英文文献-基于模糊控制的MATLAB SIMULINK仿真

Based On Fuzzy Controler On MATLAB Simulink SimulationAbstract: For improving the temperature control precision as the industry require. In this paper we introduce how to design Fuzzy controller in detail and how to model in MATLAB and use Fuzzy Toolbox and SIMULINK in MATLAB to realize the computer simulation of parameters control system. Using the algorithm of Fuzzy control in the system,the temperature was controlled in good state.At present,the system has been used in the phase of the application and the pilot of the resistance furnace temperature in the actual industrial,and satisfying results were achieved.Practice shows that Fuzzy control method improves the lealtime performance,stability and accuracy of controlling and makes the operation simplified.The use for reference of the method was obviously in industrial application.Keywords:Fuzzy Controler,MATLAB, SIMULINK,SimulationI Introduction Incineration of municipal solid waste (MSW) is a practical way to reduce waste and to produce energy. Mass burn incineration technology has been widely used to solve the problem of solid waste disposal in the metropolitan areas in China. At present, classical PID control strategies are still the main tools for the incineration controller in China. This kind of strategy, however, is inadequate for the incineration process in practice, since, in China, the collection system of classified refuses is not yet well established and both heating value and the moisture content of the collected waste for incineration change widely. A PID-type control can cause the problem of temperature tendency not corresponding with the feed and may even lead to inverse-regulation. Therefore, there is the necessity to pursue alternative means to control temperature in the incineration process.Over the last 10 years,many studies have been carried out on the incineration process,one primary area of interest is the waste incineration numerical models along with experiment analysis . A numerical model may be constructed on the basis of fluid mechanics and heat transfer, but this model is usually too complicated for use in real-time control. Alternatively, a model can be built with the help of the fuzzy rules that characterize the human experts knowledge and mimic the experts handling about a given incineration process ; such a model is very suitable for controlling objects in the presence of non-linearity and even with unknown structure. Recent development of fuzzy control algorithms for incineration processes has focused on the automatic adaptation of fuzzy logic, the techniques of genetic algorithm (GA) and neural network (NN) are used to fix the adaptive parameters in the fuzzy control system. In general, a fuzzy logic controller refers to a fuzzy system consisting of fuzzifier, fuzzy rule base, fuzzy inference engine and defuzzifier. The fuzzifier converts the measured data into an acceptable form by the fuzzy controller. The fuzzy rule base contains a set of IFTHEN rules relating the measured variables to control inputs. While the antecedent part classifies the behavior of measured variables by fuzzy membership functions, the consequence part expresses the essential action of control inputs in each rule. The fuzzy inference engine is to derive a reasonable action regarding a specific situation based on the given rule base.At last, the defuzzifier converts the fuzzy reasoning outcome to the non-fuzzy data that can be accepted by the real equipment . The adaptive fuzzy approach is a system based on a fuzzy system together with a self-adapting algorithm and the algorithm can be GA, NN, etc. Fig.1 shows the fundamental configuration of an adaptive fuzzy controller. In this work, the fuzzy logic strategy with the adaptive factors is used to improve the incineration temperature control system and a new correction model is adopted to determine the proper adaptive factors.MATLAB / Simulink is a universal language of scientific computing and simulation, and the establishment of MATLAB, Simulink is a system block diagram and block diagram-based system-level simulation environment, the environment provides a number of specialized modules library: such as CDMA Reference Blockset, DSP (Digital Signal Processor) module library and so on. It is a dynamic system modeling, simulation and analysis of simulation results package has the following characteristics:(1) to invoke the preparation of the agent module to the module block diagram of the system is connected into, making the modeling and engineering simulation system block diagram of unified, more comprehensive research communication systems with high openness.(2) allows the user to freely modify the module parameters, and can seamlessly use all the analysis tool MATLAB with high interactivity.(3) simulation results can be almost real time to be displayed in graphical or data, which is the same laboratory.Fuzzy logic control, automation development and the future strategy, in which great attention has been paid, is an Intelligent Control Department. It uses linguistic rules and fuzzy sets for fuzzy reasoning. In order to solve complex systems, including nonlinearity, uncertainty and accurate mathematical model is difficult to establish the problem, fuzzy control technology to become widely used. Temperature, often using the traditional PID control algorithm is less obvious 1: conditions change. Also will change the system parameters, PID parameters need to be adjusted, otherwise it would be worse dynamic characteristics, control accuracy decreased: the temperature deviation is large, prone to the phenomenon of integral saturation, resulting in control for too long and so on. in the same Time, fuzzy toolbox and SIMULINK in MATLAB to achieve the parameter control system computer simulation, to promote efficiency and system design 2 for accuracy.The whole system mainly by the AT89S51 microcontroller, temperature data acquisition circuit, the zero crossing detection and trigger circuit, keyboard and display circuit, memory circuit (CF card), sound and light alarm circuit, reset circuit and the corresponding control software of several parts.II Ease of UseIn theory, the higher