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.     

Approximation-based adaptive fault compensation backstepping control of fractional-order nonlinear systems: An output-feedback scheme

An observer-based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional-order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional-order state-observer is extended. Applying fault-tolerant approach in a backstepping design methodology would provide a new fault-tolerant adaptive fuzzy output-feedback approach for fractional-order strict-feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.     

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.     

Axial mixing and mass transfer investigation in a pulsed packed liquid–liquid extraction column using plug flow and axial dispersion models

In this research work, the volumetric overall mass transfer coefficient based on continuous-phase (K_oca) and axial dispersion coefficients of phases (E_c, E_d) in a pilot Pulsed Packed Liquid Extraction Column (PPLEC) have been studied using plug flow model (PFM) and axial dispersion model (ADM). Experiments have been carried out using standard systems of water/acetone/toluene and water/acetone/n-butyl–acetate. Values of K_oca evaluated by ADM are greater than those of PFM by about 20% indicating that the axial mixing lowers the performance of PPLEC. It was found that the drop-size distribution is the main cause of the axial mixing in PPLEC. Increase in dispersed phase flow rate (Q_d), increases all K_oca, E_d and E_c and the minimum values of both E_d and E_c and the maximum values of K_oca are in pulse intensity ranges of 0.8–1 cm/s. Finally, three empirical correlations are proposed for the prediction of these parameters which are in good agreement with the experimental data.     

Low Temperature Hall Effect Investigation of Conducting Polymer-Carbon Nanotubes Composite Network

Polypyrrole (PPy) and polypyrrole-carboxylic functionalized multi wall carbon nanotube composites (PPy/f-MWCNT) were synthesized by in situ chemical oxidative polymerization of pyrrole on the carbon nanotubes (CNTs). The structure of the resulting complex nanotubes was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effects of f-MWCNT concentration on the electrical properties of the resulting composites were studied at temperatures between 100 K and 300 K. The Hall mobility and Hall coefficient of PPy and PPy/f-MWCNT composite samples with different concentrations of f-MWCNT were measured using the van der Pauw technique. The mobility decreased slightly with increasing temperature, while the conductivity was dominated by the gradually increasing carrier density.     

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.     

High temperature creep of polyvinyl chloride

The creep behaviour of polyvinyl chloride (PVC) has been studied in the temperature range 280 to 340° F under constant stress varying from 3.4 to 22.7 p.s.i. It is shown that the steady-state creep rate is an exponential function of stress as suggested by Norton but the exponent decreases with temperature. The activation energy for creep is determined using an activated-state rate process and it is found to be a decreasing function of stress with a higher value at temperatures 320° F and above. It is shown that the time dependent strain can be represented by $$\gamma = \gamma _0 + \dot \gamma _s t + \gamma _T \left[ {1 - \exp \left( { - K\dot \gamma _s t} \right)} \right]$$ where γ ₀ is the instantaneous strain on stressing, \(\dot \gamma _s\) the secondary creep rate, γ _T transient strain, and K is a constant. Scanning electron micrograph studies of the fracture surface and the change in activation energy apparently support the probability of two different deformation mechanisms i.e., domain flow and chain segmental or molecular flow at temperatures below and above 320° F, respectively.     

Recrystallization of Cold Spray-Fabricated CP Titanium Structures

Cold gas dynamic spray (cold spray) is a promising rapid deposition technology in which particles deposit at supersonic velocities. The effect of isothermal annealing on recrystallization and mechanical properties of commercial purity (CP) titanium structures that were directly fabricated through cold spray deposition is studied. The optimized cold spray parameters led to a dense cold spray structure. Results show that annealing improves ductility of the cold-sprayed CP titanium structure. The mechanism for softening is the nucleation and growth of equiaxed grains, which include an ultrafine grain structure. A physical-based model for recrystallization of the cold spray CP titanium is proposed. The results show that recrystallization does not eliminate preferred orientation inherited from the cold spray material.