Designing the Model Predictive Control for Interval Type-2 Fuzzy T-S Systems Involving Unknown Time-Varying Delay in Both States and Input Vector

In this paper, the model predictive control is designed for an interval type-2 Takagi-Sugeno (T-S) system with unknown time-varying delay in state and input vectors. The time-varying delay is a weird phenomenon that is appeared in almost all systems. It can make many problems and instability while the system is working. In this paper, the time-varying delay is considered in both states and input vectors and is the sensible difference between the proposed method here and previous algorithms, besides, it is unknown but bounded. To solve the problem, the Razumikhin approach is applied to the proposed method since it includes a Lyapunov function with the original nonaugmented state space of system models compared to Krasovskii formula. On the other hand, the Razumikhin method act better and avoids the inherent complexity of the Krasovskii specifically when large delays and disturbances are appeared. To stabilize output results, the model predictive control (MPC) is designed for the system and the considered system in this paper is interval type-2 (IT2) fuzzy T-S that has better estimation of the dynamic model of the system. Here, online optimization problems are solved by the linear matrix inequalities (LMIs) which reduce the burdens of the computation and online computational costs compared to the offline and non-LMI approach. At the end, an example is illustrated for the proposed approach

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Kaotik sistemlerin klasik ve zeki yaklaşımlar ile kontrolü

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Elektronik devreler için kaos istenmeyen bir davranıştır. Bu tezde, kaotik sistemlerin kontrolü için iyi bilinen bazı kaos kontrol yöntemleri ile yapay zekâ tekniklerinin birlikte kullanımı önerilmiştir. Chua devresinin kontrolü kayma kipli kontrol yöntemi, yapay sinir ağları ve ikisinin bir arada kullanımı ile, Bonhoeffer–van der Pol devresinin kontrolü pasif kontrol yöntemi, bulanık mantık ve ikisinin bir arada kullanımı ile, Colpitts devresinin kontrolü ise geri-beslemeli kontrol yöntemi, sinirsel-bulanık ağlar ve ikisinin bir arada kullanımı ile gerçekleştirilmiştir. Sonuçlar karşılaştırmalı grafikler ile sunulmuştur. Sonuçlar, önerilen yaklaşımın kaotik sistemlerin denge noktasına kontrolünü daha hızlı sağladığını göstermiştir.Chaos is an undesired behaviour for electronic circuits. In this thesis, usage of some well-known chaos control methods with artificial intelligence techniques is proposed for the control of chaotic systems. Sliding mode control method, artificial neural networks and using both of them are applied for the control of Chua's circuit, the passive control method, fuzzy logic and using both of them are applied for the control of Bonhoeffer–van der Pol circuit, and the feedback control method, adaptive neuro-fuzzy inference system and using both of them are applied for the control of Colpitts circuit. The results are presented by comparative figures. They show that the proposed approach provides the control of chaotic systems to their equilibrium points more effectively

Robust Learning Control for Shipborne Manipulator With Fuzzy Neural Network

The shipborne manipulator plays an important role in autonomous collaboration between marine vehicles. In real applications, a conventional proportional-derivative (PD) controller is not suitable for the shipborne manipulator to conduct safe and accurate operations under ocean conditions, due to its bad tracing performance. This paper presents a real-time and adaptive control approach for the shipborne manipulator to achieve position control. This novel control approach consists of a conventional PD controller and fuzzy neural network (FNN), which work in parallel to realize PD+FNN control. Qualitative and quantitative tests of simulations and real experiments show that the proposed PD+FNN controller achieves better performance in comparison with the conventional PD controller, in the presence of uncertainty and disturbance. The presented PD+FNN eliminates the requirements for precise tuning of the conventional PD controller under different ocean conditions, as well as an accurate dynamics model of the shipborne manipulator. In addition, it effectively implements a sliding mode control (SMC) theory-based learning algorithm, for fast and robust control, which does not require matrix inversions or partial derivatives. Furthermore, simulation and experimental results show that the angle compensation deviation of the shipborne manipulator can be improved in the range of ±1°

