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Payload estimators have many parameters, which are trained using the recorded position, velocity and known payload information. To use these payload estimators in the real-time applications, accurate position and velocity information of the system are required. In this paper, first recently proposed sliding-mode observers (SMOs) are designed and compared for velocity estimation of a nonlinear servo system. Second, a parameter estimation based on sliding-mode super-twisting approach is designed to estimate unknown and varying parameter for a class of nonlinear systems. The convergence property of observers is considered using Lyapunov stability method. In the applications, the constant and varying payloads of the servo system have been estimated using the designed method and compared with Extended-Kalman Filter (EKF). In the final section, artificial noises with different SNR are applied to the measurement signal. When the less amplitude of noise signal is applied, second order SMO estimated the states and the payload better than EKF. However, EKF provides much better estimation results than second order SMO for large amplitude of noise signals. For the sake of generalization, second order SMO is a fast and robust observer for small noise cases. In addition, the filtering property of EKF has still importance for large noise cases.

Tüberküloz gibi epidemiyolojik hastalıkları matematiksel modeller üzerinden incelemek hastalığın gelecekteki dinamiklerini yorumlamak için faydalıdır. Bu modellerin varlığında, hastalığı ortadan kaldırabilecek stratejileri hesaplamak mümkün olmaktadır. Bu çalışmada, aktif tüberkülozlu, evde ya da hastanede tedavi edilen bireylerde ölçülemeyen tüberküloz dinamiklerini kontrol etmek için uyarlanabilir kokusuz bir Kalman filtre (UKKF) tabanlı optimal denetleyici tasarlanmıştır. Harici reenfeksiyona bağlı tüberküloz hastalarının evde ve hastanede tedavi görmeleri durumunun incelenmesi, optimal tedavi seçenekleri araştırılmasına olanak sağlamaktadır. Bu sayede küçük bir bulaşıcı insan grubu varlığında bile hastalığın uzun vadede kalıcı olmasının önüne geçileceği düşünülmektedir. Tüberküloz modelinden elde edilen tahmin ve kontrol sonuçlarına göre, tasarlanan uyarlanabilir optimal denetleyicinin, bulaşıcı virüs taşıyan kişilerin eve ya da hastaneye geçişi için doğru kararlar verebilecek nitelikte olduğu kanısına varılmıştır., Examining epidemiological diseases such as tuberculosis through mathematical models is useful for interpreting the future dynamics of the disease. In the presence of these models, it is possible to calculate strategies that can exterminate the disease. In this study, an adaptive unscented Kalman filter (AUKF) -based optimal controller has been designed to control unknown tuberculosis dynamics in individuals treated with active tuberculosis, at home or in hospital. The investigation of the treatment of tuberculosis patients at home and in hospital due to exogenous reinfections helps to search for optimal treatment options. In this way, even in the presence of a small group of infectious people, the long-term persistence of the disease is thought to be prevented. According to the estimation and control results obtained from the tuberculosis model, it was concluded that the designed adaptive optimal controller was able to make the right decision about the transfer of infected persons to the home or to the hospital.

A domestic cookie baking process was modeled using nonlinear forward and inverse models to predict surface temperature, moisture content and browning index that describe the baking quality of the end product. The baking processes were carried out at different oven temperatures (160, 180, 200 °C) and the changes in surface temperature, moisture content and browning index were determined to construct the identification models namely nonlinear polynomial models (PLN) and nonlinear artificial-neural network (ANN) model. The parameters of the artificial models were optimized using least-squares estimation and Levenberg-Marquardt optimization, respectively. The predicted baking characteristics in both forward and inverse phases were in good agreement with the measured ones even for the browning index which was difficult to model because of the its nonminimum-phase dynamics. The application results indicated that the developed intelligient models were very accurate, having low root mean-squared errors, the ANN model approximated the desired values better than the PLN models for all the state variables. Thus, the designed ANN models are applicable for the automatized industrial and domestic oven designs of the future.

Modeling and control of irrigation systems has importance for the yield of the crops and especially, saving the water sources has prominent importance for the world. In this paper, recent modeling and control methods for the irrigation systems are surveyed for the future applications. Even though a mathematical model is constructed for the amount of water in soil, there are too many unpredictable disturbances on the irrigation systems so that the mathematical model cannot be exact. Therefore, the accuracy of the literature control methods is less than expected results. As a solution, the unknown model-based control methods and also accurate prediction methods are going to be promising approaches for irrigation systems in future.

