Three-phase Pulse-Width Modulated (PWM) rectifiers used between the power grid and the load in applications requiring DC voltage have features such as high efficiency, high power factor, and low harmonics. This paper ...
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Three-phase Pulse-Width Modulated (PWM) rectifiers used between the power grid and the load in applications requiring DC voltage have features such as high efficiency, high power factor, and low harmonics. This paper proposes a hybrid control approach to improve the dynamic performance of three-phase PWM rectifiers under different operating conditions. Operating conditions are considered as step response, internal disturbance, and regenerative operation. First, Interval Type-2 Fuzzy Neural network (IT2FNN) is designed and then antecedent and consequent parameters of IT2FNN are optimized with Modified Golden Sine Algorithm (GoldSA-ii). The dynamic performance of the hybrid controller, named GoldSA-ii-IT2FNN, is analyzed for all operating conditions in Matlab/Simulink environment. The simulation studies are realized to evaluate the performance of the proposed controller. In the simulations, settling times of proposed controller are observed as 27.2 ms, and 10.8 ms for step response, respectively. Moreover, recovery times are calculated as being 12 ms to 5.5 ms for internal disturbance, and 7.2 ms to 19 ms for regenerative operation, respectively. The obtained results demonstrate that the proposed controller not only provides better dynamic performance but also improves the stability of PWM rectifier.
Zero-touch network management is one of the most ambitious yet strongly required paradigms for beyond 5G and 6G mobile communication systems. Achieving full automation requires a closed loop that combines (i) network ...
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Zero-touch network management is one of the most ambitious yet strongly required paradigms for beyond 5G and 6G mobile communication systems. Achieving full automation requires a closed loop that combines (i) network status data collection and processing, (ii) predictive capabilities based on such data to anticipate upcoming needs, and (iii) effective decision making that best addresses such future needs through proper networkcontrol and orchestration. Recent seminal works have proposed approaches to jointly implement the last two phases above via a single deep learning model trained on past network status to directly optimize future decisions. This is achieved by designing custom loss functions that directly embed the management task objective. Experiments with real-world measurement data have demonstrated that this strategy leads to substantial performance gains across diverse network management tasks. In this paper, we go one step beyond the loss tailoring schemes above, and introduce a loss meta-learning paradigm that (i) reduces the need for human intervention at model design stage, (ii) eases explainability and transferability of trained deep learning models for network management, and (iii) outperforms custom losses across a range of controlled experiments and practical use cases.
In this work, a recursive algorithm has been developed for heterogeneous network distributed systems (NDS) communicating over an undirected network to solve H-infinity optimal distributed tracking control problem of c...
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ISBN:
(纸本)9798350310375
In this work, a recursive algorithm has been developed for heterogeneous network distributed systems (NDS) communicating over an undirected network to solve H-infinity optimal distributed tracking control problem of continuoustime systems as a convex problem. Recent studies on NDS have studied the tracking control problem with decentralized performance functions defined for each subsystem in the network, on the contrary, a global performance function has been defined in this work for the whole NDS. An optimal distributed control problem has been defined as a sequential convex optimization problem benefiting off-policy reinforcement learning with sparsity constraints introduced on the symmetric positive definite matrix. Finally, the efficacy of the proposed algorithm is shown on a group of heterogeneous unmanned aerial vehicles (UAV) communicating over an undirected network.
Robotic arms are increasingly being used in collaborative environments, requiring an accurate understanding of human intentions to ensure both effectiveness and safety. Electroencephalogram (EEG) signals, which measur...
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The continuously rising number of cores per socket puts a growing demand on on-chip interconnects. The topology of these interconnects are largely kept hidden from the user, yet, they can be the source of measurable p...
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ISBN:
(纸本)9783031661457;9783031661464
The continuously rising number of cores per socket puts a growing demand on on-chip interconnects. The topology of these interconnects are largely kept hidden from the user, yet, they can be the source of measurable performance differences for large many-core processors due to core placement on that interconnect. This paper investigates the ARM Coherent Mesh network (CMN) on an Ampere Altra Max processor. We provide novel insights into the interconnect by experimentally deriving key information on the CMN topology, such as the position of cores or memory and cache controllers. Based on this insight, we evaluate the performance characteristics of several benchmarks and tune the thread-to-core mapping to improve application performance. Our methodology is directly applicable to all ARM-based processors using the ARM CMN, but in principle applies to all mesh-based on-chip networks.
networked controlsystems (NCSs) are an example of task-oriented communication systems, where the purpose of communication is real-time control of processes over a network. In the context of NCSs, with the processes s...
