An adaptive extended fuzzy function state-observer based control with unknown control direction

In this paper, a novel adaptive extended fuzzy function state observer based controller is proposed to control a class of unknown or uncertain nonlinear systems. The controller uses Nussbaum-gain technique from literature to prevent controller singularity with unknown control direction and the controller degree of freedom is increased. A state observer which employs the adaptive extended fuzzy function system to approximate a nonlinear system dynamics and estimates the unmeasurable state. The stability of closed-loop control system are shown using Lyapunov stability criterion and Nussbaum function property. The proposed and conventional fuzzy system based controllers are designed to control an inverted pendulum in simulation and a flexible-joint manipulator in real-time experiment. The integral of absoulte error (IAE) of tracking, integral of squared error (ISE) of tracking and integral of required absolute control signal (IA U) performances are compared in applications. The aim of the paper is not only to improve the tracking performances, but also to implement the adaptive extended fuzzy function based controller to a real-time system and conduct the tracking with unknown control direction

A novel online LS-SVM approach for regression and classification

In this paper, a novel online least squares support vector machine approach is proposed for classification and regression problems. Gaussian kernel function is used due to its strong generalization capability. The contribution of the paper is twofold. As the first novelty, all parameters of the SVM including the kernel width parameter σ are trained simultaneously when a new sample arrives. Unscented Kalman filter is adopted to train the parameters since it avoids the sub-optimal solutions caused by linearization in contrast to extended Kalman filter. The second novelty is the variable size moving window by an intelligent update strategy for the support vector set. This provides that SVM model captures the dynamics of data quickly while not letting it become clumsy due to the big amount of useless or out-of-date support vector data. Simultaneous training of the kernel parameter by unscented Kalman filter and intelligent update of support vector set provide significant performance using small amount of support vector data for both classification and system identification application results. © 201

Adaptive fuzzy terminal sliding-mode observer with experimental applications

In this paper, conventional gradient-descent-based adaptive fuzzy observer is improved by using the terminal sliding-mode theory for a class of nonlinear systems. The improvement is made in two ways: first, the switching term of the sliding-mode approach is added to the state of the observer. Second, the measurement error of the system is designed as the input of the observer instead of measured state. The stability of the observer and boundedness of the parameters are proved using Lyapunov approach. Contributions of the paper are summarized as follows: (i) the robustness and convergence properties of newly proposed observer are improved, (ii) the proposed adaptive fuzzy terminal sliding-mode observer, conventional adaptive fuzzy observer, adaptive neural-network observer, and Euler filtering approaches are compared in terms of their ability to estimate velocities of three real-time experimental systems reliably. The performance of the designed observers is discussed with root mean squared-error criterion where the proposed adaptive fuzzy terminal sliding-mode observer provided much accurate state estimation results than classical observers. © 2015, Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg

Adaptive dynamic neural-network observer design of velocity feedbacks

In this paper, an adaptive dynamic neural-network observer is designed for unknown or uncertain nonlinear systems and utilized to estimate unmeasurable states. The contributions of paper are in twofold. First, using variable learning rate and internally stable neurons, convergence of parameters is guaranteed and overall stable adaptive observer is designed. Second, designed observer is applied to a real-time flexible-link transmission system data with unmodeled dynamics where the velocities could not be estimated using approximate mathematical model. The SPR condition of the adaptive observer has been satisfied via output error filtering. The boundedness of the estimation error and other signals has been shown using Lyapunov stability. The application results are presented to demonstrate the applicability and efficacy of the designed observer. © 2012 IEEE

Gerçek zamanlı sistem tanılama ve takagı-sugeno bulanık gözetleyici temelli uyarlamalı bulanık kontrol

Çevrimiçi sistem tanılama, doğrusal olmayan ayrık zamanlı sistemlerin çevrimiçi denetlenmesinde ve sistem davranışının kestirilmesinde önemli bir yer tutmaktadır. Sistem davranışının gürültü ve bozucu etkiler ile değişmesi, tanılama performansını düşürmekte ve hatta kararsızlığa neden olabilmektedir. Tez çalışmasında, uyarlamalı öğrenme adımı kullanarak güncelleme metodu ile yakınsama kararlılığı sağlanabilen ve tanılama performansı yüksek olan modeller geliştirilmiştir. Önerilen modeller, benzetim ortamında ölçüt sistemlere ve gerçek zamanlı doğrusal olmayan servo sisteme uygulanarak ortalama karesel hata ve en küçük tanımlayıcı uzunluk kıstasları ile performansları karşılaştırılmıştır. Endüstriyel robotların taşıdığı bilinmeyen ve değişken yük, robot kolunun ataletini bozmakta ve dinamiklerinde belirsizliğe neden olmaktadır. Tez çalışmasında tasarlanan Takagi-Sugeno (TS) bulanık gözetleyicisi ile yük ve yükün hızı tahmin edilmektedir. Tahmin edilen değerleri kullanan TS bulanık gözetleyici temelli geri besleme ile doğrusallaştırma kontrol ve uyarlamalı bulanık kontrol metotları tasarlanmıştır. Benzetim ortamında robot koluna ve gerçek zamanlı olarak da esnek bağlantılı mekanik sisteme uygulanarak, uyarlamalı kontrol performansları artırılmıştır. Kontrol performansları ise maksimum mutlak izleme hatası, toplam mutlak izleme hatası ve sonsuz-hal izleme hatasıdır. Tez çalışmasında geliştirilen tanılama, gözetleme ve uyarlamalı kontrol metotlarının kararlılığı, Lyapunov kararlılığı ile analiz edilmiştir

Extended fuzzy function model with stable learning methods for online system identification

WOS: 000287157400005The aim of the online nonlinear system identification is the accurate modeling of the current local input-output behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real-time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete-time systems. The main contributions are to introduce an effective quasi-nonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient-descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input-to-state stability (ISS) approach. Numerical simulations are performed for Box-Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems. Copyright (C) 2010 John Wiley & Sons, Ltd

A New RBF Network Based Sliding-Mode Control of Nonlinear Systems

Abstract—In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network is a one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable convergence behavior is proved by Lyapunov stability approach. The parameter update is succeeded by a new strategy adapted from Levenberg-Marquardth (LM) method. The aim of construction of the proposed RBF network is to combine power of the networks which have different mapping abilities. These networks are auto-regressive exogenous input model, nonlinear static NN model and nonlinear dynamic NN model. To apply the model to control of the nonlinear systems, a known sliding mode control is applied to generate input of the system. From simulations; it is sown that the proposed network is an alternative model for identification and control of nonlinear systems with accurate results. Keywords—RBF network, stable learning rate, sliding mode control, online system identification and control. S I

Fuzzy functions with function expansion model for nonlinear system identification

In this study, the structure of fuzzy functions is improved by function expansion. Unlike fuzzy conventional if-then rules, classical fuzzy function structure includes fuzzy bases and linear inputs. Membership functions of fuzzy bases are set using fuzzy C-means (FCM) algorithm, and the linear parameters are computed using the least-square estimation (LSE). This study has two main contributions. First, conventional “fuzzy functions” structure is powered by the expansion of orthogonal “trigonometric functions” where the approximation capabilities of the fuzzy functions are increased. Second, the widths of the normalized membership functions determined for the fuzzy function model are optimized using the Nelder-Mead simplex algorithm that provides further enhancement on the identification performance. The advantages of the proposed model are shown via offline identification of a benchmark nonlinear system and online identification of two real-time nonlinear systems. © 2016 TSI® Press