Characteristics Of Control Signals Research Articles

Effects of Leading Signals on Metrics of Control Quality Indicators

The paper demonstrates the influence of leading variables on the quality evaluation of the operation of univariate control loops of a multi-loop control system used in a 1000 MW power unit. We analyse the course of the setpoint and process values, the calculated value of the control signal and the disturbances values and leading variables. The variables that determine the operating point of the object are used as leading variables. In the case of a power unit, the main leading variable is the power setpoint at which the unit operates. The power setpoint determines the flow of fuel, air and steam—the values of these variables were also considered in the control qualityassessment. Methods based on the characteristics of control signals and error values (residual) are used in the loop control quality assessment. Basic metrics, such as mean error, mean absolute error and mean squared error, are used to analyse the control error. In addition, statistical measures in the form of higher degree moments are applied using the difference in their estimations. The study indicates that once the leading signal values are taken into account during the assessment, the risk of incorrectly interpreting decreased values for the considered indicators is minimized. It is shown that it is important to consider the values of leading variables because they determine the operating point. Particular attention is paid to changes in the leading values since they affect the process conditions. This paper discusses mechanisms that could allow control quality to be monitored both under stable operating conditions and during operating point changes. It is shown that even relatively simple metrics can give very good results when leading signals are considered.

Process control via random forest classification of profile signals: An application to a tapping process

Abstract Due to technological advancements, many manufacturing processes are now real-time monitored through sensors that provide continuous signals of the process parameters rather than providing simpler point observations of the process response. Signals (profiles) obtained through these sensors can reveal important information about the quality of the process being monitored. In this work, we propose a general predictive control framework for on-line process quality monitoring where data is available in the form of a profile. The proposed framework is an integration of ideas from classical on-line process control and advanced machine learning techniques, namely, Random Forests. The proposed framework has the advantages of being more interpretable compared to other methods found in the literature, and has the flexibility to include several commonly used transformations of the signal as features. In addition, abnormal out of control signal characteristics of the process known from experience by operators can be easily incorporated in the random forest technique. An illustration of the proposed framework applied to the case of a tapping manufacturing process is provided. Model comparison results show a superior performance of the proposed framework over previously proposed monitoring methods for the considered tapping process. From a receiver operating characteristic curve analysis, it was found that an area under the curve (AUC) of 0.923 was achieved by the proposed model compared to an AUC of 0.867 for the Generalized Variance model proposed in the literature.

Enabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks

The recent research trends for achieving ultra-reliable and low-latency communication networks are largely driven by smart manufacturing and industrial Internet-of-Things applications. Such applications are being realized through Tactile Internet that allows users to control remote things and involve the bidirectional transmission of video, audio, and haptic data. However, the end-to-end propagation latency presents a stubborn bottleneck, which can be alleviated by using various artificial intelligence-based application layer and network layer prediction algorithms, e.g., forecasting and preempting haptic feedback transmission. In this paper, we study the experimental data on traffic characteristics of control signals and haptic feedback samples obtained through virtual reality-based human-to-machine teleoperation. Moreover, we propose the installation of edge-intelligence servers between master and slave devices to implement the preemption of haptic feedback from control signals. Harnessing virtual reality-based teleoperation experiments, we further propose a two-stage artificial intelligence-based module for forecasting haptic feedback samples. The first-stage unit is a supervised binary classifier that detects if haptic sample forecasting is necessary and the second-stage unit is a reinforcement learning unit that ensures haptic feedback samples are forecasted accurately when different types of material are present. Furthermore, by evaluating analytical expressions, we show the feasibility of deploying remote human-to-machine teleoperation over fiber backhaul by using our proposed artificial intelligence-based module, even under heavy traffic intensity.

