Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

On the off-design of a natural gas-fired combined cycle with CO2 capture

During the last 15 years cycles with CO₂ capture have been in focus, due to the growing concern over our climate. Often, a natural gas fired combined cycle with a chemical absorption plant for CO₂ capture from the flue gases have been used as a reference in comparisons between cycles. Neither the integration of the steam production for regeneration of amines in the combined cycle nor the off-design behaviour of such a plant has been extensively studied before.     

Quality Management in Pakistan's Bedwear Industry

Pakistan's bedwear industry contributes significantly to Pakistan's industry in general and to its textile industrial output and exports in particular. The exports of bedwear have increased steadily. However, its key basis of competitive advantage has been low cost. After greater liberalization from 2005 onwards and due to tariffication of quotas, the bedwear industry will not only have to scale higher tariff walls but will be more exposed to the threat of anti-dumping duties as it relies primarily on low prices to penetrate the export markets. To enhance its competitiveness, it will have to combine better quality with low costs and quick response. Research in quality management in Pakistan's bedwear industry was, therefore, undertaken. A postal self-completion questionnaire was mailed to 38 members of Pakistan Bedwear Manufacturers' and Exporters' Association (PBMEA). The response rate was almost 79%. It was found that this industrial segment was in various stages of development. This study demonstrates both the effectiveness and the limits of quality assurance in improving the levels of quality which remain a moving target in a dynamic liberalizing international trade environment that is more competitive since 2005. The bedwear industry, therefore, needs to graduate to advanced practices in quality management in order to remain competitive in rapidly changing times.     

Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator failures

In this paper, an adaptive dynamic surface control approach is developed for a class of multi‐input multi‐output nonlinear systems with unknown nonlinearities, bounded time‐varying state delays, and in the presence of time‐varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time‐varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed‐loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

CuIns2/Cus Nanocomposite: Synthesis via Simple Microwave Approach and Investigation Its Behavior in Solar Cell

CuInS₂/CuS nanocomposite were synthesized by a copper complex, [bis(ethylenediamine)copper(ΙΙ)] sulfate. Eight sulfur sources were used for this experiment. The products were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis spectroscopy, and room temperature photoluminescence spectroscopy. Thin film of nanocomposite powder was fabricated and its feature (V_oc, J_sc and FF) was calculated by current–voltage (I–V) curve.     

Solidification crack initiation and propagation in pulsed laser welding of wrought heat treatable aluminium alloy

The effect of preheating on strain rate and liquid flowrate is analysed to develop a model for solidification cracking in successive laser weld spots in 2024 aluminium alloy. It is shown that the lower tendency for solidification cracking with preheating can be attributed to the lower strain rate but not the feeding rate. It is also shown there is a strong tendency for solidification crack propagation through the fusion lines between the consecutive weld spots where there is already a crack present at the fusion line. This observation can be attributed to the higher local strain rate and lower feeding rate.     

Ab initio study of phase stability in doped TiO2

Ab initio density functional theory calculations of the relative stability of the anatase and rutile polymorphs of TiO₂ were carried out using all-electron atomic orbitals methods with local density approximation. The rutile phase exhibited a moderate margin of stability of ~ 3 meV relative to the anatase phase in pristine material. From computational analysis of the formation energies of Si, Al, Fe and F dopants of various charge states across different Fermi level energies in anatase and in rutile, it was found that the cationic dopants are most stable in Ti substitutional lattice positions while formation energy is minimised for F^? doping in interstitial positions. All dopants were found to considerably stabilise anatase relative to the rutile phase, suggesting the anatase to rutile phase transformation is inhibited in such systems with the dopants ranked F?>?Si?>?Fe?>?Al in order of anatase stabilisation strength. Al and Fe dopants were found to act as shallow acceptors with charge compensation achieved through the formation of mobile carriers rather than the formation of anion vacancies.     

Optimising the ice cream formulation using basil seed gum (Ocimum basilicum L.) as a novel stabiliser to deliver improved processing quality

Mixture design was used to determine the optimum ratio as well as concentration of basil seed gum (BSG), guar gum (GG) and carboxymethyl cellulose (CMC) in the formulation of ice cream stabilisers. Predicted equations and contour plots of physicochemical responses were also generated. Generally, increasing the ratio of BSG in gums mixture increased the apparent viscosity of ice cream mixes and decreased the melting rate. Increasing the proportion of GG at concentration of 0.35% enhanced overrun of samples. High ratios of BSG at concentration of 0.35% and CMC at concentration of 0.15% increased the fat destabilisation in ice creams. Combination of 84.31% BSG and 15.69% CMC at concentration of 0.35% proposed as optimum formulation which verified in practice. Introducing BSG as a novel source of stabiliser could be promising as alternative and improve the quality and diversity of ice cream and related products.     

Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface

Recently, neuro-rehabilitation based on brain–computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.