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This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy C-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi–Sugeno (T–S) fuzzy model. A lately and important proposed integral inequality is considered and selected according to the method of the free weight matrix, with these comparatively flexible stability criteria which are determined in the numerical form of linear matrix inequalities (LMIs). Under the condition of the premise in which the controller and the control system partake the same rules, the method does not inquire the same number of membership functions and mathematical rules. In addition, the improved control is used for large-scale nonlinear systems, where the stability criterion of the closed T–S fuzzy system is obtained through LMI and rearranged through the membership function for machine learning . The close-loop controller criteria are derived by using the Lyapunov energy functions to guarantee the stability of the system . Eventually, an instance is presented to reveal the efficacy of evolution.

This paper addressed the robust fuzzy C-Means design for a class of clustering algorithm that are robust against both the plant parameter perturbations with nonlinearity and controller gain variations. Based on the description of Takagi–Sugeno (TS) fuzzy model, the stability and control of nonlinear systems are studied. The recently proposed integral inequality is selected based on the free weight matrix, and the minimum conservative stability criterion is given in the form of linear matrix inequality (LMI). Assuming that the controller and the system have the same premise, this method does not require the number and membership function rules. In addition, the improved control is used as the stability criterion of the closed-loop TS fuzzy system obtained from LMI in large-scale nonlinear systems, and is reorganized for machine learning. The novelty of this paper is to develop a simplified and robust controller design for a class of nonlinear perturbed systems. Moreover, the proposed control process was also ensured by the control criterion derived from the energy function for the stability of the nonlinear system. Finally, a simulation is given and demonstrated the feasibility of the practical application motivated by certain concrete-real problem in vibrated structures.

An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment. In the design procedure of the controller, a parallel distributed compensation (PDC) scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis is carried out for a real structure system by using Lyapunov method. The corresponding boundary value problems are then incorporated into scattering and radiation problems. They are analytically solved, based on separation of variables, to obtain series solutions in terms of the harmonic incident wave motion and surge motion. The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width, thickness and mass has been thus drawn with a parametric approach. From which mathematical models are applied for the wave-induced displacement of the surge motion. A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system, but has the robustness against external disturbance.

This paper deals with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems. It is well known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system. Finding system parameters for stabilizing the control system is also an issue need to be concerned. In this paper, we give additional sufficient conditions for the global stabilization of a TLP nonlinear system. In particular, we consider a class of NN based Takagi-Sugeno (TS) fuzzy TLP systems. Using the so-called parallel distributed compensation (PDC) controller, we prove that this class of systems can be globally asymptotically stable. The proper design of system parameters are found by a swarm intelligence algorithm called Evolved Bat Algorithm (EBA). An illustrative example is given to show the applicability of the main result.

Large-area, high-resolution visual monitoring systems are indispensable in surveillance applications. To construct such systems, high-quality image capture and display devices are required. Whereas high-quality displays have rapidly developed, as exemplified by the announcement of the 85-inch 4K ultrahigh-definition TV by Samsung at the 2013 Consumer Electronics Show (CES), high-resolution surveillance cameras have progressed slowly and remain not widely used compared with displays. In this study, we designed an innovative framework, using a dual-camera system comprising a wide-angle fixed camera and a high-resolution pan-tilt-zoom (PTZ) camera to construct a large-area, multilayered, and high-resolution visual monitoring system that features multiresolution monitoring of moving objects. First, we developed a novel calibration approach to estimate the relationship between the two cameras and calibrate the PTZ camera. The PTZ camera was calibrated based on the consistent property of distinct pan-tilt angle at various zooming factors, accelerating the calibration process without affecting accuracy; this calibration process has not been reported previously. After calibrating the dual-camera system, we used the PTZ camera and synthesized a large-area and high-resolution background image. When foreground targets were detected in the images captured by the wide-angle camera, the PTZ camera was controlled to continuously track the user-selected target. Last, we integrated preconstructed high-resolution background and low-resolution foreground images captured using the wide-angle camera and the high-resolution foreground image captured using the PTZ camera to generate a large-area, multilayered, and high-resolution view of the scene.