Model Öngörülü Kontrol Ve Bir Endüstriyel Model Öngörülü Kontrol Uygulaması

Abstract

Model tabanlı öngörülü kontrol (MBPC) veya diğer bir adıyla model öngörülü kontrol (MPC) standart PID kontrolünden daha gelişmiş ve endüstriyel proses kontrolünde önemli ve geniş çaplı etki yapmış, gelişmiş bir kontrol teknolojisidir. Günümüze kadar ki ana kullanım alanının kimya ve petrokimya endüstrileri olmasına karşın son zamanlarda getirdiği avantajlardan dolayı diğer proses endüstrilerinde de kullanımı hızla yaygınlaşmış ve hızla yaygınlaşmaya devam etmektedir. Çok değişkenli kontrol problemlerini ele alabilmesi, proses için önemli olabilecek sınır değerlerinde güvenle çalışabilmesi, aktüatörlere ait sınırlamaları analiz edebilmesi ve uzun ölü zaman problemlerini ortadan kaldırabilmesi bu kontrol teknolojisinin hızla yaygınlaşmasında önemli paya sahiptir. Tezin birinci bölümünde yukarıda bahsedilen model öngörülü kontrole daha detaylı bir giriş yapılmış, endüstride kullanılan model öngörülü kontrol ve dörtlü tank sistemi kısaca incelenmiş ve tezin amacı belirlenmiştir. İkinci bölümde model öngörülü kontrol ve model tanımlama incelenmiş. Model öngörülü kontrolün en iyi giriş ve çıkış değerlerini elde edebilmek için kullandığı birinci ve ikinci dereceden eniyileme yöntemleri araştırılmış ,basamak fonksiyonları ile sistem modeli elde etme üzerinde durulmuştur. Ayrıca Honeywell’e ait aralık (range) değerli kontrol yapısının kullandığı huni (funnel) ve bölgeler (zones) terimleri incelenmiştir. Son olarak ise dörtlü tank sisteminin modellenmesi üzerine durulmuştur. Üçüncü bölümde endüstride kullanılan model öngörülü kontrol teknolojilerine örnekler verilmiştir. Honeywell şirketi tarafından geliştirilmiş olan dayanıklı çok değişkenli model öngörülü kontrol (RMPCT) teknolojisi, kontrolcünün çalışma adımları ile birlikte aktarılmıştır. Dördüncü bölümde Honeywell şirketinin tasarlamış olduğu PID_PLA kontrol modülü tanıtılmış, kontrolün endüstriyel proseslerde kullanılabilmesi için yapılması gereken işlemler belirlenmiş ve Profit Loop PKS Assistant programı kullanımı ve program kullanılarak model tanımlama işlemleri anlatılmıştır. Son bölümde ise gerçek bir akış seviye kontrolü yapan bir proses işlemi, Honeywell kontrolcü kullanılarak sırası ile PID kontrol, ayar değerli ve aralık değerli model öngörülü kontrol işlemlerine tabi tutulmuştur. Son olarak bu işlemler sonrası elde edilen sonuçlar incelenmiş ve yorumlanmıştır. Elde edilen sonuçlar göstermiştir ki; sistemin iyi modellenmesi ile yapılan model öngörülü kontrol işlemi, en az PID ile yapılan kontrol işlemi kadar iyi sonuçlar vermiştir.