The aim of this paper is to investigate the rehydration properties of freeze- and vacuum-dried damson plums (Prunus insititia) at three different temperatures (25, 45 and 60°C). First, kinetic models (Weibull, Peleg, Exponential and First-order) were designed to construct mathematical models and analyze the rehydration kinetics. Second, an artificial Chebsyhev network was designed for modeling of the rehydration kinetics such that a novel extreme learning machine-based feature extraction layer is proposed to improve its modeling capability. The experimental data and artificial models were analyzed considering the randomly selected data sets, and the root mean squared errors (RMSE) were computed to compare accuracy of the models. Due to orthogonality and feature extraction, the proposed enhanced Chebyshev network was obtained as the best approximator model among tested models with the the lowest RMSE values to explain the rehydration behavior of damson plums. While the percentage RMSE values for kinetic models vary in the range of ~3.1 and 4.8%, the maximum and minimum percentage values for Chebyshev networks are 2.32% and 0.51%, respectively. It is concluded that the proposed Chebyshev network can be used as a parsimonious model in the embedded design of the rehydration and drying machines so that predefined rehydration and drying characteristics can be accurately defined., Bu çalışmanın amacı, dondurarak ve vakumla kurutulmuş mürdüm eriklerinin (Prunus insititia) üç farklı sıcaklıkta (25, 45 ve 60°C) rehidrasyon özelliklerini incelemektir. İlk olarak, kinetik modeller (Weibull, Peleg, Üstel ve Birinci derece) matematiksel modeller oluşturmak ve rehidrasyon kinetiğini analiz etmek için tasarlanmıştır. İkinci olarak, yapay bir Chebsyhev ağı, modelleme kabiliyetini geliştirmek için yeni bir aşırı öğrenme makinesi tabanlı özellik çıkarma katmanı önerilecek şekilde rehidrasyon kinetiğinin modellenmesi için tasarlanmıştır. Deneysel veriler ve yapay modeller, rastgele seçilen veri setleri dikkate alınarak analiz edilmiş ve modellerin doğruluğunu karşılaştırmak için hataların kök ortalama kareleri (RMSE) hesaplanmıştır. Diklik ve öznitelik çıkarımı nedeniyle önerilen geliştirilmiş Chebyshev ağı, mürdüm eriklerinin rehidrasyon davranışını açıklamak için en düşük RMSE değerleri ile test edilen modeller arasında en iyi yaklaşım modeli olarak elde edilmiştir. Kinetik modelleri için yüzde RMSE değerleri ~%3.1 ve 4.8 aralığında değişirken, Chebyshev ağları için maksimum ve minimum yüzde değerleri sırasıyla %2.32 ve %0.51'dir. Önerilen Chebyshev ağının, rehidrasyon ve kurutma makinelerinin gömülü tasarımında cimri bir model olarak kullanılabileceği ve böylece rehidrasyon ve kurutma özelliklerinin önceden doğru bir şekilde tanımlanabileceği sonucuna varılmıştır.

Bu çalışma, boru hattı ağlarının sızıntı tespiti ve izolasyonu için verimli bir gözetleyici tasarımı sunmaktadır. Tasarlanan gözetleyici, dağıtık şekilde bulunan sızıntılara sahip çok katmanlı boru hattı ağlarına uygulanabilir. Birbirinden farklı sızıntıların konumu ve akış hızı verimli bir şekilde tahmin edilebilir. "Ayrıklaştırma Temelli Gözetleyici" olarak adlandırılan tasarlanan gözetleyici sistemin ayrıklaştırılmış modeline dayanmaktadır. Bilgisayar uygulamaları için, 150 metrelik bir boruda farklı konumlara ve akış hızlarına sahip iki sızıntı dikkate alınmıştır. Ölçüm ve kestirimlerden 5 saniye sonra doğru sızıntı tahminleri elde edilmiştir. Literatür gözetleyicileri ile karşılaştırıldığında, tasarlanan gözetleyici tarafından hızlı yakınsama ile eşzamanlı durum ve parametre tahminleri elde edilir., This paper introduces an efficient observer design for leak detection and isolation of pipeline networks. The designed observer can be applied to the multistage pipeline networks with distributed leaks. The position and flow rate of the several leaks can be estimated efficiently. The designed observer is based on the discretized model of the system so called "Discretization Based Observer". For computer applications, two leaks with different positions and flow rates have been considered in a 150 meter pipe. After 5 seconds measurements and estimations, accurate leak estimations have been obtained. Comparing to the literature observers, simultaneous state and parameter estimations are obtained with fast convergence by the designed observer.

