This paper proposes a power factor correction (PFC) method in a single-phase AC/DC converter using a predictive control algorithm (PCA) through a boost DC/DC converter. The minimum value of the DC-link capacitor has b...
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This paper proposes a power factor correction (PFC) method in a single-phase AC/DC converter using a predictive control algorithm (PCA) through a boost DC/DC converter. The minimum value of the DC-link capacitor has been evaluated on the DC distribution side for stable operation of the converter. The proposed technique can be applied to the cascade connection of the converters, that is, the uncontrolled rectifier with boost converter and load. The value of the minimum capacitance is obtained as a function of the reference voltage of the DC-link capacitor, the frequency of the supply, the input power, and the maximum DC-link voltage. The proposed design is applied to control the boost converter for PFC applications using PCA at the minimum DC-link capacitor value. The calculated value of DC-link capacitance has been reduced to a greater extent as compared to the conventional calculation of DC-link capacitance. The converter operation with the designed value of DC-link capacitance has been verified through the MATLAB and Typhoon Simulink environment, and an experimental prototype model is used for the validation by using the FPGA based Typhoon HIL-402 kit.
Medium-voltage industrial motor drives have been facing the problem of common-mode voltage(CMV) for the past few decades. Multilevel inverters provide not only better inverter performance but also greater electrical m...
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ISBN:
(纸本)9781665455664
Medium-voltage industrial motor drives have been facing the problem of common-mode voltage(CMV) for the past few decades. Multilevel inverters provide not only better inverter performance but also greater electrical motor utilization. Conventional control of inverters with SVPWM has to limit constraints to accommodate the non-linearity and operational flexibility. The predictivecontroller can build the cost function to accommodate all possible improvement functions that are preferred over these conventional inverter-driven machines. This paper describes a generic approach to developing a predictive control algorithm for two-, three-, and five-level diode-clamped inverters. This algorithm provided the choice of optimal voltage vector selection in the elimination of the common-mode-voltages in three-phase inverters. The effectiveness of these switching voltage vectors also contributes to the improvement of inverter efficiency and DC-bus utilization. The implementation of this generic algorithm has been verified through simulation and laboratory prototypes.
To achieve a reasonable power split scheme of Li battery pack and supercapacitor hybrid electric vehicles, we propose dynamic programming (DP) based predictive control algorithm (PCA) in this paper. First, the model o...
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ISBN:
(纸本)9781479944972
To achieve a reasonable power split scheme of Li battery pack and supercapacitor hybrid electric vehicles, we propose dynamic programming (DP) based predictive control algorithm (PCA) in this paper. First, the model of the vehicle plant is established consisting of mathematical models of supercapacitor and Li battery pack. Then, the PCA based control system is designed in order to make full use of future road information. Thirdly, a DP-based-controller is proposed to minimize the cost function which consists of power loss and constrains of output. The simulation suggests that the proposed strategy can generate reasonable power split by taking the power loss, constraints of two sources and flatness of power output of Li battery pack into account.
This article deals with the output-current control of a single-pulse-operated switched reluctance generator (SRG). Typically, a linear proportional integral (PI) controller whose gain is obtained by hit-and-trial or b...
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This article deals with the output-current control of a single-pulse-operated switched reluctance generator (SRG). Typically, a linear proportional integral (PI) controller whose gain is obtained by hit-and-trial or by a tuning method is utilized. However, due to the highly nonlinear nature of single-pulse operated SRG, the performance of the PI controller tuned at one operating point deteriorates at another. This article proposes a nonlinear predictive control algorithm for output-current control of single-pulse operated SRG. In particular, two independent controllers are developed, one controlling the turn-ON angle and the other controlling the turn-OFF angle. These controllers manifest good steady-state tracking, dynamic response, and disturbance rejection. Compared to a PI controller, the proposed controller performs consistently at different operating points without tuning any controller parameter. The proposed controlalgorithm does not require prior measurement of machine characteristics. It does not require storing any offline computed data.
