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.
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 this paper, we propose a CNN-based inverse reinforcement learning method that optimizes a reward function modeled by a linear combination. The proposed method efficiently extracts features from expert demonstration...
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ISBN:
(纸本)9798331517939;9788993215380
In this paper, we propose a CNN-based inverse reinforcement learning method that optimizes a reward function modeled by a linear combination. The proposed method efficiently extracts features from expert demonstrations using a CNN-based network and effectively estimates the reward function with a few iterations. The proposed method is called CNN-based apprenticeship learning for inverse reinforcement learning. The policy estimated by this method guarantees performance similar to or better than that of expert behavior. Through the Super Mario simulation, we demonstrate that the proposed CNN-based apprenticeship learning outperforms traditional imitation learning and reinforcement learning methods.
Calamansi is one of the major crops grown in the Philippines. Citrus diseases have been an issue in crop production worldwide, and these diseases can spread and infect other crops nearby. This study aims to implement ...
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ISBN:
(纸本)9798350372113;9798350372106
Calamansi is one of the major crops grown in the Philippines. Citrus diseases have been an issue in crop production worldwide, and these diseases can spread and infect other crops nearby. This study aims to implement a Convolutional Neural network to detect the three calamansi diseases: Citrus Huanglongbing, Citrus Canker, and Citrus Blackspot, along with the healthy calamansi leaf. This paper shows a portable device using Raspberry Pi 4 that can detect calamansi disease with specific diseases: Datasets gathered online with the said disease were trained using the Python programming language with the libraries of TensorFlow and Keras, a library for image processing with the Convolutional Neural network ResNet-50 pre-trained model to detect the said diseases. ResNet-50 is a type of Convolutional Neural network used in image classification because of its high performance. A total of twenty (20) samples, five (5) each classification, were used for testing, and a confusion matrix was used to calculate the system's accuracy, which is 90%.
Central Pattern Generators (CPG) nonlinear oscillation network is being increasingly used in the control of multi-joint collaborative robots. The motion attitude of robots can be effectively adjusted by tuning paramet...
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ISBN:
(纸本)9798350377712;9798350377705
Central Pattern Generators (CPG) nonlinear oscillation network is being increasingly used in the control of multi-joint collaborative robots. The motion attitude of robots can be effectively adjusted by tuning parameters of the CPG neural network. However, the mapping from CPG parameters to motion attitude is relatively complicated. To improve the motion performance, an optimization method combining computational fluid dynamics (CFD) and CPG network is proposed. In this work, we design a three-joint biomimetic soft robot fish following the body structure of trevally and an improved CPG network based on the Hopf model is incorporated into the control system. Directly optimizing the swimming performance through experiments is time consuming and complex, a mode of first adjusting parameters on the simulation platform and then refining on the robot is usually adopted. Therefore, a CFD simulation platform using hydrodynamic solutions has been established to assist in analyzing the swimming effect. Finally, the experimental results show that the swimming simulation by the CFD is highly similar to the real test, and the swimming performance after the improved CPG network optimization has been significantly increased.
Unmanned aerial vehicles (UAVs), such as drones, are beginning to be utilized for various purposes. Currently, most communication between drones and their control terminals is one-to-one direct communication, with the...
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ISBN:
(纸本)9798350379068;9798350379051
Unmanned aerial vehicles (UAVs), such as drones, are beginning to be utilized for various purposes. Currently, most communication between drones and their control terminals is one-to-one direct communication, with the range for transmitting data, such as video, limited to the direct reach of radio waves. As a solution to this problem, DANET, a drone-composed ad-hoc network, is gaining attention. However, existing protocols are insufficient for achieving adequate performance in DANETs. To configure a reliable DANET, we have proposed iFORP-3DD, an enhanced version of the iFORP routing protocol that incorporates cooperative control functionalities for managing the movement of relay terminals based on their location information and evaluated its communication performance. In this paper, we propose a route selection method using a cooperative operation approach that determines the duration of cooperative operation and movement speed according to the number of neighboring terminals in order to reduce the load on relay terminals and describe the evaluation results of the proposed method through simulation.