Chua's Circuit: Control and Synchronization

Chaos-based data encryption is one of the most reliable methods used in secure communications. This implies a good control of a chaotic system and a good synchronization between the involved systems. Here, experimental results are shown on the control and synchronization of Chua's circuits. The control of the chaotic circuit was achieved by using the switching method. The influence of the control signal characteristics (amplitude, frequency and shape) on the system's states was also investigated. The synchronization of two similar chaotic circuits was studied, emphasizing the importance of the chaotic state characteristics of the Master system in respect to those of Slave system. It was shown that the synchronization does not depend on the chaotic state type, neither on the dimension (x, y or z) used for synchronization.

Comparing Functional EMG Characteristics Between Zero-Order and First-Order Interface Dynamics

The optimal control scheme for powered prostheses can be determined using simulation experiments, for which an accurate model of prosthesis control is essential. This paper focuses on electromyographic (EMG) control signal characteristics across two different control schemes. We constructed a functional EMG model comprising three EMG signal characteristics-standard deviation, kurtosis, and median power frequency-using data collected under realistic conditions for prosthesis control (closed-loop, dynamic, anisometric contractions). We examined how the model changed when subjects used zero-order or first-order control. Control order had a statistically significant effect on EMG characteristics, but the effect size was small and generally did not exceed inter-subject variability. Therefore, we suggest that this functional EMG model remains valid across different control schemes.

A model of the effects of automatic generation control signal characteristics on energy storage system reliability

This paper presents a model of a distributed energy storage resource and its actuation signal. Many previous studies of distributed energy storage systems have assumed that the system has no energy storage limitations and that the actuation signal has no net or instantaneous energy content. This model includes these conditions so as to enable the design of automatic generation control signals whose characteristics maximize the device-level and system-level reliability of distributed energy storage systems. Analytical and numerical results show that the reliability of the DES device can be characterized using a set of non-dimensional parameters. These device-level reliability results are then translated into system-level reliability. These results are novel in that they can guide the balancing authority in constructing an automatic generator control signal specific to the capabilities of distributed energy resources.

Construction and characteristics of codewords and pulse shapes that satisfy spectral constraints

Constrained sequence codes and pulse shapes are used to control signal characteristics in digital communication systems. This letter outlines a technique to construct codewords and pulse shapes to meet specified spectral constraints. Based on a block-coded representation of the signal, it is shown how symbol values can be evaluated to satisfy the most significant terms of a Fourier series representation of the desired spectral response. Several properties of the resulting signals are given, and examples are presented to confirm the validity of this approach.

Coarticulation of tongue and jaw movements in speech

Coarticulatory patterns in speech movements arise from the interaction of neural control signals with mechanical and geometrical properties of vocal tract structures. By determining those aspects of the biomechanical system that are compensated for in neural signals, it can be learned what the nervous system ‘‘knows’’ abouts its own dynamics. Previous work has shown that coarticulatory patterns in jaw movement may arise as a consequence of muscle properties and dynamics rather than characteristics of the neural code [Ostry, Gribble, and Gracco, J. Neurosci. (1996)]. This finding suggests that the nervous system may take limited account of orofacial dynamics in planning speech movements. However, in the case of interarticulator coarticulation, mechanical interaction among articulators may necessitate a more complex pattern of control. As a first step in assessing the characteristics of control signals that underlie interarticulator patterning, kinematic patterns of coarticulation in the tongue and jaw are reported on. Patterns of individual articulator motion as well as interarticulator spatial and temporal relationships will be described. [Research supported by National Institute of Deafness and Other Communication Disorders Grant No. DC-00594, NSERC (Canada), and FCAR (Quebec).]

Traffic characteristics of control signals in an ISDN switching system

Abstract In an ISDN switching system, traffic characteristics of signalling information messages via the D-channel is investigated according to the network layer protocol described in the CCITT I/Q series recommendations. A simple traffic model for the signalling information messages is proposed and analyzed. Using this model, signalling information traffic via the D-channel can be estimated accurately enough to design a D-channel handler for an ISDN switching system.