Modele dayalı öngörülü ağ baglantılı kontrol sistemi

Ağ bağlantılı kontrol sistemlerinin endüstriyel alandaki ihtiyaçları karşılayan çeşitli avantajları vardır. Uygulamalar karmaşıklaştıkça ağ bağlantılı kontrol sistemlerinin kullanımının kaçınılmaz hale gelmesi beklenmektedir. Ancak haberleşme ağının neden olduğu belirsiz gecikmeler ve veri kayıpları, çevrim dinamiklerini olumsuz etkilemekte ve kararsızlıklara sebep olabilmektedir. Bu çalışmada veri gecikmesi ve kaybı ile algılayıcı gürültüsü gibi ideal olmayan durumlarda da çalışabilen bir ağ bağlantılı kontrol sistem mimarisi tanıtılacaktır. Yapı olarak, kontrol edilen sistemin bir modelinin, kontrolörün de içinde bulundurulması sayesinde haberleşme ağının neden olduğu kayıplar ve gecikmelerin kompanze edilmesi sağlanmaktadır. Model sayesinde, öngörülmüş kontrol çıktıları hesaplanıp haberleşme gerçekleşemediği durumlarda sistemin bunlarla kontrolü sayesinde yüksek derecede veri kayıplarında bile kararsızlıg˘ın önlenmesi amaçlanmaktadır. Önerilen yöntemde kontrol edilen sistemin durumu ile kontrolör içindeki modelin durumunun eşleştirilmesi önemli bir problem haline gelmektedir. Bu yapı bilgisayar, kontrol ve haberleşme dallarının özelliklerini kullanarak her birinin eksiğini tamamlamaya yönelik olup, çeşitli kontrol metotlarıyla kullanılmaya açıktır. Önerilen Modele Dayalı Öngörülü¨ Ağ Bağlantılı Kontrol Sistemi (MODOAKOS) benzetim yolu ile bir doğru akım motorunun kontrolüne uygulanmıştır. Normal ağ bağlantılı kontrol sistemin kararlılığını bozucu gecikme ve kayıplar varken bile önerilen sistem altında kontrol uygulandığında kararlı çalışma bozulmamış ancak referans girişine olan cevabın geciktiği gözlenmiştir

Model based predictive networked control systems /

Advantages of networked control systems (NCS) are very diverse and NCS’s address many of the demands of industrial development. As more and more sophisticated problems arise, networked control systems will not only become a convenience or an advantage but they will become an indispensable necessity. However usage of networked control systems introduces different forms of timedelay uncertainty in closed-loop system dynamics. These time delays are caused by the time sharing of the communication medium as well as computation time necessary for control algorithms and digital to analog conversions and have a destabilizing effect on system performance. Computational power of computers has increased dramatically; networks speed has also increased. Although both the network and computer architectures have tended to improve throughput over time, their real-time characteristics have not evolved to match the requirements from a control point of view. New control methodologies that cope with these factors and even take advantage of them are emerging. This work first examines some current methods in design and implementation of networked control systems that try to improve existing methods. Then a novel networked control system architecture that runs under non ideal network conditions with packet loss and noise is introduced. The proposed network control system architecture uses a model to predict the plant states into the future and generate corresponding control signals, then an array of the predicted control signals is sent to the actuator node side of the NCS rather than a single control signal like in basic networked control systems. This array of signals can control the plant if they are applied consecutively with sampling time intervals. However this is not the case under ideal conditions, where the network is lossless. Only the first control signal in each array is applied to the plant as a newer packet arrives every sampling period. The remainder of the array of predicted control signals is only used when packet loss occurs. This approach enables the system to be controlled in a pre-simulated manner and stability can be maintained even with high packet loss probabilities. Synchronization of the network elements becomes a major problem in this approach since models are involved. Synchronizing the actuator and controller nodes is done by an algorithm that can identify control signal arrays that have trustable information. Also the controller has a distributed architecture; some parts of the controller are implemented in the sensor node. This is to ensure that sensor to controller synchronization is not broken. The proposed model based predictive networked control system architecture was tested on a DC motor. The effects of packet loss were examined to reveal that the packet loss does not cause destabilization of the system, when packet loss occurs and the control packet cannot be sent to the actuator node, which prevents the changes in reference from being applied to the plant. The overall effect is the retardation of the response of the plant to the reference. Effects of noise are also examined. Under low packet loss conditions noise does not have an unusual effect on the system but when packet loss increases noise cannot be tolerated because the feedback loop is interrupted due to packet loss. Finally a method for determining the number of predictions to be made at the controller node (the prediction horizon) is suggested. The systems settling time is examined and the settling time is taken as the basis for the prediction horizon. The transmission of a single array of control signals from the controller node to the actuator node will enable the system to reach the desired reference. However this approach is only valid for open loop stable systems