Mathematical modelling of the biological processes, diseases and organs are important for the model-based control of diseases. Due to unmodeled dynamics, unknown and external disturbances, the performance of controllers based on these models are degraded for the accurate control. Therefore, robust controllers are need especially for the applications on patients. Inflammation, the cause of many complex biological phenomena and diseases, is a nonlinear process that is difficult to control. In this paper, continuous-time sliding-mode controller has been designed for the control of acute inflammation response (AIR) and antibacterial drug infusion under external disturbances both for septic and aseptic cases. Sliding-mode controller (SMC) is mostly used to control nonlinear systems against external disturbances and parametric uncertainties. Beside the control signal generation, we propose constraints on the control signals based on the clinical experiences such that the applied control signal is suitable for the health and improves the performance of the controller. Due to the multiple equilibrium point on the behavior of the acute inflammation response, it is difficult to design such model-based controllers without input constraints. In the numerical applications, septic death case and aseptic death case with disturbances are controlled and acceptable performances are obtained for future clinical applications.

In this paper, an extremum seeking fault estimation based output feedback controller is proposed for the control of flexible-joint robot manipulator. First, using the approximate nonlinear robot model, a extremum seeking controller is designed to minimize the tracking error via output feedback.Then, in order to prevent the effects of faults, disturbances or unknown dynamics, an extremum seeking based fault estimator is proposed. In order to show that the advantage of the proposed configuration, a flexible-joint manipulator with unknown fault is controlled both in a numerical simulation and real-time experiment. An artificial payload is applied to the end-effector in a simulation environment. But, in the real-time experiment, an additional payload attached to the end effector when it is continuing the process. The approximate model of the robot manipulator is obtained by the state-space identification. As a result using the proposed estimation and controller, acceptable tracking and estimation results are obtained both in numerical and real-time experiments for future applications., Bu çalışmada, ekstremum arama hata tahmini ve ekstremum arama temelli çıkış geri besleme kontrolcüsü doğrusal olmayan esnek-bağlantılı robot kolu için önerilmiştir. İlk olarak, doğrusal olmayan sistemin yaklaşık modeli kullanılarak izleme hatasını minimize etmek için çıkış geribesleme ile ekstremum arama denetleyicisi tasarlanmıştır. Daha sonra hataların, bozucu etkilerin ve bilinmeyen dinamiklerin etkisini yok etmek için ekstremum arama hata tahminleyicisi tasarlanmıştır. Önerilen hata tahminleyici temelli kontrolün avantajını göstermek için bilinmeyen yüke sahip esnek-bağlantılı robot kolu benzetim ortamında ve gerçek zamanlı olarak kontrol edilmiştir. Benzetim ortamında yapay bir yük uygulanmıştır. Fakat gerçek-zamanlı deneyde, esnek bağlantılı robot kolu çalışmaya devam ederken üzerine ek yük bağlanmıştır. Esnek-bağlantılı robot kolunun yaklaşık modeli ise durum uzayı tanılama ile elde edilmiştir. Sonuç olarak önerilen tahminleyici ve kontrolör yöntemi ile gelecek uygulamalar içinde kabul edilebilir izleme ve tahmin sonuçları hem benzetim ortamında hem de gerçek-zamanlı deneylerde elde edilmiştir.

In this paper, a novel adaptive Takagi-Sugeno (TS) fuzzy observer-based controller is proposed. The closed-loop stability and the boundedness of all the signals are proven by Lyapunov stability analysis. The proposed controller is applied to a flexible-transmission experimental setup. The performance for constant payload in the presence of noisy measurements is compared to a controller based on a classical extended Luenberger observer. Simulation and real-time results show that the proposed observer-based feedback controller provides accurate position tracking under constant and varying payloads.