Systematic Design of Type-2 Fuzzy Logic Systems for Modeling and Control with Applications to Modular and Reconfigurable Robots

Fuzzy logic systems (FLSs) are well known in the literature for their ability to model linguistics and system uncertainties. Due to this ability, FLSs have been successfully used in modeling and control applications such as medicine, finance, communications, and operations research. Moreover, the ability of higher order fuzzy systems to handle system uncertainty has become an interesting topic of research in the field. In particular, type-2 FLSs (T2 FLSs), systems consisting of fuzzy sets with fuzzy grades of membership, a feature that type-1 (T1) does not offer, are most well-known for this capability. The structure of T2 FLSs allows for the incorporation of uncertainty in the input membership grades, a common situation in reasoning with physical systems. General T2 FLSs have a complex structure, thus making them difficult to adopt on a large scale. As a result, interval T2 FLSs (IT2 FLSs), a special class of T2 FLSs, have recently shown great potential in various applications with input-output (I/O) system uncertainties. Due to the sophisticated mathematical structure of IT2 FLSs, little to no systematic analysis has been reported in the literature to use such systems in control design. Moreover, to date, designers have distanced themselves from adopting such systems on a wide scale because of their design complexity. Furthermore, the very few existing control methods utilizing IT2 fuzzy logic control systems (IT2 FLCSs) do not guarantee the stability of their system. Therefore, this thesis presents a systematic method for designing stable IT2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy systems when antecedents are T2 fuzzy sets and consequents are crisp numbers (A2-C0). Five new inference mechanisms are proposed that have closed-form I/O mappings, making them more feasible for FLCS stability analysis. The thesis focuses on control applications for when (a) both plant and controller use A2-C0 TSK models, and (b) the plant uses T1 Takagi-Sugeno (T1 TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear matrix inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included to validate the effectiveness of the new inference methods. Case studies reveal that a well-tuned IT2 TS FLCS using the proposed inference engine can potentially outperform its T1 TSK counterpart, a result of IT2 having greater structural flexibility than T1. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. In addition, a novel design methodology is proposed for IT2 TSK FLC for modular and reconfigurable robot (MRR) manipulators with uncertain dynamic parameters. A mathematical framework for the design of IT2 TSK FLCs is developed for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications. Finally, a rigorous mathematical analysis of the robustness of FLSs (both T1 and IT2) is presented in the thesis and entails a formulation of the robustness of FLSs as a constraint multi-objective optimization problem. Consequently, a procedure is proposed for the design of robust IT2 FLSs. Several examples are presented to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The rigorous methodologies presented in this thesis lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing IT2 FLCSs. In addition, the proposed control technique for tracking purposes of MRRs will provide control engineers with tools to control dynamic systems with uncertainty and changing parameters. Finally, the systematic approach developed for the analysis and design of robust T1 and IT2 FLSs is of great practical value in various modeling and control applications