Predictive control, or model based predictive control (MPC or MBPC) as it is sometimes known, is the only advanced control technique –that is, more advanced than standart PID control- to have had a significant and widespread impact on industrial process control. The main reason; • The only generic control technology which can deal routinely with equipmant and safety constraints. Operation at or near such constrains is necessary for the most profitable or most efficient operation in many cases. The penetration of predictive control into industrial practice has also been helped by the facts that • Its underlying idea is easy to understand, • Its basic formulation extends to multivariable plants with almost no modification, • It is more powerful than PID control, even for single loops without constraints, without being much difficult to tune, even on difficult loops such as those containing long time delays. Today’s control software and technology offers the potential to implement more advanced control algorithms but often the preferred strategy of many industrial engineers is to design a robust and transparent process control structure that uses simple controllers. This is one reason why PID controller remains industry’s most widely implemented controller despite the expensive developments of control theory; however, this approach of structured control can create limitationson good process performance. One such limitation is the possible lack of a coordinator within the hierarchy that systematically achieves performance objectives. Another is the omission of a facility to accommodate and handle process operational constraints easily. The method of model predictive control (MPC) can be used in different levels of the process control structure and is also able to handle a wide variety of process control constraints systematically. These are two of reason why MPC is often cited as one of the more popular advanced techniques for industrial process applications. Surprisingly, MPC and the associated receding horizon control principle have a history control principle have a history of development and applications going back to the late 1960s; Jacques Richalet developed his predictive functional control technique for industrial application from that time onward. Work on using the receding horizon control concept with state space models can be identified in the literature of the 1970s, and the 1980s saw the emergence first of dynamic matrix control and then, towards the end of the decade, of the influential generalised predictive control technique. The methodology of all the controllers belonging to the MPC family is characterized by the following strategy; 1) The future outputs for a determined horizon N, called the prediction horizon, are predicted at each instant t using the process model. These predicted outputs y(t+k|t), for k = 1,2,…,N depend on the known values up to instant t (past inputs and outputs) and on the future control signals u(t+k|t), k = 0,1,…,N-1, which are those to be sent to the system and to be calculated. 2) The set of future control signals is calculated by optimizing a determined criterion in order to keep the process as close as possible to the reference trajectory r(t+k) (which can be the setpoint itself or a close approximation of it). This criterion usually takes the form of a quadratic function of the errors between the predicted output signal and the predicted reference trajectory. The control effort is included in the objective function in most cases. An explicit solution can be obtained if the criterion is quadratic, the model is linear and there are no constraints, otherwise an iterative optimization method has to be used. Some assumptions about the structure of the future control law are also made in some cases, such as that it will be constant from a given instant. 3) The control signal u(t|t) is sent to the process whilst the next control signals calculated are rejected, because at the next sampling instant y(t+1) is already known and step 1 is repeated with new value and all the sequences are brought up to date. Thus the u(t+1|t+1) is calculated (which in principle will be different to the u(t+1|t) because of the new information available) using the receding horizon concept. MPC and the associated receding horizon control principle have a history of development and applications going back to the late 1960s; Jacques Richalet developed his predictive function control technique for industrial application from that time onward. Work on using the receding horizon control concept with state-space models can be identified in the literature of the 1970s, and the 1980s saw the emergence first of dynamic matrix control and then, towards the end of the decade, of the influential generalised predictive control technique. Predictive control Technologies that have great impact on the industrial world and commercially available. Although there are companies that make use of technology developed inhouse, that is not offered externally, the ones listed below can be considered representative of the current state of the art of model predictive control technology. Their product names and acronyms are: • DMC Corp. : Dynamic Matrix Control (DMC) • Adersa : Identification and Command (IDCOM), Hierarchical Constraint Control (HEICON) and Predictive Function Control (PFC) • Honeywell Profimatics : Robust Model Predictive Control Technology (RMPCT) and Predictive Control Technology (PCT) • Ayar değeri Inc. : Setpoint Multivariable Control Architecture (SMCA) and IDCOM-M (multivariable) • Treiber Controls : Optimum Predictive Control (OPC) • SCAP Europa : Adaptive Predictive Control System (APCS) In this study, Honeywell Robust Model Predictive Control is introduced and a control application which is a flow and level control application is achieved by using Honeywell RMPCT. First of all in first section general information and history of model predictive control is given and the aim of the thesis is announced. In second section model predictive control is researched and analyzed associated with linear and quadratic programing optimazition and robustness. And Funnel and zones parameters which are used in Honeywell RMPCT tecnology in range control applications are researched. In third section model predictive control Technologies for industrial areas is introduced and detailed informations, structure and characteristics of RMPCT is investigated. In fourth section PID_PLA model predictive control modul which is designed by Honeywell is presented with how can be used and how can be installed. Also Profit Loop PKS assistant software program is presented how can be used and how can defined a system or process model with using profit loop PKS assistant. In last section flow-level control application is controlled by using PID control and setpoint model predictive control. And all results are analyzed and interpreted.