Types Of Fuzzy Models Research Articles

H∞ decentralized fuzzy model reference tracking control design for nonlinear interconnected systems

In general, due to the interactions among subsystems, it is difficult to design an H/sub /spl infin// decentralized controller for nonlinear interconnected systems. The model reference tracking control problem of nonlinear interconnected systems is studied via H/sub /spl infin// decentralized fuzzy control method. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. A state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H/spl infin/ model reference tracking performance is achieved. Furthermore, the stability of the nonlinear interconnected systems is also guaranteed. If states are not all available, a decentralized fuzzy observer is proposed to estimate the states of each subsystem for decentralized control. Consequently, a fuzzy observer-based state feedback decentralized fuzzy controller is proposed to solve the H/sub /spl infin// tracking control design problem for nonlinear interconnected systems. The problem of H/sub /spl infin// decentralized fuzzy tracking control design for nonlinear interconnected systems is characterized in terms of solving an eigenvalue problem (EVP). The EVP can be solved very efficiently using convex optimization techniques. Finally, simulation examples are given to illustrate the tracking performance of the proposed methods.

Dynamic fuzzy model based predictive controller for a biochemical reactor

The kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In this study, a dynamic fuzzy model based predictive controller (DFMBPC) is presented for the control of a biochemical reactor. The DFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. The DFMBPC has the flexibility to opt with various types of fuzzy models whose choice also lead to improve the control performance. The performance of DFMBPC is evaluated by comparing with a fuzzy model based predictive controller (FMBPC) with no model adaptation and a conventional PI controller. The results show that DFMBPC provides better performance for tracking setpoint changes and rejecting unmeasured disturbances in the biochemical reactor.

Designing a fuzzy model by adaptive macroevolution genetic algorithms

In this paper the adaptive macroevolution genetic algorithms are proposed to identify three different types of fuzzy models. Several newly established techniques, such as adaptive choice function and macroevolution, are adopted into the simple genetic algorithms to improve the optimization capability. The genetic algorithms used here are controlled to retain the best solution in the population until a better one is found in the next generation. The procedures of applying the proposed genetic algorithms to optimize the fuzzy models are also investigated. We also compare whether the instantaneous or batch model is more suitable to adjust the fuzzy models. The refined fuzzy models are then used to remedy the prediction output from a grey system. The superiority of the adaptive macroevolution genetic algorithms to the simple ones is discussed and an example is given to verify our viewpoints. Simulation results from difference equation and prediction filter are presented to illustrate the effectiveness of the proposed genetic-based fuzzy grey model.

Detection and Isolation of Sensor and Process Faults in a Heat Exchanger Using a Fuzzy-Model Library

In this contribution, an approach to model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. The process is decomposed into several subprocesses and for each a nonlinear model is identified. This model library consists of Takagi-Sugeno type fuzzy models and is used to generate several estimates for process outputs and states. Comparing these estimates with measured signals leads to residuals which indicate the state of the system and provide information about the source of possible faults. Implementation of the models either as a parallel model or as a series-parallel model lead to different FDI results. In this contribution this different sensitivity is investigated also. The applicability of the multi-model based FDI is illustrated on an industrial scale thermal plant. Seven different process faults and eight different sensor faults can be detected.

FTSM — Fast Takagi-Sugeno Fuzzy Modeling

Takagi-Sugeno type fuzzy models are widely used for model-based control and model-based fault diagnosis. They provide high accuracy with relatively small and easy to interpret models. The problem that we address in this paper is that data driven identification of such fuzzy models is computationally costly. Whereas most identification algorithms for Takagi-Sugeno models restrict the model’s generality in order to simplify the identification, a different approach is taken here: we apply resilient propagation (RPROP), an efficient nonlinear optimization technique, for parameter identification in order to achieve a fast Takagi-Sugeno modeling (FTSM) that is suited to model high-dimensional data sets containing a large number of data.

Water bath temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this paper suitable for adaptive control of practical plant systems in general, and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified TSK (Takagi–Sugeno–Kang)-type fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a water bath temperature control system. As compared to the BPNN under the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomenon, but the NFIN also has perfect regulation ability.

Fuzzy model predictive control of nonlinear pH process

A new fuzzy model-based predictive control scheme was developed to control a nonlinear pH process. The control scheme is based on the Takagi-Sugeno type fuzzy model of the process being controlled. In the present fuzzy model predictive control method, the process model maintains a linear representation of the conclusion parts of fuzzy rules. Therefore, it has a significant advantage over other types of models in the sense that nonlinear processes can be handled effectively by embedding the linear characteristic. The fuzzy model of the pH process to be controlled was constructed and used in the predictive control algorithm. Results of computer simulations and experiments demonstrated the effectiveness of the present control method.

An online self-constructing neural fuzzy inference network and its applications

A self-constructing neural fuzzy inference network (SONFIN) with online learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing neural network learning ability. There are no rules initially in the SONFIN. They are created and adapted as online learning proceeds via simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to an aligned clustering-based algorithm. As to the structure identification of the consequent part, only a singleton value selected by a clustering method is assigned to each rule initially. Afterwards, some additional significant terms selected via a projection-based correlation measure for each rule will be added to the consequent part incrementally as learning proceeds. The combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network, a main feature of the SONFIN. In the parameter identification, the consequent parameters are tuned optimally by either least mean squares or recursive least squares algorithms and the precondition parameters are tuned by a backpropagation algorithm. To enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved.

線形多様性クラスタリングと楕円型ファジィモデル

線形多様性クラスタリングと楕円型ファジィモデル

Adaptive Predictive Control Based on Local Linear Fuzzy Models

Abstract In this paper, local linear fuzzy models are used for adaptive predictive control of nonlinear processes. First, an algorithm (LOLIMOT = local linear model tree algorithm) for off-line identification of Takagi-Sugeno type fuzzy models is briefly reviewed. In order to cope with time-variant nonlinear processes and also with the influence of non-measurable disturbances, an on-line adaptation of the model is performed. A recursive least-squares algorithm (RLS) is applied to update the linear parameters in the rule consequents locally. The local estimation results in low computational effort and avoids forgetting in non-active operating regimes. The effectiveness of the approach is demonstrated by simulations and by controlling the output temperature of a cross-flow heat exchanger.

The identification of fuzzy grey prediction system by genetic algorithms

The application of genetic algorithms to the identification of a fuzzy grey model is investigated. Based on a few past output data, the next output from the unknown plant can be predicted by the basic grey model. To improve the accuracy of the prediction model, a fuzzy controller is designed to determine the quantity of compensation for the output from the grey system. Genetic algorithms are used to optimize the roughly determined fuzzy model. A test pattern is then fed to the well-tuned fuzzy system to infer the quantity of compensation through the centre of gravity defuzzification method. The procedures of identifying three different types of fuzzy models are presented. Simulation results from a well-known example are used to demonstrate that simple modelling and accurate in prediction are the merits of the proposed methodology.

SYSTEM APPROACH TO DYNAMIC FUZZY MODELS

This paper describes the general framework within which dynamic systems can be modelled by various types of fuzzy models. The characteristics, advantages and disadvantages of the major kinds of fuzzy models are discussed in the context of several system representations. Unstructured (linguistic) and structured (Sugeno type) models are contrasted and compared.