In this paper, the global prescribed performance tracking control issue for a class of uncertain switched nonlinear systems is presented by using adaptive neural network technology. First, a state-dependent switching ...
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ISBN:
(纸本)9798350366907;9789887581581
In this paper, the global prescribed performance tracking control issue for a class of uncertain switched nonlinear systems is presented by using adaptive neural network technology. First, a state-dependent switching signal is devised, which can guarantee a dwell time between any two adjacent switching instants. Secondly, an adaptive controller is designed to ensure that output tracking error satisfies the prescribed performance for any initial points and all of the signals in the closed-loop system are uniform ultimate bounded. Thirdly, the prescribed performance tracking control problem of each subsystem does not demand to be solvable. Finally, a simulation example is used to demonstrate the feasibility of the proposed method.
In Wireless networked controlsystems (WNCSs), the feedback control loops are closed over a wireless communication network. The proliferation of WNCSs requires efficient network resource management mechanisms since th...
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ISBN:
(纸本)9798350300529
In Wireless networked controlsystems (WNCSs), the feedback control loops are closed over a wireless communication network. The proliferation of WNCSs requires efficient network resource management mechanisms since the controlperformance is significantly affected by the impairments caused by network limitations. In conventional communication networks, the amount of transmitted data is one of the key performance indicators. In contrast, in WNCSs, the efficiency of the network is measured by its ability to facilitate control applications, and the data transmission rate should be limited to avoid network congestion. In this work, we consider an experimental setup where multiple control loops share a wireless communication network. Our testbed comprises up to five control loops that include Zolertia Re-Mote devices implementing IEEE 802.15.4 standard. We propose a novel relevance- and network-aware transport layer (TL) scheme for WNCSs. The proposed scheme admits the most important measurements for the control process into the network while considering current network conditions. Moreover, we propose a mechanism for the scheme parameters adaptation in dynamic scenarios with unknown network statistics. Unlike the conventional TL mechanisms failing to provide adequate controlperformance due to either congestion in the network or inefficient utilization of available resources, our method prevents network congestion while keeping the controlperformance high. We argue that relevance- and network-awareness are critical components of network protocol design to avoid controlperformance degradation in practice.
We investigate the impact of five power allocation methods on the physical layer performance of an optical multi-band system and identify the one that maximizes the transport capacity. Using this method, a capacity in...
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ISBN:
(纸本)9783903176621;9798350351132
We investigate the impact of five power allocation methods on the physical layer performance of an optical multi-band system and identify the one that maximizes the transport capacity. Using this method, a capacity increase of up to 21% can be attained. This capacity increase can be directly translated into a 10.4% reduction of optical transceivers over the entire network. (c) 2024 The Author(s)
An adaptive backstepping control algorithm combined with RBF neural network is proposed for the control problem of electrohydraulic servo system caused by factors such as nonlinearity and parameter uncertainty. Firstl...
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An adaptive backstepping control algorithm combined with RBF neural network is proposed for the control problem of electrohydraulic servo system caused by factors such as nonlinearity and parameter uncertainty. Firstly, the complex high-order nonlinear electrohydraulic servo system is decomposed into a low-order simple system with backstepping method. And the control law with unknown nonlinear function term is obtained. Secondly, the RBF neural network is applied to approximate the nonlinear function terms in the control law, and the adaptive rate is designed using the Lyapunov stability analysis method. Finally, simulation verification is conducted on the built Simulink simulation model, and the simulation results show that the adaptive control algorithm based on the RBF neural network backstepping method can achieve tracking control of the given signal and meet the desired dynamic performance criteria. In addition, in order to deal with the sensor noise problems that may be encountered in the actual deployment, the first-order filtering algorithm is introduced and verified in the simulation, which effectively reduces the noise interference.
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