Synchronous control of double-containers for overhead crane

The development and wide application of double spreaders overhead cranes have effectively improved the loading and unloading efficiency of the container terminals. However, due to the nonlinear time-varying characteristics and parameter perturbation of the lifting device of the double spreaders, the difficulty of synchronous and coordinated control of the double spreader overhead crane is increased. In order to solve the problem of synchronous control of double spreaders overhead cranes, this work establishes the mathematical model of the double spreaders overhead crane and proposes two main methods. The controller based on the fuzzy sliding mode method is established. Fuzzy logic control can effective estimate the parameters of the system, reduce the chattering of sliding mode control, and improve the performance of its control. Mean deviation coupling synchronization control combined with sliding mode control can effectively control the speed error between the two spreaders, so that they can keep working synchronously. The other controller is established which use fast non-singular terminal sliding mode control to ensure that the system can converge in a finite time. The combination of terminal sliding mode control and super twisting algorithm can enhance the stability of the system.O desenvolvimento e a vasta aplicação de pontes rolantes de duplo espalhamento tem melhorado a eficiência de carga e descarga dos terminais de contentores. No entanto devido ao facto das variações não lineares do tempo e a perturbação dos parâmetros do dispositivo de elevação de duplo espalhamento, é dificultado o controlo sincronizado e coordenado. Com o objetivo de resolver o problema do controlo síncrono das pontes rolantes de duplo espalhamento, este projeto usa o modelo matemático do guindaste de dupla propagação e propõe dois métodos de resolução. O controlo baseado no método do modo deslizante difuso. O controlo lógico difuso pode estimar eficazmente os parâmetros do sistema, reduzir a vibração do controlo do modo deslizante e melhorar o seu desempenho. O control de sincronização do acoplamento do desvio médio, combinado com o control do modo deslizante que pode controlar eficazmente o erro de velocidade entre os dois espalhadores, para que o seu trabalho possa continuar de forma síncrona. O outro controlador usa um controlo rápido e não singular do modo de deslizamento do terminal para garantir que o sistema possa convergir num tempo limitado. A combinação do control no modo deslizante do terminal e do algoritmo de super rotação pode melhorar a estabilidade do sistema

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible

Design and implementation of a soft computing-based controller for a complex mechanical system

