This brief presents an adaptive neural zeta-backstepping control strategy for a class of uncertain nonlinear systems, which allows these systems to be practically stabilized with predefined damping ratios. By introducing the zeta-backstepping technique, system damping ratios can be predetermined based on specific parameter selection rules. To reduce the impact of unknown nonlinearities, neural networks (NNs) with gradient descent training are applied to compensate such nonlinearities online. A new filter, called dynamic command filter, is used to construct the gradient of the NNs. By resorting to second-order Lyapunov stability criteria, it is proved that the closed-loop system is practically stable and has predefined damping ratio. Finally, experiments on a perturbed direct current (DC) motor system demonstrate the advantages of the proposed method.

Active Data Technology in Virtual Machines with Dynamic Command System

Real-time trajectory generation for dual-stage feed drive systems

Two axis servo turntable could be configured with different functional equipment and instruments to achieve a variety of task applications. It is affected by uncertain and nonlinear factors such as friction, load disturbance and ambient temperature. The control of two axis servo turntable was difficult to establish accurate mathematical model and load disturbance, which brings trouble to application of accurate control. Based on the dynamic modeling and servo control modeling of two axis servo turntable, an active disturbance rejection control which does not depend on the accurate mathematical model of two axis servo turntable was proposed, and active disturbance rejection controller was designed. Two axis servo turntable was simulated under the typical dynamic command input and simulated disturbance environment. The simulation test characteristics of classical PID control and active disturbance rejection control were compared and analyzed. The simulation results show that active disturbance rejection control has better accuracy and adaptability under the same excitation and simulated disturbance environment, and has practical application value.

In three-phase three-wire (3P3W) voltage-source converter (VSC) systems, utilization of filter inductors with deep saturation characteristics is often advantageous due to the improved size, cost, and efficiency. However, with the use of conventional synchronous frame current control methods, the inductor saturation results in significant dynamic performance loss and poor steady-state current waveform quality. This article proposes an inverse dynamic model-based compensation (IDMBC) method to overcome these performance issues. For this purpose, two-phase exact modeling of the 3P3W VSC control system is obtained. Based on the modeling, the inverse system dynamic model of the nonlinear system is obtained and employed such that the nonlinear plant is converted to a virtual linear inductor system for linear current regulators to perform satisfactorily. Further, to control phase currents in the synchronous frame, a two-phase coordinate transformation is proposed. The IDMBC method is tested via dynamic command response and waveform quality simulations and experiments that employ saturable inductors reaching down from full inductance at zero current to 1/9th inductance at full current. The results obtained demonstrate the suitability of the method for 3P3W VSCs employing saturable inductors.

In this study, inconspicuous visual stimuli with hidden targets are considered as a new paradigm for reactive brain-computer interface (BCI) applications suitable for actuating a motorized system with a dynamic command from user. To drive the motor's control signal level as close as possible to the targeted dynamic command via wireless transmission, an embeddable intelligent control scheme is introduced to improve the overall electroencephalography (EEG)-based decoding strategy of the BCI system. The proposed technique which can induce motivated attention only requires a single EEG channel, and the intelligent control scheme is constructed with a decoder consisting of a multilayer perceptron (MLP) neural network and a recursive digital Boxcar filter to suppress the influence of noise and ocular artifacts that are typically caused by user's involuntary movements. Results from thirty subjects showed that the predictive ability of the BCI system was significantly improved via the proposed decoder compared to the performance of MLP models alone and those with low pass and Kalman filters which were two existing methods commonly used to alleviate the aforementioned perturbations in real-time applications. The BCI's predictive ability could also be further enhanced by selecting a suitable stimulus to construct a generic MLP model for each gender due to notable performance disparities between male and female groups. The findings of this study will have the potential to increase the degree of freedom in reactive BCI applications particularly when embedded control systems with multiple actuator speeds or accelerations are desired such as those used to control mobile robotics.

