Robust Absolute Stability Analysis of Multiple Time‐Delay Lur'e Systems With Parametric Uncertainties

The problem of robust absolute stability for time‐delay Lur'e systems with parametric uncertainties is investigated in this paper. The nonlinear part of the Lur'e system is assumed to be both time‐invariant and time‐varying. The structure of uncertainty is a general case that includes norm‐bounded uncertainty. Based on the Lyapunov–Krasovskii stability theory, some delay‐dependent sufficient conditions for the robust absolute stability of the Lur'e system will be derived and expressed in the form of linear matrix inequalities (LMIs). These conditions reduce the conservativeness in computing the upper bound of the maximum allowed delay in many cases. Numerical examples are given to show that the proposed stability criteria are less conservative than those reported in the established literatures.     

Chattering free with noise reduction in sliding-mode observers using frequency domain analysis

This paper presents analysis and regulation of switching in the sliding-mode observers for nonlinear systems. First, the high gain property of the sliding observer is employed in order to obtain fast convergence of the estimated states to the system ones. Then, when the system is in the switching condition, signals are decomposed into two modes: the slow-mode and the fast-mode. The observer parameters are designed based on the relay feedback systems such that the high gain property is provided for the slow-mode operation. This ensures fast convergence of the estimated states and at the same time, by controlling the fast-mode of the observer, the high frequency oscillations (i.e. the chattering phenomena) can be rejected by a simple low-pass filter. In addition, the behavior of the proposed observer is analyzed in the presence of the measurement noise. Moreover, a Variable Relay-Equivalent Gain technique will be introduced to make the proposed observer less sensitive to measurement noises and to maintain good estimation of the states. The proposed nonlinear observer is tested through simulations in an illustrative example involving a bioreactor. Simulation results show good performance of the proposed method as compared to the conventional sliding-mode observer.     

Robust model predictive control of biped robots with adaptive on-line gait generation

International Journal of Control, Automation and Systems - In this paper, an on-line gait control scheme is proposed for the biped robots for walking up and down the stairs. In the proposed...     

Adaptive neural network output feedback stabilization of nonlinear non‐minimum phase systems

This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non‐minimum phase systems. The proposed controller comprises a linear, a neuro‐adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input–output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model. Copyright © 2009 John Wiley & Sons, Ltd.     

Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes

This paper presents an artificial neural network model for detecting and classifying three types of non‐random disturbances referred to as level shift, additive outlier and innovational outlier which are common in autocorrelated processes. To the best of our knowledge, this is the first time that a neural network has been considered for simultaneous detection and classification of such non‐random disturbances. An AR (1) model is considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations are generated over time. The performance of the proposed procedure is evaluated through the use of a numerical example. Preliminary results indicate that the procedure can be used effectively to detect and classify unusual shocks in autocorrelated processes. Copyright © 2003 John Wiley & Sons, Ltd.     

Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold,sensing time and user selection

Wireless Networks - Cooperative spectrum sensing schemes proposed to solve the hidden terminal problem and mitigate multipath fading and shadowing effects, which enhance the sensing performance and...     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.

     

Removal of methylene blue from aqueous solution by electrophoretically deposited titania‐halloysite nanotubes coatings

Two‐component suspensions of titania and halloysite nanotubes (HNTs) were prepared in ethanol with 0.5 g/L (optimum concentration) of polyethyleneimine (PEI) and different wt% of HNTs. Kinetics of Electrophoretic deposition (EPD) decreased with increasing the HNTs content in suspensions due to their less mobility compared with titania particles. HNTs reinforced the microstructure of coatings and reduced or completely prevented from cracking during drying and heat‐treatment steps. Removal of methylene blue (MB) via adsorption by HNTs coatings was faster than its photocatalytic degradation by titania coating. Dispersion of HNTs (up to ≈30 wt%) in the matrix of titania resulted in the synergistic catalytic effect in MB removal. The synergistic effect was because of the shorter traveling distance of MB molecules adsorbed on HNTs toward the photocatalytic active site of titania particles in composite coatings. However, the synergistic effect was destroyed with increasing the HNTs content in coating. Difference between the amount of MB removed by titania and composite coatings increased at longer times (≥60 minutes). Mass transfer of MB adsorbed on HNTs toward the photocatalytic active sites of adjacent titania particles can compensate the decline in the mass transfer from solution at longer times.     

Enhanced adaptive nonlinear extended state observer for pure feedback systems withmatched andmismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.