H∞ fault-tolerant control for time-varied actuator fault of nonlinear system

This paper studies H_∞ fault-tolerant control for a class of uncertain nonlinear systems subject to time-varied actuator faults. A radial basis function neural network is utilised to approximate the unknown nonlinear functions; an updating rule is designed to estimate on-line time-varied fault of actuator; and the controller with the states feedback and faults estimation is applied to compensate for the effects of fault and minimise H_∞ performance criteria in order to get a desired H_∞ disturbance rejection constraint. Sufficient conditions are derived, which guarantees that the closed-loop system is robustly stable and satisfies the H_∞ performance in both normal and fault cases. In order to reduce computing cost, a simplified algorithm of matrix Riccati inequality is given. A spacecraft model is presented to demonstrate the effectiveness of the proposed methods.     

Distributed consensus observer-based H∞ control for linear systems with sensor and actuator networks

This paper proposed a distributed consensus observer (DCO) based H_∞ control method for a class of linear time-invariant (LTI) continuous systems with a sensor and actuator network (SAN). The communication topology of the SAN under consideration is represented by a directed graph, in which the sensor nodes are not able to acquire all the control inputs applied to the target system from the actuator nodes. To overcome this difficulty, a set of novel DCOs embedded in the sensor nodes and a set of DCO-based controllers embedded in the actuator nodes are initially constructed to estimate and control the state of the target system in a fully distributed way, respectively. The constructed DCOs take full advantage of their consensus property and replace the unavailable control inputs with the approximate ones computed on the basis of the state estimates of the underlying sensor node and its neighboring sensor nodes. Subsequently, a design method of DCO-based H_∞ control is proposed in terms of bilinear matrix inequality (BMI) to ensure that the closed-loop system is exponentially stable while satisfying a prescribed overall H_∞ performance of disturbance attenuation. Moreover, in order to make attenuation level as small as possible, a suboptimal H_∞ control design problem is formulated as a BMI optimization problem, and a modified path-following method is provided for solving this problem by using the existing linear matrix inequality (LMI) optimization techniques. Finally, simulation results demonstrate the effectiveness of the proposed method.     

Intelligent integrated optimization and control system for lead–zinc sintering process

The lead–zinc sintering process (LZSP) is an important step in imperial smelting. This paper presents an intelligent integrated optimization and control system (IIOCS) for the LZSP. The optimization and control scheme has a hierarchical configuration. First, the requirements of process control and the configuration of the IIOCS are described. Then, models for predicting quantity and quality (Q&Q) are established using correlation and mechanism analysis, and are implemented by improved back-propagation neural networks. Based on the models, an integrated algorithm combining c-means clustering, genetic, and chaos approaches is employed to optimize the operating parameters of the process. Finally, the control of the process state is carried out by a distributed control system to control the Q&Q of the product.     

An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks

In this paper, we develop a novel distributed adaptive control architecture for addressing networked multiagent systems subject to stochastic exogenous disturbances with compromised sensor and actuators. Specifically, for a class of linear leader–follower multiagent systems, we develop a new structure of the neighbourhood synchronisation error for the control design protocol of each follower. The proposed control algorithm addresses time-varying multiplicative sensor attacks on the leader state measurements. In addition, the framework addresses time-varying multiplicative actuator attacks on the followers that do not have a communication link with the leader and additive actuator attacks on all follower agents in the network. The proposed adaptive controller guarantees uniform ultimate boundedness of the state tracking error for each agent in a mean-square sense.     

Iterative learning control for hyperbolic distributed parameter systems based on sensor–actuator networks

This paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor–actuator networks which can be described by hyperbolic partial differential equations. A D-type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD-type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor–actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods.     

Reliable H∞ guaranteed cost control for uncertain switched fuzzy stochastic systems with multiple time-varying delays and intermittent actuator and sensor faults

Neural Computing and Applications - In this paper, the issue of delay-dependent passive fault-tolerant control and optimal guaranteed cost control is considered for multiple time-varying delayed...     

Being your own role model for improving self‐efficacy: An elementary teacher self‐actualizes through drama‐based science teaching

Abstract:

Many teachers in elementary schools lack school science self‐efficacy, largely because of their inexperience with the subject. This frequently leads them to avoid teaching science or to teach it in ways that compromise the development of aspects of students’ scientific literacy. This paper describes how one teacher was able to improve her school science self‐efficacy through facilitated action research. In response to becoming aware of a discrepancy between her school science practices and her fundamental educational beliefs, Lisa developed a drama‐based, integrated science unit that she judged successful in helping students to achieve relevant learning goals. This experience led Lisa and her students to feel much more positive about teaching and learning in school science. Rather than learning from another, however, “Lisa, the science teacher” learned— to a great extent—from “Lisa, the drama‐based educator.” This finding has implications for science‐phobic teachers and for facilitators of their action research.     

