عنوان مقاله
کنترل فازی دو فرمان CSTR غیر خطی
فهرست مطالب
مقدمه
سیستم استنباط فوزی بکارگرفته شده
کنترل پیشگویی عصبی تک فرمان CSTR
کنترل دوفرمانCSTR
ملاحظات ثبات و پایداری
نتیجه گیری
بخشی از مقاله
پایداری در برابر نویزها و عدم قطعیت ها
کنترلر تغذیه رو به جلو تنها بخش بر مبنای مدل سیستم کنترل ترکیبی پیشنهاد شده می باشد. این قبیل کنترلرها معمولاً نسبت به نویز و عدم قطعیت های پارامتر حساس تر می باشند. حساسیت بالا نسبت به عدم قطعیت یا نویزها به خاطر کنترلر تغذیه رو به جلو بر مبنای مدل می تواند یک نقص جدی برای سیستم کنترل پیشنهاد شده تلقی گردد.
کلمات کلیدی:
Double-command fuzzy control of a nonlinear CSTR Morteza Mohammadzaherf and Lei Chen Mechanical Engineering School, University of Adelaide, Adelaide 5005, South Australia (Received 15 April 2009 • accepted 27 July 2009) Abstract -In this research, double-command control of a nonlinear chemical system is addressed. The system is a stirred tank reactor; two flows of liquid with different concentrations enter the system through two valves and another flow exits the tank with a concentration between the two input concentrations. Fuzzy logic was employed to design a model-free double-command controller for this system in the simulation environment. In order to avoid output chattering and frequent change of control command (leading to frequent closing-opening of control valves, in practice) a damper rule is added to the fuzzy control system. A feedforward (steady state) control law is also derived from the nonlinear mathematical model of the system to be added to feedback (fuzzy) controller generating transient control command. The hybrid control system leads to a very smooth change of control input, which suits real applications. The proposed control system offers much lower error integral, control command change and processing time in comparison with neuro-predictive controllers. Key words: Fuzzy Control, Hybrid Control, CSTR, Nonlinear System, Steady State Control INTRODUCTION Catalytic continuous stirred tank reactors (CSTR)s have been extensively used as a benchmark for testing different control systems. These systems are multi-input and multi-output and may be highly nonlinear. Self-tuning PIDs [1,2] robust controllers [3], adaptive-like control systems [4], and different kinds of nonlinear predictive controllers [5,6] have been successfully tested on this class of chemical systems. The CSTR is also known as an outstanding example for the application of neuro-predictive controllers [7], which are a sub-class of nonlinear predictive controllers. Moreover, fuzzy logic controllers are used in the control of CSTRs to generate either the control command directly [8,9] or control command increments [10,11]. As well as improving the performance; other aims achieved by the application of fuzzy control systems on CSTRs are stability guarantee [8,12] and disturbance rejection [13,14]. In this paper, at first, successful control of a non-thermic CSTR by neuro-predictive technique is reported. In this test, the flow mass rate of one of two entering flows is subject to adjustment in order to control outlet concentration of the tank Although, this problem is known as a good example for neuro-predictive control [7,15]. This technique worked both ineffectively (in terms of offering improper performance) and inefficiently (in terms of needing heavy computation) when it was tried for double-command control (to adjust the mass rate of both inlet flows). This paper then presents a hybrid control system designed to adjust the flow rates of both entering flows simultaneously. In the presented control system, the control commands are the sum of a transient control command whose increments are generated by a non-model-based fuzzy logic controller and a steady state control command generated by a set-point dependent control law. Finally, the control system was tested in simfTo whom correspondence should be addressed. E-mail: morteza.mohammadzaheri@adelaide.edu.au ulation environment. In order that the results are applicable in practice, the "input constancy" is particularly addressed; that is, the proposed control system is designed to reduce the change of control inputs as well as the error. THE UTILIZED FUZZY INFERENCE SYSTEM In this research, a non-weighted first-order Sugeno type fuzzy inference system, with AND connectors, is used as the fuzzy controller. A schematic of such a system is shown in Fig. 1. Each fuzzy rule includes two main parts: antecedent and consequent. Antecedents contain linguistic (fuzzy) values with membership functions. A 'membership function' is a function which receives the crisp (numeric) value of a variable (e.g., 25 oC) and returns another number in the range of [0,1], namely 'membership grade'. As a result, in each rule, the number of membership grades equals the number of fuzzy values in the antecedent. All these membership grades (in the range of 0 and 1) pass through a function, namely T-norm The output of the T-norm is the fire strength of the rule: fire strength of rule (wi)=Tnorm (all membership grades), (1) The fire strengths of rules (wi) are the outcome of this step. In a