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ISBN:
(纸本)9781538674628
networked controlsystems (NCSs) are an example of task-oriented communication systems, where the purpose of communication is real-time control of processes over a network. In the context of NCSs, with the processes sending their state measurements to the remote controllers, the deterioration of controlperformance due to the network congestion can be partly mitigated by shaping the traffic injected into the network at the transport layer (TL). In this work, we conduct an extensive performance evaluation of selected TL protocols and show that existing approaches from communication and control theories fail to deliver sufficient controlperformance in realistic network scenarios. Moreover, we propose a new semantic-aware TL policy, which uses the process state information to filter the most relevant updates and the network state information to prevent delays due to network congestion. The proposed mechanism is shown to outperform all the considered TL protocols with respect to controlperformance.
The need for renewable energy in power systems is growing exponentially. Several algorithms may be used to track the Maximum Power Point (MPP) quickly and precisely. This research provides a comparison and analysis of...
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ISBN:
(纸本)9798350372113;9798350372106
The need for renewable energy in power systems is growing exponentially. Several algorithms may be used to track the Maximum Power Point (MPP) quickly and precisely. This research provides a comparison and analysis of different control techniques for the maximum power point tracking (MPPT) of a photovoltaic system subject to varying irradiance and temperature by using three algorithms which are Perturb and Observe (PO), Artificial Neural network (ANN), and Hybrid NN-PO. The three MPPT algorithms were created in a standalone photovoltaic system with a boost converter to maintain the maximum power point of the solar panel. Using MATLAB/SIMULINK software, the performance of these controllers is evaluated under varying irradiance and temperature conditions. Under the 100 (W/m2s) slope, PO's efficiency is the lowest, at 96.443% and the hybrid efficiency is nearly identical to the ANN algorithm at 99,996% and 99,997%, respectively. Based on the simulation that has been demonstrated, the Perturb and Observe (PO) algorithm exhibits the lowest performance in the simulation with time response. The Hybrid Neural network and Neural network algorithm performs better than PO. At the same time, hybrid efficiency is similar to the ANN algorithm.
This brief concerns the fixed-time backstepping trajectory tracking control problem for uncertain AUVs subject to unknown input saturation. By making use of the command filter technique, the adaptive control method an...
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This brief concerns the fixed-time backstepping trajectory tracking control problem for uncertain AUVs subject to unknown input saturation. By making use of the command filter technique, the adaptive control method and neural networks, a low-complexity nonlinear controller that contains only three dynamic update laws is proposed. And the new fixed-time stabilizing function is proposed to avoid the singularity problem. System performance analysis shows that the fixed-time stability is guaranteed for the AUV closed-loop system without violating the input saturation. The simulation result is given to demonstrate the effectiveness of our developed strategy.
We construct a neural network model of Sparameters, from which the S-parameters can be quickly predicted. Numerical tests on a filter model show that the proposed method accurately predicts the S-parameters with multi...
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ISBN:
(纸本)9798350351248;9798350351231
We construct a neural network model of Sparameters, from which the S-parameters can be quickly predicted. Numerical tests on a filter model show that the proposed method accurately predicts the S-parameters with multiple sharp resonances.
In the context of Industry 4.0, manufacturing systems face increased complexity and uncertainty due to elevated product customisation and demand variability. This paper presents a novel framework for adaptive Work-In-...
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In the context of Industry 4.0, manufacturing systems face increased complexity and uncertainty due to elevated product customisation and demand variability. This paper presents a novel framework for adaptive Work-In-Progress (WIP) control in semi-heterarchical architectures, addressing the limitations of traditional analytical methods that rely on exponential processing time distributions. Integrating Deep Reinforcement Learning (DRL) with Discrete Event Simulation (DES) enables model-free control of flow-shop production systems under non-exponential, stochastic processing times. A Deep Q-network (DQN) agent dynamically manages WIP levels in a CONstant Work In Progress (CONWIP) environment, learning optimal control policies directly from system interactions. The framework's effectiveness is demonstrated through extensive experiments with varying machine numbers, processing times, and system variability. The results show robust performance in tracking the target throughput and adapting the processing time variability, achieving Mean Absolute Percentual Errors (MAPE) in the throughput - calculated as the percentage difference between the actual and the target throughput - ranging from 0.3% to 2.3% with standard deviations of 5. 5% to 8. 4%. Key contributions include the development of a data-driven WIP control approach to overcome analytical methods' limitations in stochastic environments, validating DQN agent adaptability across varying production scenarios, and demonstrating framework scalability in realistic manufacturing settings. This research bridges the gap between conventional WIP control methods and Industry 4.0 requirements, offering manufacturers an adaptive solution for enhanced production efficiency.
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