the prefrontal cortex, the amygdala, a double function-link network (FLN) and a self-organizing structure. The prefrontal cortex and the amygdala networks work as a mathematical form that presumes the judgment and emotion of a brain. Specifically, a new double FLN is designed to support the above networks for updating their weights. Next, a self-organizing structure can automatically add or prune the layers to achieve efficient network structure. In addition, the fuzzy inference rules are presented to explain the inference processes of the amygdala and orbitofrontal networks. From the above factors, the proposed SDFLFBEC can effectively reduce the tracking error and achieve favorable control performance. The parameters of the control system are adjusted online using the derived adaptation laws that are taken from a Lyapunov function so that the stability of the system is ensured. Simulation studies of a biped robot and the experimental results of a magnetic levitation system are employed to validate the effectiveness and superiority of the proposed SDFLFBEC. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach to synthesize a decentralized observer-based controller for a large-scale nonlinear systems in which the interconnection terms are expressed in the nonlinear forms. The large-scale nonlinear system is modeled under the framework of the large-scale polynomial T–S fuzzy systems which can reduce the modeling error and the number of fuzzy rules. It is noted that the interconnection terms are arbitrary nonlinear functions and unnecessary to satisfy the bound constraints, while these constraints are mandatory in the previous studies. Therefore, the proposed method is more relaxed and applicable in practice. A new observer form based on an unknown input method is proposed to simultaneously estimate the unmeasurable states and interconnection terms, which has not been taken into consideration in any previous articles. The information of the unknown states and interconnection is fed back to the controller for stabilizing the system. With the support of the Lyapunov function, the sum-of-square (SOS) technique, the conditions for designing the observer and controller are derived. Finally, the two numerical examples are provided to prove the success and merit of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to synthesize a controller for stabilizing the nonlinear large-scale system which is represented by a large-scale polynomial Takagi–Sugeno (T–S) fuzzy system. The large-scale system consists of a set of the uncertain polynomial T–S fuzzy system with interconnection terms. Modeling the large-scale nonlinear system under the framework of the polynomial form will decrease both the modeling errors and the number of fuzzy rules with respect to the conventional large-scale T–S fuzzy system. In addition, because of the existence of uncertainties, the synthesizing controller for the large-scale polynomial fuzzy system becomes much more challenging and has not been investigated in the previous studies. In this paper, a controller is synthesized to simultaneously eliminate the impact of the uncertainties and stabilize the system. With the aid of Lyapunov theory, sum-of-square technique, and S-procedure, the conditions for controller synthesis are derived in the main theorems. Finally, two examples are illustrated to show the effectiveness and merit of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for synthesizing a decentralized observer is proposed to estimate the unmeasurable states of a discrete-time large-scale nonlinear system. The large-scale nonlinear system in this study is modeled in terms of a set of discrete-time polynomial Takagi-Sugeno (T-S) fuzzy subsystems and interconnection terms. This modeling method will assist to reduce significantly the number of fuzzy rules. The interconnection terms are considered as the unknown inputs; then, the unknown input method is employed to design observer for this system. It should be emphasized that the interconnection parts in this paper are arbitrary and their effects are eliminated completely. On the basis of the Lyapunov methodology and SOS (Sum of Square) technique, the conditions for observer design expressed under the framework of SOS are derived in the main theorems. Finally, an illustrative example is presented to show the effectiveness and merit of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for designing the observer and controller for Takagi–Sugeno (T–S) fuzzy systems with an enlarged class of disturbances. The class of the disturbances in this study is significantly enlarged because they do not need to satisfy the constraints such as bounded, generated from an exogenous system, or finite derivative is equal to zero. First, the state/disturbance observer is proposed to estimate the unmeasurable states and disturbances, simultaneously. Then, the controller incorporating with the observer is synthesized to attenuate the disturbances completely and stabilize the T–S fuzzy systems. On the basis of the Lyapunov function and linear matrix inequality technique, the sufficient conditions for the synthesis of the observer and controller are derived in the main theorems. Finally, the effectiveness of the proposed method is verified via two examples. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach based on the unknown input method to synthesize the observer for polynomial Takagi–Sugeno fuzzy system with uncertainties. In this paper, the upper bounds of uncertainties are not given and the effect of uncertainties is eliminated without designing an extra controller. With the aids of the noncommon Lyapunov theory and Matlab's tools of the sum-of-square, a new observer is synthesized in which the observer form is completely different from the traditional observer forms reported in previous papers. The conditions for the observer synthesis are much relaxed and the complexity of the design process is reduced. Finally, two illustrative examples are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach based on an unknown input method to synthesize the observer for a discrete time uncertain Takagi–Sugeno fuzzy system. The uncertainty bounds of the system can be unknown and the premise variables can be measurable or unmeasurable. Two main theorems are derived based on the noncommon quadratic Lyapunov function and feasible parameters are solved by the linear matrix inequality technique. Finally, a practical example is given to prove the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]