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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
to a system. The time response of a control system is usually divided into two parts: the transient response and the steady-state response. time shift for a signal x(t) a displacement in time t0 . The time shift is given by x(t - t0 ). time slot in time-division multiple access (TDMA), a time segment during which a designated user transmits, or control information is transmitted. In time-division multiplexing (TDM), each time slot carries bits associated with a particular call, or control information. time stability the degree to which the initial value of resistance is maintained to a stated degree of certainty under stated conditions of use over a stated period of time. Time stability is usually expressed as a percent or parts per million change in resistance per 1000 hours of continuous use. time variant channel a communication channel for which the impulse response and transfer function are functions of time. All practical channels are time-variant, providing the observation interval can be arbitrarily long. See also fading channel. time variant system system in which the parameters vary with time. In practice, most physical systems contain time varying elements. time varying system (1) See time invariant system. (2) a system which not exhibiting time invariance. In particular, one in which the impulse response varies as a function of the time at which the impulse occurs. time-bandwidth product (1) in an acoustooptic deflector, the product of the acoustic-wave propagation time across the optical beam and the electrical bandwidth for optical diffraction; equivalent to the number of independent resolvable
Systems Theory and Optimal Control
Published in Larry W. Mays, Optimal Control of Hydrosystems, 1997
A system is time-invariant if the kernel does not change with time, whereas a system is time-variant if the kernel changes with the time. A system is deterministic if the kernel and inputs are known exactly; a system is stochastic if either the parameters in the kernel or the inputs are not exactly known and are described by statistical concepts. A system is a continuous-time system if the time varies continuously and can take any value in the continuous set of real numbers. A system is a discrete-time system if the inputs, outputs, and the parameters in the kernel take values at discrete times. A system is a lumped-parameter system if the inputs, outputs, and the parameters in the kernel are only functions of time and there is no spatial variable involved in the system. A system is a distributed-parameter system if the inputs, outputs, and the parameters in the kernel are functions of time and space. A continuous-time (discrete-time) lumped-parameter system is described by ordinary differential (difference) equations, whereas a distributed-parameter system is described by partial differential (difference) equations.
Adaptive Control
Published in Alex Martynenko, Andreas Bück, Intelligent Control in Drying, 2018
The basic aim of a feedback control system is to keep a process in a desired mode of operation, which may be a constant set point, a time variant trajectory, or more generally a desired level of performance, in the presence of unknown disturbances and (minor) uncertainties on the process’ characteristics. A feedback control is designed and its parameters are adjusted such that this objective is fulfilled. However, for large unforeseen disturbances (e.g., sudden changes in environmental conditions) and changing process conditions (e.g., time variant behavior of process units resulting from deterioration), initially good performance of a control system can degrade during the process and the desired control goals may no longer be reachable. Moreover, for badly designed controllers, the overall control system may even become unstable. In these cases, the controller parameters have to be changed to meet the objectives under the changed process conditions. This approach is also known as adaptive control and can be thought of as an additional control loop that adjusts the controller parameters. In the following, an overview on the main characteristics and different types of adaptive control are presented. The advantage makes adaptive control algorithms highly relevant for industrial applications. As a result, research on development and application of adaptive control schemes has received high interest in the scientific community for the last decades, which is reflected in the number of excellent publications and textbooks, for example, Åström and Wittenmark (1995), Sastry and Bodson (1989), and Landau et al. (2011), that provide the sound basis for the rather condensed information presented in this chapter and give much more detail on the topics touched upon in this section.
Intelligent vibration control for ultra-high-speed elevators: A type 2 variable universe fuzzy neural network method with input saturation
Published in Mechanics Based Design of Structures and Machines, 2023
Ruijun Zhang, Qinghua Ge, Hao Zhang
The accuracy of the variable universe fuzzy controller is based on the contraction-expansion factors and the accuracy of fuzzy inference. For one thing, considering that the adjustment of the contraction-expansion factor does not have a unified description, even if the approximation is learned in a data-driven method, it can be prone to disturbed by sample noise. For another, due to the influence of random factors such as the passenger mass, station distribution, air flow and pressure, the actual operating parameters of the elevator are highly uncertain, and the traditional type 1 fuzzy partitions and the corresponding fuzzy inference rules cannot accurately reflect the actual responses of inputs and outputs. Therefore, it is necessary to further enhance the fuzziness of the control system to accommodate the uncertainty, time-variant and the ability of anti-interference in the control system.
Transient thermal shock behavior of the graphene-platelets reinforced annular-shape sector plates in the case of fully-clamped and simply-supported boundary conditions
Published in Mechanics of Advanced Materials and Structures, 2023
Peng Cang, Shen Yu, HuiDan Zhang, Zhen Wang
Despite the destructive detriments of the thermal shock loads (TSLs) on the performance of the nanocomposite plates operating as the components of industrial machines, there is no record of analyzing the thermal shock resistance of the annular-shape sector plates reinforced by GPLs in the published literature. In this respect, this study’s authors explored the impact of the major factors affecting on the thermal shock response of the functionally graded graphene-platelets reinforced (FG-GPLR) nanocomposite annular-shaped sector plate resting on the Kerr-type substrate in the course of the exact elasticity theory. To enable the solvability of the system’s equations for the fully-clamped edges’ boundary conditions as well as the simply-supported ones, HDQA is employed. For the first time, the effect of different shapes of thermal shocks in the form of Heaviside, sinusoidal and cosinusoidal functions on the fluctuation of the stress and deflection terms of the system is explored. The convergence of DQA and HDQA with respect to the number of grid nodes are compared to select the solution with the optimal convergence rate, and it is found that HDQA requires a comparatively lower number of grid nodes to present stable results. To find the time variant system’s response, the Laplace transform is operated to map the relations from time frame to Laplace domain. At the end, the modified declaration of Dubner and Abates’ approach is utilized to translate the thermoelastic system’s response from Laplace domain to time frame.
A machine sound monitoring for predictive maintenance focusing on very low frequency band
Published in SICE Journal of Control, Measurement, and System Integration, 2021
Kazuki Tsuji, Shota Imai, Ryota Takao, Tomonori Kimura, Hitoshi Kondo, Yukihiro Kamiya
The analysis of the sounds has been typically performed through the fast Fourier transform (FFT). To cope with time-variant signals, the short-time Fourier transform (STFT) based on FFT is also applied. However, it is well-known that FFT is not suitable for the analysis of low-frequency signals [13]. In order to improve the resolution of the low frequency band, FFT requires a huge number of signal samples. It results in not only a huge amount of memory and computational load but also long time to capture the signal samples.