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Studied in this paper is the control problem of hyperchaotic systems. By combining Takagi–Sugeno (T–S) fuzzy model with parallel distributed compensation design technique, we propose a delay-dependent control criterion via pure delayed state feedback. Because the result is expressed in terms of linear matrix inequalities (LMIs), it is quite convenient to check in practice. Based on this criterion, a procedure is provided for designing fuzzy controller for such systems. This method is a universal one for controlling continuous hyperchaotic systems. As illustrated by its application to hyperchaotic Chen's system, the controller design is quite effective.

This paper focuses on the issue of fuzzy resilient control for synchronizing chaotic systems with time-variant delay and external disturbance. The goal is to design a fuzzy resilient controller with additive gain perturbations to guarantee that not only the drive and response systems are asymptotically synchronized in the absence of external disturbance, but also the synchronization error system has a prescribed disturbance attenuation index under the zero initial condition. By utilizing an appropriate Lyapunov–Krasovskii functional, the Bessel–Legendre inequality, and the reciprocally convex combination technique, a criterion on the stability and ${{\mathscr{H}}}_{\infty }$ performance of the synchronization error system is derived. Then, by means of some decoupling methods, a design scheme of the fuzzy resilient controller is developed. Finally, one numerical example is provided to examine the effectiveness of the fuzzy resilient controller design scheme.

This paper combines the traditional output regulation (OR) theory with fuzzy identification method, and designs a distributed output regulator for the multi-agent system (MAS). In view of the situation that the follower model in the MAS cannot be obtained accurately, the fuzzy model is established to make it approximate to the considered nonlinear discrete follower agent system first. By employing fuzzy identification method and OR theory, a distributed controller is proposed, make follower agents track the reference signal given by the dynamic leader. Two numerical examples demonstrate the obtained results.

In this paper, a modified methodology to find an optimal T-S fuzzy model based on an extended heterogeneous Multi-swarm PSO (MsPSO) algorithm is developed in order to enhance search ability of the classical MsPSO algorithm. However, this simple MsPSO algorithm search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome this drawback, two parameter automation strategies (inertia weight and acceleration coefficients) are introduced based on gray relational analysis in MsPSO to guarantee a highly accurate model. The performance of the proposed algorithm is evaluated by adopting standard tests and indicators which are reported in the specialized literature. The numerical results demonstrate that the proposed algorithm is significantly better than the original MsPSO algorithm and the rest of compared algorithms according to mean and standard deviation (std) tests. Next, to further validate the generalization ability of the Improved OptiFel approach, the proposed algorithm is secondly applied on the BoxJenkins Gas Furnace system. Then, the improved OptiFel method is applied to an irrigation station process in order to provide an optimal T-S fuzzy model. Compared to the other existing methods, we achieve the result that the improved OptiFel can generate good fuzzy model with high accuracy and strong generalization ability.

In this paper, we propose a fuzzy tracking control for chaotic systems with immeasurable states. First we represent the chaotic and reference systems into T–S fuzzy models. Some properties concerning the premise variable selection and controller placement for chaotic systems are discussed. When considering immeasurable states, an observer is designed along with the controller to track a reference model which is a fixed point, a stable nonlinear system, or a chaotic system. For different premise variables between the plant and reference models, a robust approach is used to deal with the problem. The conditions for dealing with the stability of the overall error system are formulated into LMIs. Since the simultaneous solution to both the controller and observer gains with disturbances are not trivial, a two-step method is utilized. The methodology proposed above is applied to both continuous-time and discrete-time chaotic systems. Two well-known examples, the Chua's circuit for continuous-time and Hénon map for discrete-time, are used in numerical simulations and DSP-based experiments. The results verify the validity of theoretical derivations.

This paper proposes a fuzzy model-based chaotic encryption approach using synchronization. The cryptosystem uses T–S fuzzy models to exactly represent discrete-time chaotic systems into separate linear systems. Then the synchronization problem is solved using linear matrix inequalities. The advantages of this approach are: the general and systematic T–S fuzzy model design methodology suitable for well-known Luré type discrete-time chaotic systems; flexibility in selection of chaotic signals for cryptosystem secure key generator; and multiuser capabilities. Especially taking a chaotic superincreasing sequence as an encryption key enhances the chaotic communication structure to a higher-level of security compared to traditional masking methods. In addition, numerical simulations and DSP-based experiments are carried out to verify the validity of theoretical results.

