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In this paper, we address the extended dissipativity (ED) performance of delayed T–S fuzzy model (TSFM)-based wind generation systems (WGSs). First, a concept of coupled leakage time-varying delay (CLTVD) is proposed to construct a more widespread TSFM. Second, the relaxed term with time-delay-product function (TDPF) is introduced. Then, a befitting Lyapunov-Krasovskii functional (LKF) is constructed, which can handle the delay and its derivation. Third, by using valid integral inequalities, new stabilization criteria are established. Meanwhile, the desired fuzzy quantized control with CLTVD is designed. Finally, simulation results are given to show the validity and superiority of our derived results.

This paper addresses the issue of the state estimation for cyber–physical systems (CPSs) with limited communication resources, sensor saturation and denial-of-service (DoS) attacks. In order to conveniently handle nonlinear term in CPSs, a Takagi–Sugeno (T–S) fuzzy model is borrowed to approximate it. The event-triggered scheme and quantization mechanism are introduced to relieve the effects brought by limited communication resources. By taking the influence of sensor saturation and DoS attacks into account, a novel mathematical model of state estimation for CPSs is constructed with limited communication resources. By using the Lyapunov stability theory, the sufficient conditions, which can ensure the system exponentially stable, are derived. Moreover, the explicit expressions of the event-based estimator gains are obtained in the form of linear matrix inequalities (LMIs). At last, a simulated example is provided for illustrating the effectiveness of the proposed method.

This paper investigates the sensor and actuator fault (SAF) estimations and the fault tolerant tracking control (FTTC) problem for uncertain systems represented by Takagi–Sugeno (T–S) fuzzy model affected by Unknown Bounded Disturbance (UBD). A robust descriptor adaptive observer based FTTC is firstly designed to simultaneously estimate the unmeasurable system states and SAF vectors and then to track reference trajectories. Sufficient design conditions are developed in terms of Linear Matrix Inequalities ( L M I s ) . The gains of both observer and fault tolerant controller are computed by solving a set of LMI constraints in single step. Robustness against external disturbances is analysed using the H ∞ performance index to attenuate its effect on the tracking error for bounded reference inputs. Finally, simulation results are illustrated by considering three types of actuator faults. Moreover, two comparisons are presented to show the mentioned process effectiveness.

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions.

We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.

In this paper, an efficient fuzzy model-based leak detection algorithm is designed for a pilot heat exchanger. A dynamic fuzzy model of the physical plant is first derived from input-output measurements using a fuzzy clustering technique. This model is run in parallel to the process for symptom generation. The leak detection mechanism has been tested and validated on the real co-current heat exchanger, and has proven to be efficient in detecting leaks of different magnitudes in the water circulation pipe.