A DC motor is a common actuator in process controlsystems that converts electrical energy into mechanical energy. This paper examined the performance of a deep learning-based neural network predictive controller (NNP...
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The comprehensive performance and user experience of large-scale distributed video conferencesystems are constrained by network traffic, and conventional network services are difficult to ensure the stability and rel...
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This paper is concerned with the adaptive resilient event-triggered control problem for networked switched systems with nonperiodic denial of service (DoS) attacks. In order to save limited network bandwidth and reduc...
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ISBN:
(纸本)9798350387780;9798350387797
This paper is concerned with the adaptive resilient event-triggered control problem for networked switched systems with nonperiodic denial of service (DoS) attacks. In order to save limited network bandwidth and reduce the negative impact on system performance caused by DoS attacks, a novel adaptive resilient event-triggered scheme (ARETS) is developed, which can save the communication cost and improve the flexibility of the system. With considering the ARETS, dwell time switching and transmission delays, the criterion of asymptotic stability and L2-gain performance is derived such that the system under DoS attacks owns security performance. On this basis, the co-design conditions of the ARETS and the controller are proposed. Finally, a simulation example is given to verify the effectiveness and merits of the proposed method.
Advent of new communication technologies made it possible to replace point-to-point control structures with networked systems as a breakthrough toward Industry 4.0. However, overcoming communication imperfections is s...
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ISBN:
(纸本)9798350376357;9798350376340
Advent of new communication technologies made it possible to replace point-to-point control structures with networked systems as a breakthrough toward Industry 4.0. However, overcoming communication imperfections is still a major issue. In this paper, a group of network-based cascade controlsystems is investigated in which, network communication is considered within the controller-actuator channel. In order to reduce conservatism and increase robustness of the control system, time-varying delay and packet dropouts are considered as separate terms using an augmented continuous-time state-space model. Based on Lyapunov theorem, the H-infinity stability conditions are attained dealing with external disturbances. An identified linear model for an industrial gas turbine unit is employed for performance assessment of the designed controllers. Obtained results confirm capability of the proposed method to achieve larger gain margins and more robustness against uncertain delays and data loss.
Many medical scenarios require the accurate and rapid infusion of medical liquids, such as blood, saline, and medications. However, the centralized platforms used in existing infusion systems struggle to balance perfo...
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ISBN:
(纸本)9798400709746
Many medical scenarios require the accurate and rapid infusion of medical liquids, such as blood, saline, and medications. However, the centralized platforms used in existing infusion systems struggle to balance performance and cost, making it difficult to achieve accurate control of liquid flow in these situations. To address this challenge, this paper proposes an accurate-rapid infusion system based on a distributed network. Taking performance, safety, and cost into account, the system utilizes a pressure bag as the main actuator, and a corresponding mathematical model is developed. A distributed measurement & controlnetwork is then constructed to execute complex control algorithms. Unlike centralized platforms, the distributed network can be constructed quickly and flexibly, enabling computility sharing for edge computing. Experimental results demonstrate that the system controls liquid flow accurately and stably in the steady state, with the error mean of all algorithms being less than 9.7 mL/min and the standard deviation (STD) less than 7.9 mL/min. This paper introduces a novel method for future research on rapid infusion systems.
Training stable neural networkcontrollers for closed-loop tracking controlsystems remains a challenging task. Previously, the authors proposed a method to ensure the stability of a neural networkcontrolsystems by ...
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Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers...
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Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers have a strong potential as an approximation framework of predictive control laws as they usually yield better approximation quality and smaller memory requirements than existing explicit (multi -parametric) approaches. In this paper, we first show that neural networks with HardTanh activation functions can exactly represent predictive control laws of linear time -invariant systems. We derive theoretical bounds on the minimum number of hidden layers and neurons that a HardTanh neural network should have to exactly represent a given predictive control law. The choice of HardTanh deep neural networks is particularly suited for linear predictive control laws as they usually require less hidden layers and neurons than deep neural networks with ReLU units for representing exactly continuous piecewise affine (or equivalently min-max) maps. In the second part of the paper we bring the physics of the model and standard optimization techniques into the architecture design, in order to eliminate the disadvantages of the black -box HardTanh learning. More specifically, we design trainable unfolded HardTanh deep architectures for learning linear predictive control laws based on two standard iterative optimization algorithms, i.e., projected gradient descent and accelerated projected gradient descent. We also study the performance of the proposed HardTanh type deep neural networks on a linear model predictive control application.
The UAV flight control tasks, especially their stabilities under large disturbances, are always complex and troublesome. In many application environments, drones are subject to both external interference and internal ...
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ISBN:
(纸本)9798350357899;9798350357882
The UAV flight control tasks, especially their stabilities under large disturbances, are always complex and troublesome. In many application environments, drones are subject to both external interference and internal structural changes, which greatly affects the tracking accuracy. In this paper, an AI-based nonlinear control strategy was presented for unmanned aerial vehicles to achieve accurate tracking tasks. The basic idea is to combine neural network learning with traditional nonlinear model predictive control (NMPC). Moreover, some adaptive control algorithms are adopted for better robustness. First, existing MPC with L1 adaptive control is adopted as main controller for rejecting external disturbances. Second, considering path tracking performance, model reference adaptive control is added to the control policy to compensate internal structural changes. Third, to take advantage of the wealth of flight data, the neural network learning is adopted and the predictive error signal is fed back to the control system. Finally, the simulation studies are given to illustrate the effectiveness of proposed control strategy.
The 5G network's control-plane, a critical component of modern telecommunications infrastructure, manages the signaling and control functions that enable seamless connectivity and service delivery. This paper pres...
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In this study, we selected lightweight AI models such as LeNet, miniVGG-Net, Shallow-Net, AlexNet, MobileNet, and GoogLeNet. These models have been recently applied or considered for CubeSat space missions. The goal o...
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ISBN:
(纸本)9798331517939;9788993215380
In this study, we selected lightweight AI models such as LeNet, miniVGG-Net, Shallow-Net, AlexNet, MobileNet, and GoogLeNet. These models have been recently applied or considered for CubeSat space missions. The goal of this study is to identify SOTA (State-Of-The-Art) models that could be considered for use in implementing autonomous driving in extraterrestrial environments such as the Moon or Mars. The classification performance of these models was analyzed in a categorical classification problem, including label classes such as moving straight, turning right, and turning left. The results showed that the AlexNet model had the highest performance, with an ACC (Accuracy) of 0.9999 and an F-1 score of 0.9999, while the MobileNet model had the lowest performance, with an ACC of 0.8000 and an F-1 score of 0.4572. Consequently, AlexNet and LeNet were selected as the benchmarks for comparing and analyzing the performance of RoverNet-1, the AI for autonomous driving to be developed for future exploration rovers.
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