Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller’s superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure’s characteristics change.

Adaptive synchronization of chaotic systems with hysteresis quantizer input

This paper proposes two new designing methods of adaptive controllers in order to synchronize uncertain nonlinear chaotic systems with input quantization. The hysteresis quantizer, which is a class of sector-bounded quantizers, has been used to quantize the control signal. This can avoid the possible chattering caused by some conventional controllers. Two adaptive robust schemes are proposed to accomplish chaos synchronization of master and slave systems in presence of unknown parameters and uncertainties. The proposed controllers in this paper do not require the restrictive conditions for quantized parameters in contrast to some available control techniques for systems with input quantization. In addition, asymptotic stability of the proposed adaptive controllers is also verified analytically. Finally, the proposed controller is applied to a chaotic gyroscope and also to a Micro-Electro-Mechanical-System to validate its efficiency and robustness.

Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network

This study introduces a novel self-organizing recurrent interval type-2 fuzzy neural network (SRIT2FNN) for the construction of a soft sensor model for a complex chemical process. The proposed SRIT2FNN combines interval type-2 fuzzy logic systems (IT2FLSs) and recurrent neural networks (RNNs) to improve the modeling precision. The Gaussian interval type-2 membership function is used to describe the antecedent part of the SRIT2FNN fuzzy rule, and the consequent part is of the Mamdani type with an interval random number. An adaptive optimal clustering number of fuzzy kernel clustering algorithm based on a Gaussian kernel validity index (GKVI-AOCN-FKCM) is developed to determine the structure of the SRIT2FNN and fuzzy rule antecedent parameters, and the parameter learning of SRIT2FNN used the gradient descent method. Finally, the proposed SRIT2FNN is applied to the soft sensor modeling of ethylene cracking furnace yield in a typical chemical process. Comparisons between the SRIT2FNN and conventional fuzzy neural network (FNN) and interval type-2 fuzzy neural network (IT2FNN) are made via simulation experiments. The results show that the proposed SRIT2FNN performs better than the conventional FNN and IT2FNN.

Robust synchronization of master-slave chaotic systems using approximate model: An experimental study

Robust synchronization of master slave chaotic systems are considered in this work. First an approximate model of the error system is obtained using the ultra-local model concept. Then a Continuous Singular Terminal Sliding-Mode (CSTSM) Controller is designed for the purpose of synchronization. The proposed approach is output feedback-based and uses fixed-time higher order sliding-mode (HOSM) differentiator for state estimation. Numerical simulation and experimental results are given to show the effectiveness of the proposed technique.

The chaotic characteristic of the carbon-monoxide utilization ratio in the blast furnace

In this paper, carbon monoxide utilization ratio (CMUR) is served as a real-time index to evaluate the energy consumption of blast furnace (BF), and the chaotic analysis method is also presented to study the characteristic of CMUR. Firstly, the time series data measured from two representative BFs are adopted as the sample to investigate the characteristics of CMUR. Secondly, the phase space model of CMUR is reconstructed, and two key related parameters of the model are derived as well. Finally, the value of the chaotic attractor's saturated correlative dimensions in the reconstructed phase space of CMUR is obtained. The result shows that the sample time series of these two BFs have chaos property. Furthermore, the development process of CMUR is also proved to be the chaotic process. It provides a solid foundation for us to further study the chaotic predication and control of CMUR, which helps us to better master the variational tendency of CMUR and provides the effective operation guidance for the BF on the spot to reduce the energy consumption in BF.