This paper tackles the problem of control for a class of nonlinear systems with state-dependent nonlinearities. A new control algorithm combining Time Delay control and Neural networks is proposed for such systems. As...
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ISBN:
(纸本)9798350328066
This paper tackles the problem of control for a class of nonlinear systems with state-dependent nonlinearities. A new control algorithm combining Time Delay control and Neural networks is proposed for such systems. Assuming all state variables are available, the proposed control algorithm is shown to learn the nonlinearity online, provide closed loop stability and achieve tracking performance better than that of time delay control. The performance of the proposed control algorithm is evaluated and compared to that of Time Delay control and traditional PI control through simulation studies of an Interior type Permanent Magnet Synchronous Motor (IPMSM).
This paper proposes a novel approach to Software-Defined networking (SDN) Admission control (AC) based on Graph Neural networks (GNNs) for Beyond 5G (B5G). AC is a critical function in SDN, as it determines which traf...
ISBN:
(纸本)9798350310900
This paper proposes a novel approach to Software-Defined networking (SDN) Admission control (AC) based on Graph Neural networks (GNNs) for Beyond 5G (B5G). AC is a critical function in SDN, as it determines which traffic flow to pass by the network and which should be rejected. GNNs are a type of Neural networks (NNs) that are able to learn how to make real-time AC decisions by training on pre-existing data, including network topologies and traffic characteristics. The solution we propose is made of two layers: (i) network Delay Predictor (NetDelP) leveraging on the RouteNet-Fermi GNN model, used to predict the network latency for different topologies and traffic patterns. (ii) Admission control Agent (AdConAgt) supporting the SDN and used to regulate the traffic flow in the network. The outlined concept is able to manage large-scale and complex topology networks with optimized Key performance Indicators (KPIs) such as network latency and Packet Loss Rate (PLR). The envisioned approach is evaluated with various network topology scales and classes of traffic. The obtained results outperform the SDN-Shortest Path (SDN-SP) solution by demonstrating the ability of our proposal to guarantee the End-To-End (E2E) latency and prevent link congestion in order to meet the QoS requirements.
In this work, a novel online model-free controller for an underactuated dirigible is developed based on reinforcement learning and optimal control theory. A reinforcement learning structure is used while overcoming th...
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ISBN:
(纸本)9781665491907
In this work, a novel online model-free controller for an underactuated dirigible is developed based on reinforcement learning and optimal control theory. A reinforcement learning structure is used while overcoming the dependence of the value function on future values by introducing a neural network that is adapted using input-output data. The suboptimal critic neural network is structured such that optimality is guaranteed over the interval from which the data is valid. The system performance is validated using a highly realistic physics engine, Gazebo, with the robot operating system (ROS) interface and the results are compared to the performance of a model-based controller specifically designed to control the airship model. It is emphasized that the proposed formulation does not leverage any knowledge of vehicle dynamics and thus is considered a vehicle agnostic control strategy.
Encoder Particle Swarm (EPS) is an innovative approach that combines Autoencoder (AE) and Particle Swarm Optimization (PSO). Research on deep neural network optimization algorithm is applied to precision numerical con...
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The proceedings contain 15 papers. The special focus in this conference is on Ad Hoc networks. The topics include: Two Embedding Algorithms in Schur-Based Image Watermarking Scheme;metaheuristics-Based Hyperparam...
ISBN:
(纸本)9783031559921
The proceedings contain 15 papers. The special focus in this conference is on Ad Hoc networks. The topics include: Two Embedding Algorithms in Schur-Based Image Watermarking Scheme;metaheuristics-Based Hyperparameter Tuning for Convolutional Neural networks;antenna Array Pattern Nulling via Convex Optimization;interference Suppression Approaches Utilizing Phase-Only control and Metaheuristic Algorithms: A Comparative Study;reconfigurable Intelligent Surface-Aided Wireless Communication Considering Interference Suppression;nature-Inspired Algorithms-Based Beamforming for Advanced Antenna systems;Investigation of Transmit Antenna Selection for MU-VASM systems over Correlated Channels;Millimeter Wave Path Loss Modeling for UAV Communications Using Deep Learning;Enhance Secrecy performance of the Cooperative NOMA/UAV network Applying NSGA-ii Algorithm;fake News Detection Based on Multi-view Fuzzy Clustering Algorithm;an Efficient Approach to the k-Strong Barrier Coverage Problem Under the Probabilistic Sensing Model in Wireless Multimedia Sensor networks;an Efficient Method for Solving the Best Coverage Path Problem in Homogeneous Wireless Ad-Hoc Sensor networks;performance of Uplink Ultra Dense network with Antenna Selection.
