Zhang Huaguang

Modeling, identification, and control of a class of nonlinear systems

In this paper, we propose a new fuzzy hyperbolic model for a class of complex systems, which is difficult to model. The fuzzy hyperbolic model is a nonlinear model in nature and can be easily derived from a set of fuzzy rules. It can also be seen as a feedforward neural network model and so we can identify the model parameters by BP-algorithm. We prove that the stable controller can be designed based on linear system theory. Two methods of designing the controller for the fuzzy hyperbolic model are proposed. The results of simulation support the effectiveness of the model and the control scheme.

A fuzzy basis function vector-based multivariable adaptive controller for nonlinear systems

In this paper, a new fuzzy basis function vector (FBFV) approach for the adaptive control of multivariable nonlinear systems is presented. With this method, the nonlinear plant is first linearized. The linearized bias and uncertainties as well as disturbances are assumed to be included in the model structure and their upper bound will be adaptively learned by the FBFV method. The output of the FBFV is used as the parameters of the robust controller in the sense that both the robustness and the asymptotic error convergence can be obtained for the multivariable nonlinear system. The effectiveness of the proposed analysis and design method is illustrated with a simulated example.

Decentralized nonlinear adaptive control of an HVAC system

This paper presents a new decentralized nonlinear adaptive controller (DNAC) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. In this scheme, an HVAC system is considered to be two subsystems and controlled independently. The interactions between the two subsystems are treated as deterministic types of uncertain disturbances and their magnitudes are supposed to be bounded by absolute value. The decentralized nonlinear adaptive controller (DNAC) consists of an inner loop and an outer loop. The inner loop is a single-input fuzzy logic controller (FLC), which is used as the feedback controller to overcome random instant disturbances. The outer loop is a Fourier integral-based control, which is used as the frequency-domain adaptive compensator to overcome steady, lasting uncertain disturbances. The global DNAC controller ensures that the system output vector tracks a desired trajectory vector within the system bandwidth and that the tracking error vector converges uniformly to a zero vector. The simulated experimental results on the HVAC system show that the performance is dramatically improved.

Nonlinear adaptive control using the Fourier integral and its application to CSTR systems

This paper presents a new nonlinear adaptive tracking controller for a class of general time-variant nonlinear systems. The control system consists of an inner loop and an outer loop. The inner loop is a fuzzy sliding mode control that is used as the feedback controller to overcome random instant disturbances. The stability of the inner loop is designed by the sliding mode control method. The other loop is a Fourier integral-based control that is used as the feedforward controller to overcome the deterministic type of uncertain disturbance. The asymptotic convergence condition of the nonlinear adaptive control system is guaranteed by the Lyapunov direct method. The effectiveness of the proposed controller is illustrated by its application to composition control in a continuously stirred tank reactor system.

A practical approach to robust impulsive lag synchronization between different chaotic systems

In this paper, a practical impulsive lag synchronization scheme for different chaotic systems with parametric uncertainties is proposed. By virtue of the new definition of synchronization and the theory of impulsive differential equations, some new and less conservative sufficient conditions are established to guarantee that the error dynamics can converge to a predetermined level. The idea and approach developed in this paper can provide a more practical framework for the synchronization between identical and different chaotic systems in parameter perturbation circumstances. Simulation results finally demonstrate the effectiveness of the method.

A unified approach to fuzzy modelling and robust synchronization of different hyperchaotic systems

In this paper, a Takagi–Sugeno (T–S) fuzzy model-based method is proposed to deal with the problem of synchronization of two identical or different hyperchaotic systems. The T–S fuzzy models with a small number of fuzzy IF–THEN rules are employed to represent many typical hyperchaotic systems exactly. The benefit of employing the T–S fuzzy models lies in mathematical simplicity of analysis. Based on the T–S fuzzy hyperchaotic models, two fuzzy controllers are designed via parallel distributed compensation (PDC) and exact linearization (EL) techniques to synchronize two identical hyperchaotic systems with uncertain parameters and two different hyperchaotic systems, respectively. The sufficient conditions for the robust synchronization of two identical hyperchaotic systems with uncertain parameters and the asymptotic synchronization of two different hyperchaotic systems are derived by applying the Lyapunov stability theory. This method is a universal one of synchronizing two identical or different hyperchaotic systems. Numerical examples are given to demonstrate the validity of the proposed fuzzy model and hyperchaotic synchronization scheme.

Oil pipeline leak detection and location using double sensors pressure gradient method

The monitoring of oil pipeline is an important task for economical and safe operation, loss prevention and environmental protection from crude oil emission. A leak detection of oil pipeline, therefore, plays a key role in the overall integrity monitoring for a pipeline system. Especially for a long pipeline operated alongside of dense cropland, a leak detection system is an indispensable condition to allow its construction. In this paper, a leak detection and location approach based on double sensors pressure gradient method is proposed. An industrial application to a long oil pipeline is also illustrated. The result of application supports the effectiveness of the proposed method.

A simple fuzzy adaptive control method and application in HVAC

A simple and practical fuzzy sliding mode adaptive control is presented in this paper and applied to HVAC (heating, ventilating and air-condition). The effects of parameters variance of the air-conditioner are considered, and performance to uncertainty is assessed. The proposed control methodology address the fundamental issues of stability and performance requirement. Simulation results verified that the approach is superior to the PID algorithm, and the effectiveness of the technique is demonstrated by its application to the HVAC of one building.

A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systems

This paper proposed a fast synthetic genetic algorithm (FSGA). The algorithm has faster convergence speed and higher computation precision, and the number of individuals and populations decreased, respectively. To overcome long training time of back propagation (BP) algorithm, this paper combined FSGA with BP, and applied the hybrid method to short-term economic dispatch of hydrothermal power system. The simulation results demonstrate that the hybrid method is effective and training time is short.

Fault tolerant synchronization of chaotic systems based on T–S fuzzy model with fuzzy sampled-data controller

In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi–Sugeno (T–S) fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov–Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results.