Elektro hidrolik bir sistemin gerçeğe yakın konum kontrolü
Başlık çevirisi mevcut değil.
- Tez No: 75565
- Danışmanlar: DOÇ. DR. KENAN KUTLU
- Tez Türü: Yüksek Lisans
- Konular: Makine Mühendisliği, Mechanical Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 1998
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Makine Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Makine Teorisi ve Kontrol Bilim Dalı
- Sayfa Sayısı: 107
Özet
ÖZET Bu çalışmada dört yollu üç konumlu oransal valf kullanılarak hidrolik sistemin konum kontrolü gerçekleştirilmiştir. Sistemin kontrolünün gerçeğe yakın olabilmesi için her 5 milisaniyede bir sisteme kumanda gönderilmiştir. Sisteme PD, PD+D2, PD etkili ikili ve bulanık kontrol algoritmaları uygulanmıştır. Sistemin konum kontrolünün gerçekleştirilmesinde Matlab paket programından yararlanılmıştır. Aynı zamanda Fuzzy kontrolörün simülasyon parametrelerinin belirlenebilmesi için“Matlab Fuzzy Toolbox”tan yararlanılmıştır. Sisteme çeşitli kontrol algoritmaları uygulanmasıyla elde edilen simülasyon değerleri, yerleşme zamanı, yükselme zamanı, gecikme zamanı ve birim kumanda enerjisi bakımından karşılaştırılmış ve tablolar halinde verilmiştir. Sistem dış etkilerden daha az etkilenir hale getirilerek, kütlenin ve viskoz sürtünme katsayısının farklı değerleri için cevap eğrileri çizdirilmiş ve tablolarda verilmiştir. IX
Özet (Çeviri)
SUMMARY REALISTIC SIMULATION OF POSITION CONTROL OF AN ELECTROHYDRAULIC SYSTEM Fulid power systems are used in a wide variety of applications ranging from precision control systems, such as robotics and aerospace, to heavy industrial systems, such as forging presses and steel rolling mills, and mobile systems, such as agricultural machines and civil engineering plant. The relatively high power-to-weight ratio of electrohydraulics together with advantages of electronic signal processing produces a flexible and efficient means of power transfer and control. In general the advantages of hydraulic components are: Rapid response in changing speed or direction. Good stiffness characteristics. Zero backlash. Low rate of wear. Ease of accurate control of work table position and velocity. In this study, position control of an electrohydraulic cylinder operated by an an electrohydraulic proportional valve for different control strategies is investigated. Before deriving the mathematical model, typical system components used in the electrohydraulic control system and their steady-state (pressure/flow) characteristics are discussed. Bulk modulus is a measure of the compressibility of a fluid and inevitably required to calculate hydraulic undamped naturel frequencies in a system. The basic definition of fluid bulk modules aries by considering the copression of a fluid initally at atmospheric pressure and volume Vi to a new value V2 at pressure P Bulk modulus written as fallows, AP A mathematical model is derived for an asymmetric lineer actuator including the compressibility of fluid and nonlinearity of the control valve. The state space equations of the system using an underlapped valve are as follows: xXj -X2 X2 =(-Bv.X2 + A1.X3 -A2.X4 -F)/M X3=^-(Qi-A1.X2) **=Â^xö(A^-Q»> When the model is examined, we notoced that the variable coefficent have different degrees. Therefore, coeeficents of pressure variables are too small according to others. To avoid the differences between coefficents, a dimensional scaling is realized. From this point of view, we described: For variables of pressure X3* = io-5 X3 => X3 = 10s X3* X4* = 10'5 X4 => X4 = 105 X4 X3* = 10'5X3* => X3=105X3* x4* = io-5x4* => X4=105X4* For flow terms Qx = 10* Qx => Q,= 1 Qx Q2*=103Q2 => Q2 = 10-3Q2' New Model Xi = X2 XIX2=(105X3 At-105X4 -BvX2-Fd)/M X3*=f^-00-3Q1*-A1.X2). * BIO“5 * where flow rates are nonlinear functions of the state variables, for example for uv>8 Q, = k^uv + 8) sign(Ps -X3) V(Ps-X3).sign(Ps-X3) Q2 = k2(uv + 8) sign(X4 -Pt) V(X4-Pt).sign(X4-Pt) for -8< uv d uv = 0 FUZZY CONTROL The human being, when making decisions, tends to work with vague or imprecisie concepts which can often be expressed linguistically. For example, a human operator, when controlling a process usually encounters complex patterns of quantitative conditions, which are difficult to interpret accurately. The magnitute of the measurement is usually described as fast, big, slow, high etc. xiuAs the strategy he uses is vague and qualitatively described, the use of the ”Fuzzy Set Theory" in such an investigation is self evident. This vay of modeling the decision making process has been proposed by Zadeh (1968) and is based on the theory of approximate reasoning which enables certain classes of linguistic statements to be treated mathematically. Some investigations revealed that incorporating human intelligence with microcomputers into control systems would be a more efficient solution, and this led to the development of fuzzy control algorithms (Mamdani, 1974). Conventional control algorithms can be developed by the transfer function of the process. However in practice, it is not always easy to describe an engineering system by means of discrete transfer functions so as to realize ideal compensation. Since the devolopment of microprocessors in 1970's, microprocessor based controllers have come more popular. One obvious reason is that the microprocessors can be used to implement intelligent control algorithms to cope with varying environments as a result of load disturbances, process nonlinearities, and changes of plant parameters. Since its introduction by Mamdani and Assilian (1975), fuzzy controllers have been successfully implemented in many test cases and in actual industrial applications (Pappis and Mamdani, 1977; Umbers and King, 1980; Tong, Beck and Latten,1980), Tts nerformanoe is rlpnenHAfit upon thç availabilty of 2 reüabe linguistic control stragety which is not always easily formulated. Fuzzy controller offers the following advantages. 1- They do not require a detailed mathematical model to formulate the algorithms. 2- Because both error and change of the error are required to evaluate the control input, the fuzzy controller has more adaptive capabilitiy. 3- By using the different set of control rules, the fuzzy controller can operate for a large range of inputs. A fuzzy control algorithm consists of situation and action pairs. Conditional rules expressed in IF and Then statements are generally used. For example, the control rule might be, for a single input single output system. If the error is big positive (BP) AND change of error is medium positive (MP) THEN the input the system is medium negative(NM) If the error is small negative (NS) AND change of error is medium positive (PM) THEN the input to the system is medium (NM), where, e is difference between the process output (y) and the set-point ( yref), e =yref - y, change of error de =e(k)- e(k-l). For the defining control input to the controller (uv), the fuzzy reasining algorithm is used in the following way : 1- For each element in the table 1 the releated membership functions have to be calculated for e and de. xiv2- For each control rule the memebership function is calculeted as, u^cde) = min[u(e),n(de)] 3- The releated command value uv is calculeted using defuzzification process. Decision table for error (e) and change of error (de) is given Table 1. Table 1 Decision table for error(e) and change of error(de) XV
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