A high‐level data fusion and spatial modelling system for change‐detection analysis using high‐resolution airborne digital sensor data

A high‐level data fusion system that uses Bayesian statistics involving weights‐of‐evidence modelling is described to combine disparate information from airborne digital data such as digital surface model (DSM), colour, thermal infrared (TIR) and hyperspectral images at different time periods. To determine the efficacy of the system, an analysis of change detection was performed. The data fusion system is capable of detecting changes in man‐made features automatically in a densely populated area where there is little prior information. Multiclass segmented images were obtained from the data captured by four airborne remote sensing sensors. The system performs data fusion modelling by using binary images of each theme class and a total of 40 binary patterns were obtained. Through Bayesian methods, involving weights‐of‐evidence modelling, all the binary images were analysed and finally four binary patterns (indicator images) were identified automatically as significant for the change‐detection application. A weighted index overlay model available in the system combines these four patterns. Data fusion by weights‐of‐evidence modelling is found to be straightforward and unequivocal for predicting newly transformed locations. The results of the Bayesian method are accurate as the weights are based on statistical analysis. Changes in features such as colour of roofs, parking areas, openland areas, newly built structures, and the presence or absence of vehicles are extracted automatically by using the high‐level data fusion approach. The final predictor image shows the probability of change‐detected areas in a densely populated city in Japan.     

Predefined-time control for Euler–Lagrange systems with model uncertainties and actuator faults

A novel adaptive predefined-time tracking control algorithm is proposed for the Euler–Lagrange systems (ELSs) with model uncertainties and actuator faults. Compared with traditional finite-time and fixed-time studies, the system output tracking error under the proposed predefined-time controller converges to a small neighborhood of zero in finite time, whose upper bound is exactly a design parameter in the control algorithm. For the uncertain model, radial-based function neural network (RBFNN) is utilized to approximate the continuous uncertain dynamics. To deal with the actuator faults, an adaptive control law is involved in the fault-tolerant controller. In order to achieve the predefined-time bounded, a novel predefined-time sliding mode surface is designed. It is proved that the tracking error vector trajectory of closed-loop system is semi-globally uniformly ultimately predefined-time bounded, and the upper bounds of both the system settling time and the corresponding output tracking error can be adjusted with a simple parameter. Simulation examples finally demonstrate the effectiveness of the proposed control algorithm.     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.     

A μ-H∞ control strategy for decreasing torque fluctuation of electro-hydraulic integrated braking system in mode switching

Due to the difference of time-domain response between hydraulic braking and regenerative braking, as well as changes of equivalent parameters and operating parameters during braking mode switching, it is liable to cause torque fluctuation, which affects braking safety and vehicle ride comfort. First, the uncertainties of vehicle load and frictional coefficient model are investigated. Second, the hybrid system theory is applied to provide state transfer condition for mode switching strategy. Finally, the control strategy that utilizes regenerative braking torque to compensate for difference of the required braking torque is designed, and a new μ-H_∞ control algorithm through D-K iteration is presented to improve the robust performance. The proposed μ-H_∞ control strategy is examined under various braking situations, and the results indicate that (1) the μ-H_∞ controller have the advantage of robustness performance, the amplitude of regenerative braking is decreased by 6.14%, and the steady-state error of hydraulic braking is decreased by 5.26% over the H_∞, and (2) under the braking mode switching, the designed compensation control strategy has the performance of fast and accurate tracking of the desired torque, and the steady-state error does not exceed 3.5%.     

Performance analysis of an integrated piezoelectric ZnO sensor for detection of head–disk contact

Integrated capability for detection of head–disk contact is desired for magnetic sliders with near-contact flying height. At the same time, fabrication of added features should be compatible with the existing slider micromachining process which is highly specialized and cost sensitive. Aimed at meeting the two requirements, a novel sensor configuration is explored in the present study. The new sensor configuration consists of a piezoelectric zinc oxide (ZnO) thin-film sensor sandwiched in the magnetic slider on its trailing side. Coupled structural and piezoelectric finite-element analysis for a sensor–slider–suspension assembly was performed to investigate the dynamic sensing performance. Output voltages on the millivolt level were obtained under typical head–disk interactions. The second in-plane bending mode of the slider was found to be the major contributor to the output voltage. Parametric study further showed that a thicker ZnO layer generally generated a larger output, while the thickness of the slider overcoat had only minimal effect. Simulation results from harmonic and transient analyses demonstrated that the piezoelectric thin-film ZnO sensor was sufficiently sensitive for detection of head–disk contact.     