Tackling the prevalent challenge of unknown model elements and perturbations in practical systems poses a significant barrier to enhancing control *** paper proposes a novel RBF-based Nonlinear MPC for mode compensati...
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ISBN:
(纸本)9798350366907;9789887581581
Tackling the prevalent challenge of unknown model elements and perturbations in practical systems poses a significant barrier to enhancing control *** paper proposes a novel RBF-based Nonlinear MPC for mode compensation. Initially, the conventional approach of dynamic modeling is utilized to identify and isolate unmodeled characteristics. Subsequently, Radial Basis Function (RBF) neural networks are employed to predict and compensate for these unmodeled parts. Driven by the sampled data, this method efficiently explores the control action space to improve controlperformance. Our three-layer neural network architecture significantly reduces computational overhead, and online network updates effectively mitigate neural network generalization issues. We apply the proposed approach to force tracking control of Antagonistic Pneumatic Artificial Muscles (APAM) in flexible structures. Case studies demonstrate a significant improvement in control accuracy compared to the feedforward PID control method.
network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and ot...
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ISBN:
(纸本)9798350386066;9798350386059
network policy plays a crucial role in cloud-native networking, especially in multi-tenant scenarios. It provides precise control over connectivity by specifying source and destination endpoints, traffic types, and other criteria to allow or deny traffic. However, manual configuration of these policies introduces the risk of errors, leading to isolation violations or network service unavailability. Therefore, network policy verification is essential for maintaining security and quality of service in cloud-native networking. Currently, a naive approach involves individually checking each policy within the cluster, which can take over 100s for verification in a cluster size of over 100k. Existing verification frameworks, like Kano and Verikube, improve performance by leveraging pre-filtering and Satisfiability Modulo Theories (SMT) solvers, achieving a 3.12x to 12.99x performance boost over the naive baseline. However, as network policy changes rapidly within 100ms in real cloud-native networks, both frameworks need over 10s to perform verification for cluster sizes over 100k, which is far from satisfying. To overcome these issues, we propose and implement a novel network policy verification framework NPV, which utilizes the policy-label pre-filter process with bitwise compression. We further enhance the policy verification algorithm with a policy-namespace divide-and-conquer strategy to improve the data-level parallelism. We implement NPV on commodity servers and evaluate its performance using real network policy datasets. Our experiments indicate that, compared with the state-of-the-art methods, NPV can achieve up to 139.00x to 651.06x improvement in verification time compared to Kano and Verikube, with 65% less memory usage.
The thermal pyrolysis process of industrial solid waste involves characteristics such as nonlinearity, uncertainty, and high inertia, making traditional control methods difficult to apply effectively. To optimize the ...
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Medium-voltage direct-current (MVDC) shipboard microgrids (SMGs) are the state-of-the-art architecture for onboard power distribution in navy. These systems are considered to be highly dynamic due to high penetration ...
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ISBN:
(纸本)9798350366235;9798350366242
Medium-voltage direct-current (MVDC) shipboard microgrids (SMGs) are the state-of-the-art architecture for onboard power distribution in navy. These systems are considered to be highly dynamic due to high penetration of power electronic converters and volatile load patterns such as pulsed-power load (PPL) and propulsion motors demand variation. Obtaining the dynamic model of an MVDC SMG is a challenging task due to the confidentiality of system components models and uncertainty in the dynamic models through time. In this paper, a dynamic identification framework based on a temporal convolutional neural network (TCN) is developed to learn the system dynamics from measurement data. Different kinds of testing scenarios are implemented, and the testing results show that this approach achieves an exceptional performance and high generalization ability, thus holding substantial promise for development of advanced data-driven control strategies and stability prediction of the system.
With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distrib...
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With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation;while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.
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