A versatile and reconfigurable microassembly workstation

In this paper, a versatile and reconfigurable microassembly workstation designed and realized as a research tool for investigation of the problems in microassembly and micromanipulation processes and recent developments on mechanical and control structure of the system with respect to the previous workstation are presented. These developments include: (i) addition of a manipulator system to realize more complicated assembly and manipulation tasks, (ii) addition of extra DOF for the vision system and sample holder stages in order to make the system more versatile (iii) a new optical microscope as the vision system in order to visualize the microworld and determine the position and orientation of micro components to be assembled or manipulated, (iv) a modular control system hardware which allows handling more DOF. In addition several experiments using the workstation are presented in different modes of operation like tele-operated, semiautomated and fully automated by means of visual based schemes

Mikro montaj İş İstasyonu

Bu makalede, mikro boyuttaki komponentlerin verimli ve güvenilir montajı için açık-mimarili, tekrar yapılandırılabilir bir mikromontaj iş istasyonu sunulmaktadır. Bu iş istasyonu mikro dünyadaki problemlerin çözümlendirilmesine yardımcı olmak amacıyla bir araştırma aracı olarak tasarlanmıştır. Böyle bir iş istasyonunun geliştirilmesi aşağıdaki alt sistemlerin tasarımını içermektedir: (i) montaj görevlerininin gerçekleştirilebilmesi için yeterli hareket menzilini ve hassasiyeti sağlayabilecek hareket platformlarından oluşan bir manipülatör sistemi, (ii) mikro dünyanın görselleştirilmesini ve montajı yapılacak olan mikro parçaların konum ve yönelimlerini belirleyebilmek için bir görü sistemi, (iii) dayanıklı bir denetleme sistemi ve bunlara ek olarak manipülasyon araçlarının kolayca değişmesine olanak sağlayan ve sistemin önceden belirlenmiş göreve hazır hale getirilmesine yardımcı olacak uç takımlar için gerekli fikstürler. Ayrıca sistemde kumandalı ve yarı otomatik montaj uygulamaları da gerçekleştirilmiştir. Tasarım mikro parça manipülasyonu içeren çeşitli uygulamalar yapılarak test edilmiştir. İş istasyonunun çok yönlülüğü ve yüksek doğrulukta konumlama yeteneği yapılan deneylerle gösterilmiştir

Çok Yönlü ve Tekrar Yapılandırılabilir Mikro Montaj İş İstasyonu

Bu makalede, mikro montaj ve mikro manipülasyon süreçlerindeki sorunların incelenmesi amacıyla bir araştırma aracı olarak tasarlanan ve geliştirilen çok yönlü ve tekrar yapılandırılabilir mikro montaj iş istasyonu ve yine aynı grup tarafından geliştirilen bir önceki sistem üzerinde mekanik ve denetim yapıları açısından yapılan geliştirmeler sunulmaktadır. Bu geliştirmeler; (i) daha karmaşık montaj ve manipülasyon işlemlerinin gerçekleştirilebilmesi için ek bir manipülatör modülünün eklenmesi, (ii) sistemi daha yetenekli kılabilmek için görü sistemi ve numune taşıyıcı platformlarına ek hareket serbestlik derecesi eklenmesi (iii) mikro dünyanın görüntülenmesi ve montajı yapılacak veya manipüle edilecek parçaların konum ve yönelimlerinin belirlenebilmesi amacıyla görü sistemi olarak yeni bir optik mikroskobun eklenmesi (iv) daha fazla serbestlik derecesinin denetimini sağlayabilmek amacıyla varolan sistemin daha modüler bir denetim sistemi donanımı ile değiştirilmesi gibi unsurları içermektedir. Ayrıca sistemde kumandalı, yarı otomatik ve görü bazlı yöntemler aracılığı ile tamamen otomatik çalışma modlarında yapılan deney sonuçları da sunulmaktadır