Multi‐sensor data fusion and comparison of total column ozone

With many remote‐sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution—as well as format, structure, and temporal and spatial coverage—make the data merging, or fusion, difficult. NASA Goddard Earth Sciences Data and Information Services Center (GES‐DISC) has archived several data products for various sensors in different formats, structures, and multi‐temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/fusing several NASA Earth Observing Systems (EOS) remote‐sensing datasets available at GES‐DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES‐DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer (MODIS) Terra and Aqua Level‐3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation (OI), also was conducted and found to produce better results. Ozone Monitoring Instrument (OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder (AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high (0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/Aqua fused total column ozone is 0.79.     

Design of hybrid type’s optical pickup actuator for system stability in high density reading and recording

Recently, there has been a trend to have large capacity and high data transfer rate in optical disk drive. Pickup actuator also should have high performance for satisfying this trend. Since moving magnet type actuator has more simple and robust structure than moving coil type one, we designed moving magnet type to accomplish high flexible mode frequency. However, the driving sensitivity of moving magnet type actuator was low because of its characteristics as following. At first, moving parts including magnets are heavy and electromagnetic (EM) circuits are not closed-loop owing to yoke composed of paramagnetic materials. Therefore, in order to increase the driving sensitivity without the degradation of the other dynamic characteristics, EM circuits were redesigned by adding solenoid coils on existing mechanism. As a result, the hybrid type actuator composed of moving magnet type and moving coil type was suggested to obtain high sensitivities and high flexible mode frequency. Design variables of EM circuits and structure parts were decided through parametric study and design of experiments procedure. Optimization algorithm using variable metric method was used to improve performance. Based on these results, the final model was presented.     

Studies on improving vehicle handling and lane keeping performance of closed-loop driver–vehicle system with integrated chassis control

This study proposes a new integrated robust model matching chassis controller to improve vehicle handling performance and lane keep ability. The design framework of the H_∞ controller is based on linear matrix inequalities (LMIs), which integrates active rear wheel steering control, longitudinal force compensation and active yaw moment control. To comprehensively evaluate the performance of the integrated chassis control system, a closed-loop driver–vehicle system is used. The effectiveness of the integrated controller on handling performance improvement is tested by a vehicle without driver model under a crosswind disturbance. At the same time, both the handling and lane keeping improving performance of the closed-loop driver–vehicle system is evaluated by tracking an S shape winding road. The simulation results reveal that the integrated chassis controller not only achieves preferable handling performance and stability, but also improves the vehicle lane keep ability significantly, and can alleviate the working load of the driver.     

Design and analysis of two-wire focusing actuator for small-sized ODD with linear VCM type’s actuator

Recent advancements in mobile devices have fueled a requirement for information storage systems with characteristics such as subminiature size, low cost, and minimum power consumption. Small optical disk drives could provide a good solution, because their storage media is cheaper than those of hard disk drives or flash memories. In this study, we design and analysis a linear voice coil motor (VCM) types actuator for the coarse and fine tracking motions and a two-wire spring types actuator for a fine focusing motion in order to apply them to small-sized optical disk drives of high data capacity. And, we make the mathematical model of a two-wire spring types actuator and find proper conditions to suppress the tilt motion of the lens holder for various focusing offset positions without sacrificing the sensitivity. Based on them, we design and modify the magnetic circuit and the structural part. Finally, we verify that the designed actuator had the proper dynamic characteristics and that it could be used in small optical disk drives by simulations and experiments.     

The physiological mirror—a system for unconscious control of a virtual environment through physiological activity

This paper introduces a system for real-time physiological measurement, analysis, and metaphorical visualization within a virtual environment (VE). Our goal is to develop a method that allows humans to unconsciously relate to parts of an environment more strongly than to others, purely induced by their own physiological responses to the virtual reality (VR) displays. In particular, we exploit heart rate, respiration, and galvanic skin response in order to control the behavior of virtual characters in the VE. Such unconscious processes may become a useful tool for storytelling or assist guiding participants through a sequence of tasks in order to make the application more interesting, e.g., in rehabilitation. We claim that anchoring of subjective bodily states to a virtual reality (VR) can enhance a person’s sense of realism of the VR and ultimately create a stronger relationship between humans and the VR.