文献翻译-单容水箱液位模糊控制系统设计

英文翻译系别自动化系专业自动化班级学生姓名学号指导教师Single tank water level fuzzy control system designIn industrial process control, the amount usually charged with the following four kinds, namely, level, pressure, flow and temperature. Where in the liquid is not only common in industrial process parameters, and for direct observation, easy to measure. Level control is a common industrial process control, impact on production can not be ignored. For level control system, although the conventional PID control parameters fixed, it is difficult to ensure the control parameters of the system to adapt to changes and changes in working conditions, it is difficult to get the desired effect; fuzzy control parameters have not sensitive and robust and strong features . Single-tank liquid level control system with linearity, hysteresis, coupling characteristics, this paper for the study of the level system, the application of fuzzy control theory to control research.Single tank water system structureTank level control system consists of a single tank water system ontology and AD / DA data acquisition card and other components, allows the computer to set the level value by controlling the regulator, at the entrance of a regulator valve to control and maintain the water level does not change; valve at the outlet of the receiver D-A converter output signal directly controlled tank. Valves on the inlet and discharge control rely on typical self-balancing system.Fuzzy ControlFuzzy logic control referred to fuzzy control, based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic of a computer-based digital control technology. In 1965, the United States LAZadeh founded the fuzzy set theory; 1973 he gives definitions and theorems related to fuzzy logic control. In 1974, the British EHMamdani first statement in accordance with the composition of fuzzy control fuzzy controller, and apply it to the control of boilers and steam engines, get the lab's success. This pioneering work marked the birth of fuzzy control theory.Birth control is fuzzy and the development of social science and technology and the need inseparable. With the rapid development of science and technology in all areas of the automatic control system to control the precision required response speed, system stability and the ability to adapt to increasingly high, the studied systems are increasingly complex. However, due to a number of reasons, such as the charged object or process nonlinear, time-varying, multi-parameter strong coupling between the larger random noise, the intricate mechanism of the process, a variety of uncertainties and imperfect means of in situ measurements, difficult to establish the mathematical model of controlled object. While conventional adaptive control techniques can solve some problems, but the scope is limited. Good manual control for complex effects that are difficult to establish the mathematical model of the controlled object, using the traditional control methods, including methods of modern control theory based control is often better than a practical experience of operating personnel carried out. Because one of the important features of the human brain is the ability to identify and fuzzy things, judgment, fuzzy means can often seem imprecise achieve precise purpose.Contains five main parts, namely: the definition of variables , fuzzy , knowledge , logic and anti- blur . Define a variable that is the situation and determine the procedures to be observed considering control actions, such as the general control problem , the input variables and output error E output error rate of change EC, and fuzzy control variables as inputs U will control the next state . Wherein E, EC, U collectively referred to as fuzzy variables . Fuzzy input value to an appropriate ratio value domain conversion , using colloquial process variables to describe the physical quantity measured , to find the value of the membership degree according to the relative value of the appropriate language , the colloquial variable called fuzzy subset . Including database and rule base knowledge of two parts, one related to the definition of fuzzy database provides data processing ; while the rule base is controlled by agroup of language rules describe the control objectives and strategies. Imitating fuzzy logic judgment under the concept of human beings, the use of fuzzy logic and fuzzy inference inference method to obtain fuzzy control signals. This part is the essence of the fuzzy controller . Defuzzification : Convert fuzzy inference value obtained for the explicit control signals as input value system.Fuzzy controller designFuzzy controller input and error rate of change of the error, the error e = r-y, the rate of change of error ec = de / dt, where, r and y are respectively the level setpoint and measured value. The exact value of the error and error rate of change in the amount of blur blur becomes E and EC, the further you can get E and EC fuzzy language collections. E and EC by the vague language subsets and fuzzy relationship matrix, decision-making based on fuzzy inference rules synthesis, controlled amount of U, the U de vague, converted to the exact amount u, the D-A converter control valve actuators generate action.Fuzzy controller, the input signal quantization error e is 8 level (NB, NM, NS, NO, O, PS, PM, PB); the rate of change of error ec and the output variable u is 7 quantization level (NB , NM, NS, O, PS, PM, PB); error e, and the error rate of change of the output variable u ec domain of [-6,6]. The error e, error change rate ec and output variables membership functions chosen triangular membership functions. Fuzzy control rules are based on fuzzy reasoning, in the design of fuzzy control rules, you should consider the completeness of the control rules, cross and consistency, should ensure that for any given input has a corresponding control rules work. If the error is negative big change, but also for the negative rate of change of the error is large, it is necessary to adjust the control amount being small, in order to ensure the expected systematic development. On the basis of summing up experiences and basic professional knowledge, get control rules (see Table 1), designed a total of 49 (7 × 7) of the Rules.ece NB NM NS O PS PM PB NB PS PS PS PS PM PB PB NM NS PS PS PS PM PM PB NS NM NS O O PS PM PM O NB NM NS O PS PM PM PS NB NM NS O O PS PM PM NB NB NM NS NS PS PS PB NB NB NM NS NS NS NSCommonly used methods of reasoning fuzzy control system CRI reasoning table method, CRI reasoning analytical method, Mamdani inference method and the consequent direct function method. This selection is a direct Mamdani inference method, first find the fuzzy relation R, then the amount calculated according to the input control and clarity. This selection of 49 (7 × 7) of control rules, the fuzzy rule table of conditional statements can be described, for example, if e = NB and ec = NB then u = PS. Corresponding fuzzy relationship: R1 = A1 × B1 × C1, where, A1 is a fuzzy set of E, B1 is a fuzzy set of EC, C1 is a fuzzy set of U; fuzzy matrix R can be integrated according to R.Fuzzy control, simplify the complexity of system design, especially for nonlinear, time-varying lag model does not fully control systems; does not depend on accurate mathematical model of the controlled object; take advantage of the system control law to describe the relationship between variables; no value but with fuzzy variables to describe the language type system, the fuzzy controller does not have to establish a complete mathematical model of the controlled object; fuzzy controller is an easy to control and master the ideal nonlinear controller has better robustness, flexibility, robustness and better fault tolerance.单容水箱液位模糊控制系统设计在工业过程控制中，被控量通常有以下4种，即液位、压力、流量和温度。