A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuator with different speed profiles. In this study, a combination of an inconspicuous visual stimulus and voluntary eyeblinks along with a machine learning-based decoder is considered as a new reactive BCI paradigm to increase the degree of freedom and minimize mismatches between the intended dynamic command and transmitted control signal. The proposed decoder is constructed based on Gaussian Process model (GPM) which is a nonparametric Bayesian approach that has the advantages of being able to operate on small datasets and providing measurements of uncertainty on predictions. To evaluate the effectiveness of the proposed method, the GPM is compared against other competitive techniques which include k-Nearest Neighbors, linear discriminant analysis, support vector machine, ensemble learning and neural network. Results demonstrate that a significant improvement can be achieved via the GPM approach with average accuracy reaching over 96% and mean absolute error of no greater than 0.8 cm/s. In addition, the analysis reveals that while the performances of other existing methods deteriorate with a certain type of stimulus due to signal drifts resulting from the voluntary eyeblinks, the proposed GPM exhibits consistent performance across all stimuli considered, thereby manifesting its generalization capability and making it a more suitable option for dynamic commands with a single-channel EEG-controlled actuator.

According to the actual needs of the emergency rescue after the explosion accident in the central city, this paper puts forward the design ideas and functional objectives of the military civilian joint emergency rescue command and decision system, and puts forward the structure, operation mechanism and functional modules of the system, which includes eight modules: comprehensive query, space positioning, path analysis, plan management, decision support, command scheduling, decision evaluation, training and performance practice. The advantages of the system include: flexible data management function, powerful spatial analysis and planning function, dynamic command and decision-making and scheduling function, and comprehensive training and drilling function. Under the condition of information, the system can provide a basis for decision makers to make emergency rescue decisions after the explosion accident in the central city, and can greatly improve the efficiency of emergency rescue.

Intelligent adaptive automation: A framework for an activity-driven and user-centered building automation

An airbreathing hypersonic vehicle(AHV) generally adopts a scramjet engine as its propulsion, which needs strict conditions of flight dynamic pressure. A new dynamic pressure control system is presented, in which the AHV autopilot directly uses dynamic pressure to track the dynamic pressure command. Firstly, the dynamic pressure model of the AHV is established and the state equations of dynamic pressure control are given. Then, the dynamic pressure control system is implemented using the LQR optimal control theory. The dynamic pressure error is augmented in order to add an integral control to track the dynamic pressure with zero steady error. The structure of the dynamic pressure control system is also obtained. The simulation results show that the dynamic pressure control system has a good performance for guaranteeing the dynamic pressure of the scramjet engine with the disturbance of drag and thrust considered.

Clamping force control system is essential for clamping tasks that require high precision. In this paper, Active Disturbance Rejection Controller (ADRC) is applied for clamping force control system, aiming to achieve higher control precision. Furthermore, the CPSO-ADRC system is proposed and implemented by optimizing the critical parameters of ordinary ADRC using chaos particle swarm optimization (CPSO) algorithm. To verify the effectiveness of CPSO-ADRC, Particle Swarm Optimization- (PSO-) ADRC is introduced as a comparison. The simulation results show that the CPSO-ADRC can effectively improve the control quality with faster dynamic response and better command tracking performance compared to ordinary ADRC and PSO-ADRC.

Tracking Control of a Monolithic Piezoelectric Nanopositioning Stage using an Integrated Sensor.

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition.

Memory controller design is challenging as mixed time-criticality embedded systems feature an increasing diversity of real-time (RT) and non-real-time (NRT) applications with variable transaction sizes. To satisfy the requirements of the applications, tight bounds on the worst-case response time (WCRT) of memory transactions must be provided to RT applications, while the lowest possible average response time must be given to the remaining applications. Existing real-time memory controllers cannot efficiently achieve this goal as they either bound the WCRT by sacrificing the average response time, or cannot efficiently support variable transaction sizes. In this article, we propose to use dynamic command scheduling, which is capable of efficiently dealing with transactions with variable sizes. The three main contributions of this article are: (1) a memory controller architecture consisting of a front-end and a back-end, where the former uses a TDM arbiter with a new work-conserving policy and the latter has a dynamic command scheduling algorithm that is independent of the front-end, (2) a formalization of the timings of the memory transactions for the proposed algorithm and architecture, and (3) an analysis of WCRT for transactions to capture the behavior of both the front-end and the back-end. This WCRT analysis supports variable transaction sizes and different degrees of bank parallelism. The critical part of the WCRT is the worst-case execution time (WCET) of a transaction, which is the time spent on command scheduling in the back-end. The WCET is bounded by two techniques applied to both fixed and variable transaction sizes, respectively. We experimentally evaluate the proposed memory controller and compare to an existing semi-static approach. The results demonstrate that dynamic command scheduling significantly outperforms the semi-static approach in the average case, while it performs equally well or better in the worst-case with only a few exceptions. The former reduces the average response time for NRT applications, and the latter pertains the WCRT for RT applications.