This paper proposes a novel population-based metaheuristic optimization algorithm, called Adolescent Identity Search Algorithm (AISA), which is inspired by the process of identity development/search of adolescents. AISA simulates the identity formation behavior of adolescents in the peer group. This behavior is modeled mathematically to solve optimization problems. The proposed algorithm is evaluated on thirty-nine well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC 2019 benchmark functions to test exploration, exploitation, local optima avoidance, and convergence properties. The results are verified by an extensive comparative study with thirteen state-of-art metaheuristic algorithms. Furthermore, AISA has been used to solve IIR system identification and inverse kinematics problem of a seven Degrees Of Freedom (7-DOF) robot manipulator considered as the real-life engineering applications. The overall optimization results demonstrate that AISA possesses a strong and robust capability to produce superior performance over other competitor metaheuristic algorithms in solving various complex numerical optimization problems. (C) 2020 Elsevier B.V. All rights reserved.

This paper proposes an observer-controller configuration for the well-known Hovarka Glucose-Insulin dynamics. The aim is to control the blood glucose level without measuring the states in the Hovarka model. In order that nonlinear model predictive control is designed to control the blood glucose level by considering the random inertial glucose inputs under external insulin inputs. For the estimation, a Cubature Kalman filter is designed to estimate the states. Numerical computations are conducted under random glucose inputs from the meals where the applied observer-controller configuration provides blood glucose control without measuring the states. As a result, by using a single subcutaneous glucose measurement, the Hovarka glucose-insuling dynamics can be controlled for a possible portable device in future. © 2019 IEEE.

This paper proposes an algorithm to solve the problem of shortest path planning for a mobile robot in a static environment with obstacles. The proposed algorithm is a Hybrid Genetic Algorithm (HGA) which includes Genetic and Dijkstra Algorithms together. The Genetic Algorithm (GA) is preferred since the structure of robot path planning problem is very convenient to apply genetic algorithm’s coding and operators such as permutation coding, crossover and mutation. GA provides diversification while searching possible global solutions, but Dijkstra Algorithm (DA) makes more and more intensification in local solutions. The simulation results show that the mobile robot can plan a set of optimized path with an efficient algorithm.

The simulation of cortical electrical activity of the cerebral cortex is very important for the treatment of neurological disorders involving seizures such as epilepsy, parkinson and etc. It is assumed that the mathematical model of the brain cortex has been exactly modeled. However, the cortex dynamics are highly nonlinear and chaotic under some conditions. The stabilization of seizures becomes difficult when cortex dynamics are unknown or uncertain. This paper presents design and application of the simple adaptive fuzzy control and proportional-integral-derivative (PID) control to the stabilization of the unknown epileptic dynamics. Assuming that all of the system states are not suitable for the measurement where only the system output is available. An adaptive update rule has been designed to allow the adjustment of the controller parameters. Simulation results show that the desired performance for the stabilization of epileptic seizure is achieved via simple controllers. C1 [Cetin, Meric] Pamukkale Univ, Dept Comp Engn, Denizli, Turkey. [Bahtiyar, Bedri] Pamukkale Univ, Dept Elect & Elect Engn, Denizli, Turkey. [Beyhan, Selami] Izmir Democracy Univ, Dept Elect & Elect Engn, Izmir, Turkey.

In this paper, a fuzzy tuning based proportional-integral (PI) and sliding mode (SM) controllers are designed for the synchronization based control of chaotic coronary artery system. The coronary artery system is assumed to be disturbed by an uncertainty function that simulates an unhealthy condition. The synchronization of this unhealthy coronary artery to the healthy coronary artery is achieved by the designed controllers that provide a proper blood feeding to the heart. In fact, PI and SMC are feedback controllers to track a healthy coronary artery system trajectory as in model-reference control. First, PI and SM controller parameters are determined by a trial-and-error approach, however in second case, fuzzy system tuned parameters are also designed to get accurate synchronization based control results. The proposed and designed controllers are applied to improve synchronization performance of coronary artery system. In experiments, the performance of controllers is illustrated and rootmean squared errors (RMSE) are given in Table for a comparison. The numerical applications show better synchronization results and control performances compared to the literature works and fixed parameter cases. © 2018 IEEE.

This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed. © 1963-2012 IEEE.

