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"Data systems"
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Accumulated-state-error-based event-triggered sampling scheme and its application to H∞ control of sampled-data systems
This paper is concerned with event-triggered
H
∞
control of sampled-data systems. Its novelties lie in three aspects: (i) A novel accumulated-state-error-based event-triggered scheme is introduced by comparing the integral of the state error from
t
k
to
t
with the system state sampled at
t
k
. This condition works well due to the fact that the so-called Zeno behaviour does not occur. (ii) A novel Lyapunov functional is constructed to establish a criterion to ensure some certain
H
∞
performance of the closed-loop system. This Lyapunov functional is dependent on the integral of the state error involved in the event-triggered scheme. (iii) Under the event-triggered sampling scheme, suitable state-feedback controllers can be designed rather than be given a priori. Moreover, a self-triggered implementation of the proposed event-triggered sampling scheme is presented as well. Finally, a batch reactor model and an inverted pendulum system are given to demonstrate the effectiveness of the proposed method.
Journal Article
Practical SCADA for Industry
A SCADA system gathers information, such as where a leak on a pipeline has occurred, transfers the information back to a central site, alerting the home station that the leak has occurred, carrying out necessary analysis and control, such as determining if the leak is critical, and displaying the information in a logical and organized fashion.
eBook
Rapid dynamical pattern modeling for sampled-data nonlinear systems using regression filter
This paper investigates a rapid dynamical pattern modeling method for a class of nonlinear sampled-data systems. Firstly, within the nonlinear sampled-data systems framework, the consistency condition is presented based on the approximate discrete-time model. Then, a regression filter-based dynamic learning method is proposed to enhance online learning performance of neural networks (NNs). The exponential convergence of NN estimated weights, which stem from regression filter-based weight update law, is deduced based on two newly derived corollaries. The unknown system nonlinear dynamics are accurately modeled using the dynamic learning method. The modeling knowledge is defined as training patterns which are expressed in a group of constant NNs. Analytical results concerning the pattern recognition condition and recognition time are derived based on the duty ratio, consistency condition, and feature interval. The stored knowledge can be reused for dynamical pattern recognition. The effectiveness of the proposed scheme is illustrated through the simulation results.
Journal Article
Destabilizing effect of Coulomb friction induced limit cycles on sampled-data linear systems
This paper presents a counterintuitive effect of Coulomb friction on the dynamics of sampled-data linear systems. Due to dry friction, the motion becomes sensitive to the initial conditions, and the different possible motion trajectories are separated by limit cycles. This paper shows that the linearly stable behavior of the frictionless case degrades due to the presence of dry friction and how the stable domain of operation decreases. The results are illustrated through the example of a single-degree-of-freedom system model. The presented analysis of the nonlinear system gives insight into the intricate dynamics of sampled-data systems due to the effect of dry friction, and numerical simulations verify the results.
Journal Article
Residue Matching: A Method to Determine Intersample Vibrations in Systems With State Feedback
In this paper, we present a new method to determine the continuous‐time response of sampled‐data systems with uniform sampling, zero‐order hold, and full‐state feedback. In such systems, a continuous‐time plant is controlled using a discrete‐time control law. Traditionally, sampled‐data systems are designed in discrete time, resulting in, given by the nature of this kind of modelling, unmodelled intersample behaviour. We show that the Laplace transform of the otherwise piecewise‐continuous state response can be expressed in closed form that fully represents the intersample dynamics. A practical technique is also provided to decouple individual vibration components and reconstruct response functions in the time domain. The proposed approach is also able to capture intersample vibrations compared to common methods, which may lead to inaccurate results in specific cases. The presented new formulae are derived analytically and verified by simulations through numerical examples and experiments on a DC motor drive. This paper presents a new method for analysing the continuous‐time response of sampled‐data systems, addressing the intersample dynamics often overlooked in traditional discretized approaches. The method includes a closed‐form expression for the Laplace transform of the state response and offers techniques for decoupling vibration components and reconstructing time‐domain response functions. The presented new formulae are derived analytically and verified by simulations and experiments.
Journal Article