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خانه مقالات-Article مقالات کامپیوتر-Computer Articles سیستم فازی-Fuzzy System Design and of Fuzzy Model-based Predictive Control – A Case Study
Design and Stability Analysis of Fuzzy Model-based[taliem.ir]

Design and of Fuzzy Model-based Predictive Control – A Case Study

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In the paper a fuzzy model based predictive control algorithm is presented. The proposed algorithm is developed in the state space and is given  in analytical form, which is an advantage in comparison with optimisation based control schemes. Fuzzy model-based predictive control is  potentially interesting in the case of batch reactors, heat-exchangers, furnaces and all the processes with strong nonlinear dynamics and high  transport delays. In our case it is implemented to a continuous stirred-tank simulated reactor and compared to optimal PI control. Some stability and design issues of fuzzy model-based predictive control are also given

توضیحات محصول

ABSTRACT

In the paper a fuzzy model based predictive control algorithm is presented. The proposed algorithm is developed in the state space and is given  in analytical form, which is an advantage in comparison with optimisation based control schemes. Fuzzy model-based predictive control is  potentially interesting in the case of batch reactors, heat-exchangers, furnaces and all the processes with strong nonlinear dynamics and high  transport delays. In our case it is implemented to a continuous stirred-tank simulated reactor and compared to optimal PI control. Some stability and design issues of fuzzy model-based predictive control are also given.

INTRODUCTION

The fundamental methods which are essentially based on the principal of predictive control are Generalized Predictive Control [4], Model  Algorithmic Control [21] and Predictive Functional Control [22], Dynamic Matrix Control [5], Extended Prediction Self-Adaptive Control [6] and  Extended Horizon Adaptive Control [30]. All those methods are developed for linear process models. The principle is based on the process model  output prediction and calculation of control signal which brings the output of the process to the reference trajectory in a way to minimise  the difference between the reference and the output signal in a certain interval, between two prediction horizons, or to minimise the  difference in a certain horizon, called coincidence horizon. The control signal can be found by means of optimisation or it can be calculated using  the explicit control law formula [3, 11]. The nature of processes is inherently nonlinear and this implies the use of nonlinear approaches in  predictive control schemes. Here, we can distinguish between two main group of approaches: the first group is based on the nonlinear  mathematical models of the process in any form and convex optimisation [8], while the second group relies on approximation of nonlinear  process dynamics with nonlinear approximators such as neural networks [28, 29], piecewise-linear models [20], Volterra and Wiener models [7],  multi-models and multi-variables [16, 23], and fuzzy models [1, 25]. The advantage of the latter approaches is the possibility of stating the  control law in the explicit analytical form.

Year: 2007

Publisher : Springer 

By :  Sašo Blažic · Igor Škrjanc

File Information: English Language/ 14  Page / size: 358 KB

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سال : 2007

ناشر : Springer

کاری از :   Sašo Blažic · Igor Škrjanc

اطلاعات فایل : زبان انگلیسی / 14 صفحه / حجم : KB 358

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