In this paper, two novel approaches are proposed to improve the performance of online least squares support vector machine for classification problem. First, the parameters of support vector classifier model including kernel width parameter are simultaneously updated when a new sample arrives. In that model, kernel width parameter is a nonlinear term which cannot be estimated via least squares solution. Therefore, unscented Kalman filter is adopted to train all the parameters where Karush–Kuhn–Tucker conditions are satisfied. Second, a variable-size moving window, which is updated by an intelligent strategy, is proposed to construct the support vector set. Thus, the proposed model captures the dynamics of data quickly while precluding itself to become clumsy due to big amount of useless data. In addition, adaptive support vector set provides a lower computational load especially for the large data sets. Simultaneous training of the model parameters by unscented Kalman filter and intelligent update of support vector set provides a superior classification performance compared to the online support vector classification approaches in the literature. © 2017, Springer-Verlag GmbH Germany.

This paper proposes a novel state space least squares support vector machine (SS LS-SVM) for polynomial nonlinear state space (PNLSS) model based recursive system identification. SS LS-SVM, which also possesses an adaptive kernel function, provides an optimum formulation of the monomials (ζ) of the states and input in the PNLSS model. Hence, the PNLSS model encompasses the proposed SS LS-SVM. Recursive nonlinear state space identification is developed in the output error prediction context. The input-output observations are processed sequentially, hence leading to recursive update of the parameters using conventional Gauss-Newton optimization. System states do not need to be measured. However, to to yield a conformal representation of the actual system, number of states need to be known via some physical insight. This characterizes the identification procedure as a grey box one. The PNLSS model is employed in the generalized predictive control (GPC) of a nonlinear continuously stirred tank reactor (CSTR) system. The case which includes additive white noise on the output measurements and a time-varying parameter in the nonlinear system is considered. Numerical applications give the results of a high closed loop identification performance addition to the smooth control input and closely tracking the reference in the GPC scheme. © 2018 IEEE.

Nowadays, mathematical model based estimation and control approaches are frequently consulted and applied for the treatment of such diseases. For the derived dynamics of the diseases, there are some states or internal variables which are very difficult to measure and needs very expensive measurement devices. Therefore, in this paper, adaptive unscented Kalman filter (AUKF) is designed for the state estimation of some vital diseases. These are Human Immunodeficiency Virus (HIV), Hepatitis-B virus (HBV) infection and Cancer such that unmeasurable states are estimated under measurement noises. The computational results show that accurate estimation of the unmeasured states are obtained and plotted for monitoring and control of possible future real-time applications. © 2018 IEEE.

This paper presents a parameter tuned Extremum Seeking Control (ESC) which is utilized for control of antilock breaking system (ABS). Extremum seeking control (ESC) is a purely based on output feedback without the need for a plant model. However, the design challenge of ESC lies in deciding the values of the amplitude of the perturbation signal, the frequency of the perturbation signal, the cut-off frequency of the high-pass filter, the cut-off frequency of the low-pass filter and the integrator gain. In the present paper, the filter parameters are optimized based on the well-known meta-heuristic optimization algorithms such as Jaya Algorithm (JA), Genetic Algorithm (GA), Sine-Cosine Optimization Algorithm (SCA) and Particle Swarm Optimization Algorithm (PSO). The designed ESC controllers are applied to control of antilock breaking system for possible performance comparisons.

The function approximation capability of a regressor model in generalized predictive control (GPC) directly affects the tracking performance of unknown nonlinear systems. In this paper, a novel deep recurrent support vector regressor (DRSVR) is proposed as a function approximator to be adopted in the GPC scheme. This study is an extension of the authors' work [1] to the control task. The DRSVR model has a recurrent state-space structure based on the least-squares support vector regressor (LS-SVR), infinite-impulse response filter (IIR) and adaptive kernel function. The model parameters, including the Gaussian kernel width parameter σ, are updated simultaneously, providing the model to capture the time-varying system dynamics quickly. Parameters are tuned online using error-square minimization via conventional Gauss-Newton optimization while keeping the poles of the IIR filter constrained in the unit circle to maintain stability. The proposed DRSVR based GPC is applied to control nonlinear HIV dynamics. The numerical applications indicate that the proposed regressor model provides high closed loop identification performance in the GPC scheme. Hence, it provides the controller with a significant tracking capability. © 2018 IEEE.