Next generation sequencing (NGS) systems produce vast quantities of data that require substantial computational resources for typical analysis tasks. In addition, data that are generated by different NGS systems are not homogeneous. Moreover, there are an overwhelming number of tools available for performing typical tasks. Managing NGS workflows involves writing custom scripts that quickly grow in complexity, often resulting in unwieldy workflows that underutilize typical high performance compute resources, and increase the demands of the staff managing these workflows. We present Node-Oriented Workflow (NOW), a dynamic command template workflow engine for high performance distributed computing (HPC) systems. Our system provides a simple-to-use browserbased front end for designing and managing complex workflows. Workflows are configured using a simple browser interface, and are managed by the integrated job engine, which initializes nodes, monitors node status, and processes results of individual jobs across nodes in an HPC configuration. We reduce excessive messaging across nodes by placing the burden on nodes to start tasks in a workflow when dependencies are met, i.e., node oriented workflow. Our system was designed for NGS processing in the clinical research setting, emphasizing user simplicity, tool scalability, minimization of redundancy in workflows, while maximizing throughput in an HPC environment. Furthermore, NOW is not restricted to NGS pipeline management, but can used to manage any computational pipeline.

This paper combines the management and prevention of geological disasters through network technology to design the management and prevention platform of geological disasters in Jiangxi Province, which achieves the electronization of the organizational structure and workflow of geological disasters prevention and control on the Internet. This platform breaks the limitations of time, space and departmental separation and promotes geological disaster prevention and management to the level of continuous accumulation, scientific management and rational utilization. Also, Geological disaster prevention and control enters a healthy circulation of dynamic assessment, rapid response, remote consultation and emergency command. The system provides all-rounded high quality, transparent and efficient Web service of geological disaster Alerting, prediction, prevention and management information, achieving the purpose of improving the efficiency and quality of geological disaster prevention and management.

Aiming at the movement characteristics of trajectory correction projectile, the characteristics, installation configuration and error processing algorithm of gyroscope free strapdown equipment were analyzed for gyroscope free strapdown inertial navigation measurement unit(GFSINMU) by utilizing “current” statistical model combined with the outliers processing method. The results showed that the cumulative errors of GFSINMU can be decreased in a certain range by selecting the installation method of accelerometer and utilizing reasonable error analysis method. Thereby, the measurement accuracy of adjusting GFSINMU can be increased, which can adapt to inertial navigation calculation of high dynamic command correction projectile.

Applying DDDAS Principles to Command, Control and Mission Planning for UAV Swarms

This study examined whether teaming up mitigates individual vulnerability to task interruptions in complex dynamic situations. Omnipresent in everyday multitasking environments, task interruptions are usually detrimental to individual performance. This is particularly crucial in dynamic command and control (C2) safety-critical contexts because of the additional challenge imposed by the continually evolving situation during the interruption. We employed a firefighting microworld to simulate C2 in the context of supervisory control to examine the relative impact of interruptions on participants working in a functional dyad versus operators working alone. Although task interruption was detrimental to participants' efficacy of monitoring resources, the negative impact of interruption was reduced for those working in teams. Teaming up translated into faster resumption time, but only if both teammates were interrupted simultaneously. Interrupting only one team member was associated with increased postinterruption communications and slower resumption time. These findings suggest that in complex dynamic situations working in a small team confers more resistance to task interruption than working alone by virtue of the reduced individual workload typical of teamwork. The benefit of collaborative work seems nevertheless mediated by the coordination and communication overhead associated with teamwork. The present findings have practical implications for operators dealing with unexpected events such as task interruptions in C2 environments.

Genotype and phenotype schemata as models of situation awareness in dynamic command and control teams