Soft-Computing basierende Regler beinhalten Algorithmen, die im Bereich des Maschinellen Lernens einzuordnen sind. Diese Regler sind in der Lage eine geeignete Steuerungsstrategie durch direkte Interaktion mit einer dynamischen Regelstrecke zu entwerfen. Sowohl klassische als auch moderne Reglerentwurfsmethoden hangen von der Genauigkeit des verwendeten dynamischen Systemmodells ab, was insbesondere bei steigender Komplexitat des Systems und auftretenden Modellunsicherheiten nicht mehr uneingeschrankt gewahrleistet werden kann. Die Ziele von Soft- Computing basierenden Reglern sind die Verbesserung der Gute des Regelverhaltens und eine geeignete Anpassung der Regler ohne eine mathematische Modellbildung auf Grundlage von physikalischen Gesetzen. Im Rahmen dieser Arbeit werden funf Algorithmen zur Modellbildung und Regelung dynamischer Systeme untersucht, welche auf dem Mehrschichten-Perzeptron-Netzwerk (Multi-Layer Perceptron network, MLP), auf der Methode der Support Vector Machine (SVM), der Gau-Prozesse, der radialen Basisfunktionen (Radial Basis Functions, RBF) sowie der Fuzzy-Inferenz-Systeme basieren. Im Anschluss an die Darstellung der zugrunde liegenden mathematischen Zusammenhange dieser Methoden sowie deren Hauptanwendungsfelder im Bereich der Modellbildung und Regelung dynamischer Systeme wird eine systematische Evaluierung der funf Methoden diskutiert. Anhand der Verwendung quantitativer Gutekennziern werden diese Methoden fur die Verwendung in der Modellbildung und Regelung dynamischer Systeme vergleichbar gegenubergestellt. Basierend auf den Ergebnissen der Evaluierung wird der SVM-basierte Algorithmus als Kernalgorithmus des Soft-Computing basierenden Reglers verwendet. Der vorgestellte Regler besteht aus zwei Hauptteilen, wobei der erste Teil aus einer Modellfunktion der dynamischen Regelstrecke und einem SVM-basierten Beobachter besteht, und der zweite Teil basierend auf dem Systemmodell eine geeignete Regelstrategie generiert. Die Verikation des SVM-basierten Regleralgorithmus erfolgt anhand eines FEM-Modells eines dynamischen elastischen Balken bzw. einseitig eingespannten elastischen Balkens. Dieses Modell kann z. B. als Ersatzmodell fur das mechanische Verhalten eines exiblen Roboterarms oder einer Flugzeugtrag ache verwendet werden. Der Hauptteil der Modellfunktion besteht aus einem automatischen Systemidentikationsalgorithmus, der auch die Integration eines systematischen Modellbildungsansatzes fur dynamische Systeme ermoglicht.Die Ergebnisse des SVM-basierten Beobachter zeigen ahnliches Verhalten zum Kalman- Bucy Beobachter. Auch die Sensitivitatsanalyse der Parameter zeigt eine bessere Gute der SVM-basierten Beobachter im Vergleich mit den Kalman-Bucy Beobachtern. Im Anschluss wird der SVM-basierte Regler zur Schwingungsregelung des Kragtragers verwendet. Hierbei werden vergleichbare Ergebnisse zum LQR-Regler erzielt. Eine experimentelle Validierung des SVM basierten Reglers erfolgt an Versuchsst anden eines elastischen Biegebalkens sowie eines invertierten Biegebalkens. Die Zustandsbeobachtung fuhrt zu vergleichbaren Ergebnissen verglichen mit einem Kalman-Bucy Beobachter. Auch die Modellbildung des elastischen Balkens fuhrt zu guten Ubereinstimmungen. Die Regelgute des Soft-Computing basierenden Reglers wurde am Versuchsstand des invertierten Biegebalkens experimentell erprobt. Es wird deutlich, dass Ergebnisse im Rahmen der erforderlichen Vorgaben erzielt werden konnen.The focus of this thesis is to obtain a soft computing-based controller for complex mechanical system. soft computing based controllers are based on machine learning algorithm that able to develop suitable control strategies by direct interaction with targeted dynamic systems. Classical and modern control design methods depend on the accuracy of the system dynamic model which cannot be achieved due to the dynamic system complexity and modeling uncertainties. A soft computing-based controller aims to improve the performance of the close loop system and to give the controller adaptation ability as well as to reduce the need for mathematical modeling based on physical laws. In this work ve dierent softcomputing algorithms used in the eld of modeling and controlling dynamic systems are investigated.These algorithms are Multi-Layer Perceptron(MLP) network, Support Vector Machine (SVM),Gaussian process, Radial Basis Function (RBF), and Fuzzy Inference System (FIS). The basic mathematical description of each algorithm is given. Additionally, the most recent applications in modeling and controlling of dynamic system are summarized. A systematic evaluation of the ve algorithms is proposed. The goal of the evaluation is to provide quantitative measure of the performance of soft computing algorithms when used in modeling and controlling a dynamic system. Based on the evaluation, the SVM algorithm is selected as the core learning algorithm for the soft computing based controller. The controller has two main units. The rst unit has two functions of modeling dynamic system and obtaining a SVM-based observer. The second unit is in charge of generating suitable control strategy based on the dynamic model obtained. The verication of the controller using SVM algorithm is done using an elastic cantilever beam modeled using Finite Element Method (FEM). An elastic cantilever beam can be considered as a representation of exible single-link manipulator or aircraft wing. In the core of the modeling unit, an automatic system identication algorithm which allows a systematic modeling approach of dynamic systems is implemented. The results show that the system dynamic model using SVM algorithm is accurate with respect to the FEM model. As for the SVM-based observer the results show that it has good estimation in comparison with to dierent Kalman-Bucy observers. The sensitivity to parameters variations analysis shows that the SVM-based observer has better performance than Kalman-Bucy observer. The SVM based controller is used to control the vibration of the cantilever beam; the results show that the model reference controller using SVM has a similar performance to LQR controller. The validation of the controller using SVM algorithm is carried out using the elastic cantilever beam test rig and the inverted cantilever beam test rig. The states estimation using SVM-based observer of the elastic cantilever beam test rig is successful and accurate compared to a Kalman-Bucy observer. Modeling of the elastic cantilever beam using the SVM algorithm shows good accuracy. The performance of controller is tested on the inverted cantilever beam test rig. The results show that required performance objective can be realized using this control strategy

A Framework for Life Cycle Cost Estimation of a Product Family at the Early Stage of Product Development

A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible.EThOS - Electronic Theses Online ServiceGBUnited Kingdo