Model based predictive network control systems

The advantages of networked control systems are diverse and address many of the demands of industrial development. As more and more sophisticated problems arise, networked control systems will not only become a convenience or an advantage but an indispensable necessity. However usage of networked control systems introduces time-delay uncertainty in closedloop system dynamics. These time delays are caused by the time sharing of the communication medium as well as computation time necessary for control algorithms and digital to analog conversions and have a destabilizing e ect on system performance jeopardizing system stability. In this work a novel networked control system architecture that runs under non ideal network conditions where packet loss and noise is introduced. The proposed network control system architecture utilizes the computer, control and network disciplines to compensate for each others' inadequacies. The architecture is independent of the control algorithm used and uses a model to predict the plant states into the future to generate corresponding control outputs. This approach enables the system to be controlled in a pre-simulated manner and stability can be maintained even with high packet loss probabilities. Synchronization of the states of plant model and actual plant becomes a major problem in this approach. The proposed model based predictive networked control system architecture is simulated on a DC motor. The overall effect is that stability is maintained although the response of the plant to the reference can be delayed

Random network delay in model based predictive networked control systems

Networked control systems (NCS) provide many advantages for idustrial development. For larger scale systems and more complex systems usage of NCS’s will become mandatory. However usage of networked control systems introduces time-delay uncertainty in closed-loop system dynamics. These time delays are caused by the time sharing of the communication medium as well as computation time necessary for control algorithms, resulting in destabilization of the system and jeopardizing system stability. In this work a novel networked control system architecture that runs under non-ideal network conditions where packet loss and random time delays occur. previously introduced MBPNCS architecture is expanded by including random delay in the communication network. The delays and data losses caused by the communication network are compensated for using the computational power of the computer nodes of the networked control system. The architecture is independent of the control algorithm and uses a model to predict the plant states into the future to generate corresponding control outputs. This approach enables the system to be controlled in a pre-simulated manner and stability can be maintained even with high packet loss probabilities. In this approach, it has to be assured that the predicted control signals are applied while their prediction conditions are still valid. The proposed model based predictive networked control system architecture is simulated on a DC motor. The overall effect is that stability is maintained although the response of the plant to the reference can be delayed. The system remains stable even when there exists network jitter. The number of predictions that have to be made to keep the system running is also examined and a prediction horizon dependent on rise time of plant is proposed

Model Based Predictive Networked Control Systems

Networked control systems where the sensors, controller and actuators of a digital control system reside on different computer nodes linked by a network, aim to overcome the disadvantages of conventional digital control systems at the application level, such as difficulty of modification, vulnerability to electrical noise, difficulty in maintenance and upgrades. However random communication delay and loss on the network may jeopardize stability since the communication delay decreases the phase margin of the control system and data loss can be considered as noise. In this project, we propose a novel networked control method where satisfactory control is possible even under random delay and data loss. We keep a model of the plant inside the controller node and use it to predict the plant states into the future to generate corresponding control outputs. At every sampling period the states of the model are reset to the actual or predicted states of the plant. The ambiguity of plant state during periods of total communication loss are also addressed. The proposed model based predictive networked control system architecture is first verified by simulation on the model of a DC motor under conditions of data loss and noise. Then experiments are repeated on a dedicated test platform using a physical DC motor. Results show that significant amounts of data loss and delay can be tolerated in the system before performance starts to degrade