dimension fuzzy controller, the control precision is higher. But the higher dimension, Control algorithm is also more difficult to achieve. Currently, the widely used two-dimensional fuzzy controller Nonlinear control law will help ensure system stability. Reduce the response process overshoot. Fuzzy controller includes fuzzification, fuzzy reasoning fuzzy three-part settlement.A. Fuzzy linguistic variables and membership functions to determineFuzzy controller and dual-input, single output structure, the input linguistic variables as temperature, rate of change of error e and error e, the output variable duty cycle for the SCR-time changes in the amount of . Temperature error e = t-T, where t is the actual temperature, the temperature settings.The basic domain of the error e is -30 , +30 , e in the fuzzy domain of: X = -6, -5, -4, -3, -2, -1,0 , +1, +2, +3, +4, +5, +6, the error e of the quantization factor = 6 / 30 = eK0.2. Linguistic variables E selected 7 language value: PB, PM, PS, 0, NS, NM, NB.Error rate of change of the basic domain of e is -24, +24, in the fuzzy domain of cy = -6, -5, -4, -3, -2, -1,0, +1 , +2, +3, +4, +5, +6, the error rate of change of the quantization factor e = 6 / 24 = 0.25. Linguistic variables selected 7 language cKEvalue: PB, PM, P3, 0, NS, NM, NBControl the amount of change in the basic domain of is -0.6,0.6, in the fuzzy domain of Z = -6, -5, -4, -3, -2, -1,0, +1, +2, +3, +4, +5, +6, control the amount of change scale factor = 0.6 / 6 = 0.1. Linguistic variables selected 7 language value: PB, PM, PS, 0, NS, NM, NB.Lessons learned through practice. Determine the language variable fuzzy set membership function, thus establishing the language variable.Lessons learned through practice. Determine the language variable fuzzy set membership function, thus establishing the language variable assignment tableEcUIII Design of fuzzy control rulesFrom the above discussion of the incineration process, it is obvious that the incineration temperature is the main factor to affect the burn-off ratio and the thermal destruction reduction ratio and the economic operation of the boiler as a consequence. In the previous studies, researchers have used the classical fuzzy logic method to control the incineration temperature and have obtained some improved results under certain given conditions . The classical fuzzy controller, however, has no adapting algorithm, its rule base is described by IFTHEN sentences, and thus, the fuzzy rules cannot be corrected automatically after they are given. For example, after the repair period, the heating surface characteristic is changed and/or the length of ram travel and the moving speed of the grate are changed; all these changes will make the existent rule base invalid, and the rule base in the classical fuzzy controller has to be modified manually in order to make the classical controller work in the new situation. This manual modification is clearly inconvenient for the frequent changing modes of operation. Compared with the classical fuzzy controller, the adaptive fuzzy controller can avoid these shortages and make the system adapt to the changing conditions automatically.Design principles of fuzzy control rules is the system output response to dynamic and static characteristics of the best: When the error is large or larger, the Selection Control the amount of the error as soon as possible to eliminate the main; and the error is small, the selection control input to be taken to avoid overshoot, The stability of the system as a starting point. Test based on actual operating experience, analysis, induction, resistance furnace temperature control to determine the rules as shown in Table 4, the table in the space X that can not happen.The table 4:the fuzzy control ruleE. The establishment of fuzzy control query tableTable 4 contains the control rules can be written in the form of the following statements:IF E=Aj AND EC=Bj THEN U=Cij （ i=1,2,7;j=1,2,7） ， Where Aj ， Bj ， Cij was error, error change and control the amount of change in their respective domain of the fuzzy sets.For the 45 rules. The overall fuzzy relation to:The membership function R:When the error, error change were to take Aj , Bj output control the amount of Uij by the fuzzy inference rules can be synthesizedOn the field for X, Y all combinations of all the elements to strike the appropriate amount of control variable changes in the language of fuzzy sets, and the method by which the maximum fuzzy membership of fuzzy set of judgments. To obtain the domain Z of the elements to control the amount of change that value u. The system is based on off-line calculation, we can establish the fuzzy controller in Table 5 lookup table.After computing the lookup table. Its pre-stored in the computer storage unit. In the actual control. Fuzzy controller changes the value of first quantization error and the error to the appropriate language variable on the domain. Find according to quantify the results of fuzzy control query table directly to obtain the control volume. To achieve real-time control system quickly.IV REPARE YOUR PAPER BEFORE STYLINGSIMULINK in Matlab is system modeling and simulation platform for users, adopting agile module combination to create dynamic system with the main characteristics of fast and accuracy. So it is a more effective method to gain better performance with SIMULINK in complex nonlinear system. Run Matlab7.0 and open command window, click Start in the left-hand comer and Toolboxes. Now Fuzzy Logic can be found. An alternative method: input fuzzy in command window, then entry fuzzy logic editor and set a new FIS document and choose Mamdani as a type of controller. According to analyzing above, The universe range of e and ec from -6 to 6,while u from 0 to 6.Input and output variables can be set and control rules table is filled in the manner of ifthen. Fig.2 and Fig.3 depict fuzzy membership function curve of input and output variables. FigA shows other settings as follows 4. We control the temperature of resistance furnace to be simulated map:V RESULTS AND CONCLUSIONThe above analysis has demonstrated that the control quality of the fuzzy adaptive controller is superior to