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In this paper, an affine Takagi-Sugeno (TS) fuzzy modeling-based observer and controller are proposed for the estimation and control of a chaotic Hindmarsh-Rose (HR) neuronal model. The main contributions are given as follows. 1) First, an affine TS fuzzy model of the HR chaotic neuronal model is constructed using sector nonlinearity-based approach. 2) Based on the constructed TS fuzzy model, a TS fuzzy observer is designed for simultaneous state and parameter estimation of HR neuronal model for unmeasurable state and parameters. 3) In the same way, a novel affine TS fuzzy model-based output feedback control law is designed with observed state and parameters where the exponential stability of the designs are guaranteed by Lyapunov approach. 4) Finally, numerical simulations are conducted to illustrate the observation and stimulation with regular and fast spiking trains and annihilation of the membrane potential. © 2013 IEEE.

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.

In this paper, an adaptive model predictive controller (MPC) with a function approximator is proposed to the control of the uncertain nonlinear systems. The proposed adaptive Sigmoid and Chebyshev neural networks-based MPCs (ANN-MPC and ACN-MPC) compensate the system uncertainty and control the system accurately. Using Lyapunov theory, the closed-loop signals of the linearized dynamics and the uncertainty modeling-based model predictive controller have been proved to be bounded. Accuracy of the ANN-MPC and ACN-MPC has been compared with the Runge–Kutta discretization-based nonlinear MPC on an experimental MIMO three-tank liquid-level system where a functional uncertainty is created on its dynamics. Real-time experimental results demonstrate the effectiveness of the proposed controllers. In addition, due to the faster function approximation capability of Chebyshev polynomial networks, ACN-MPC provided better control performance results. © 2017, The Natural Computing Applications Forum.

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.

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.

In this paper, a discretization-based sliding mode observer (DBSMO) is proposed for the state and parameter estimation of nonlinear systems. In the DBSMO structure, an accurate discretized dynamics are derived for the state and parameter update of the sliding mode observer (SMO) instead of integration-based state update. In this way, faster converging observer dynamics are obtained. The stability properties of the conventional SMO remain the same for DBSMO. In the application presented here, first a real-time DC/DC power converter is designed with a computer interface. Then, to show the enhancement of convergence properties, conventional SMO and DBSMO-based model predictive controllers are designed and applied to control of the DC/DC power converter. Through simulation and experimental results, it is shown that the estimation performance of SMO is greatly improved so that the tracking performance is also increased, which are the main contributions of the paper. © 2018, © The Author(s) 2018.

This paper proposes a novel nonlinear gradient-based observer for synchronization and observer-based control of chaotic systems. The model is based on a Runge-Kutta model of the chaotic system where the evolution of the states or parameters is derived based on the error-square minimization. The stability and convergence conditions of observer and control methods are analyzed using a Lyapunov stability approach. In numerical simulations, the proposed observer and well-known sliding-mode observer are compared for the synchronization of a Lü chaotic system and observer-based stabilization of a Chen chaotic system. The noisy case for synchronization and parameter uncertainty case for stabilization are also considered for both observer-based methods. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Soft sensing technology has still important industrial applications especially for chemical reactors, robotic applications, etc. Therefore, this paper introduces and applies novel Runge-Kutta (RK) discretization-based nonlinear observers for real-time sensing applications of unmeasurable quantities. The contribution of the paper is twofold. First, for reliability and accuracy, the stability of the RK based observers are proven. Second, they have been compared with well-known extended-Luenberger observer, extended-Kalman filter and sliding-mode observer on an experimental system. Experiments have shown that the RK model-based observers have a considerable place among conventional ones with respect to design issues and estimation performance for future soft sensing applications. © 2016 TSI® Press.

The effect of the unmodeled dynamics and unknown disturbances prevent the accurate control of the real-time systems. The designed controllers must undertake the effect of these undesired uncertainties. In this paper, adaptive uncertainty modeling based model predictive controller is proposed for the control of uncertain linear systems. The uncertainty modeling structure uses an artificial neural network with adaptive learning rate for fast approximation. The stability of the proposed adaptive uncertainty modeling based model predictive control (UMPC) is shown using Lyapunov candidate function. Conventional MPC and proposed UMPC are applied to the control of a real-time DC/DC buck power converter. The conventional MPC cannot accurately control the DC/DC converter due to the unknown parameters and unmodeled dynamics. However, the proposed UMPC controller can accurately control the system with modeling the uncertainties in controller dynamics. The proposed controller is promising to control uncertain systems in future applications.

In this paper, the tip displacement of a conducting polymer actuator (CPA), which has an unknown and time-varying dynamics, is controlled using three different model-free adaptive controllers. First, conventional indirect adaptive fuzzy and Chebyshev functional-link network controllers are introduced. Second, a novel hybrid network which combines the capability of conventional fuzzy system and Chebyshev functional-link network is proposed for online identification and control of the CPA. The stability of the proposed controllers and boundedness of the closed-loop signals are proven using Lyapunov stability approach. The designed adaptive controllers are implemented experimentally in real-time. The performances of the controllers in tracking a square and a sine reference are compared in terms of root-mean-squared error (RMSE), required input signal power (ICVP) and settling time ( ts) performances.