It has been known that very complex chaotic behaviors can be observed in a simple first-order system with time-delay. This paper presents a fuzzy model-based approach for synchronization of time-delayed chaotic system via a scalar output variable. Takagi–Sugeno (T–S) fuzzy model can represent a general class of nonlinear system and we employ it for fuzzy modeling of the chaotic drive and response system with time-delay. Since only a scalar output variable is available for synchronization, a fuzzy observer based on T–S fuzzy model is designed and applied to chaotic synchronization. We analyze the stability of the overall fuzzy synchronization system by applying Lyapunov–Krasovskii theory and derive stability conditions by solving linear matrix inequalities (LMI's) problem. A numerical example is given to demonstrate the validity of the proposed synchronization approach.

An impulsive T–S fuzzy model is presented in this paper. The stability of impulsive controlled T–S fuzzy system has been analyzed theoretically. The proposed impulsive control scheme seems to have a simple control structure and may need less control energy than the normal continuous ones for the stabilization of T–S fuzzy system. Some typical chaotic systems, such as Chua's circuit, Lorenz system and Chen's chaotic system, are considered as illustrations to demonstrate the effectiveness of the proposed control scheme.

In this paper, we propose a fuzzy model-based methodology to deal with various control objectives for discrete-time chaotic systems from a unified viewpoint. With intent to unify the design process, we introduce a new design concept called virtual-desired-variable synthesis. Then, both the chaotic control and chaotification are eventually treated as a stabilization problem. Consequently, the conditions concerning the stability of the closed-loop system are formulated into LMIs. A feasible solution of the LMI problem guarantees the quadratical stability and gives the state feedback gains as well. The well-known Hénon map is used to demonstrate the unified approach.

In this paper, the problem of H^∞ control design is studied for a nonlinear chaotic system via its corresponding Takagi–Sugeno (T–S) fuzzy model. It is known that many nonlinear chaotic systems can be exactly represented by their corresponding T–S fuzzy models. First, in this study, the T–S fuzzy model is proposed to represent a class of nonlinear chaotic systems. Next, a linear output feedback control scheme, where only linear controller and linear observer are considered, with H^∞ setting is proposed for the nonlinear chaotic system. Then, based on the T–S fuzzy model and the proposed linear control scheme, the H^∞ controller design problem for nonlinear chaotic systems is characterized in terms of minimizing the attenuation level subject to some linear matrix inequalities (LMIs), which is also called eigenvalue problem (EVP), through the scanning of a positive parameter. Finally, the proposed control scheme is applied to a chaotic system, namely Chua's circuit, to illustrate the robust performance of the proposed linear controller and linear observer.

Construction firms that work on a contractual basis are generally more concerned with short-term rather than long-term financial strategies. The main focus in short-term financial strategies is on working capital management (WCM). Cash management is a major factor for achieving good liquidity and profitability. In this study we take into consideration the cash component of working capital management based on the target cash balance. We develop a practical model that should allow Taiwan construction firms to utilize the currently available cash and assets at any point in time in the most rational way. To help understand the issues involved, we first introduce a model developed by Miller and Orr. The relationship between project duration and progress towards completion is most effectively represented in practical construction management by the S-curve. Thus, in this study we plot the fuzzy S-curve regression based on the Takagi-Sugeno (T-S) fuzzy model. The practicality of the model is demonstrated using project cash flow and progress payment records from a sample project. The data are obtained from the Taipei City Government's Department of Rapid Transit Systems. Some tentative conclusions concerning the model are also given.

The boil-turbine system is a multivariable and strong coupling system with the characteristics of nonlinearity, time-varying parameters, and large delay. The accurate model can effectively improve the performance of turbine–boiler coordinated control system. In this paper, the boil-turbine model is established by interval type-2 (IT2) T-S fuzzy model. The premise parameters of IT2 T-S fuzzy model are identified by robust IT2 fuzzy c-regression model (RIT2-FCRM) clustering algorithm. The RIT2-FCRM is based on interval type-2 fuzzy sets (IT2FS) and applies a robust objective function, this clustering algorithm can reduce the impacts of outliers and noise points. The effectiveness and practicability of RIT2-FCRM are demonstrated by the identification results of the boiler–turbine system.