The escalating charging demands driven by the rapid expansion of electric vehicles (EVs) can lead to overlap with residential load, impacting power system instability. Therefore, mitigating the overlap between the EV ...
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The escalating charging demands driven by the rapid expansion of electric vehicles (EVs) can lead to overlap with residential load, impacting power system instability. Therefore, mitigating the overlap between the EV charging load and the residential load is necessary in the development of EVs. In this context, efficient energy management is proposed in this work to reduce the overlap between the EV charging load and the residential load. First, a self-sustained transportation energy system (STES) is introduced in this work by equipping with photovoltaic (PV) power, to ensure the energy demand of EVs. Moreover, an effective three-stage predictivecontrol approach is elaborately developed in this STES, aiming to reduce the reliance on the power grid and optimize the consumption of PV power. The controlalgorithm of the three-stage predictive approach operates as follows: Stage I focuses on optimizing day-ahead electricity purchases based on supply and demand predictions, Stage II allocates charging power to stations, and Stage III executes real-time control leveraging energy storage system (ESS) capabilities. Meanwhile, an ensemble deep learning model is well-designed in this proposed method to capture the long-term dependence and the underlying periodic pattern of PV power and the charging demand, called ensemble temporal convolutional network-bidirectional long short-term memory network (ETCN-BiLSTM). The integration of ETCN is achieved by a weight fusion mechanism that calculates the contribution of different TCN layers. Then, this work employs ESS as a "mitigator" to balance the energy supply and demand. Experimental validation and comparative analysis highlight the efficacy of both prediction and control components in optimizing energy management. Through comprehensive testing, the proposed approach demonstrates its capability to efficiently manage energy in charging stations while maintaining economic feasibility.
Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using c...
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Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion controlalgorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictivecontroller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictivecontrol approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.
Changes in water diversion flow are the major disturbance sources in the daily operation of water diversion projects. Ensuring efficient and safe project operation while dealing with different degrees of water diversi...
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Changes in water diversion flow are the major disturbance sources in the daily operation of water diversion projects. Ensuring efficient and safe project operation while dealing with different degrees of water diversion disturbance is crucial for real-time control operation. Based on the historical water diversion projects in China and abroad, this study constructs the water diversion disturbance conditions, selects the typical disturbance lines, and constructs the control objectives for different water diversion disturbance lines. The discrete state space equation of the multi-channel pool integral time-delay model is introduced and used as the system prediction model. Concurrently, the simulation results of the river channel hydrodynamic model are used to correct the system state. The model predictive control algorithm is established according to the objective functions of different typical water distribution disturbance lines, and the control strategy of the control gate and pump station along the water diversion project is formulated to assist in the decision making of the project scheduling operation scheme. The proposed method can better cope with different degrees of river diversion disturbance, compensate for the loss of control performance caused by the low accuracy of the generalized model simulation, and improve water level control and sluice regulation.
In oil production platforms, processes are nonlinear and prone to modeling errors, as the flow regime and components are not entirely known and can bring about structural uncertainties, making the design of predictive...
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In this paper, the learning and behavior predictivecontrol based on cloud computing is proposed for efficiently planning autonomous real time prespecifled trajectory tracking and obstacle avoidance control for an omn...
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ISBN:
(纸本)9781538662793;9781538662786
In this paper, the learning and behavior predictivecontrol based on cloud computing is proposed for efficiently planning autonomous real time prespecifled trajectory tracking and obstacle avoidance control for an omnidirectional wheeled robot using fuzzy inference algorithm. The autonomous trajectory tracking control includes dynamic simulation according to object surface and depth measurement. The robot is equipped with three independent driven omnidirectional wheels and six ultrasonic sensors. The Jacobian between Cartesian space with respect to the joint space is setup for ellipse motion planning so that it not only can autonomously follow the prespecifled trajectory tracking but also avoid obstacles. An architecture is setup to split computation between the remote cloud and the robots so that the robots can interact with the computing cloud. Given this robot/cloud architecture, the stability of the closed loop control system using the predictive control algorithm is guaranteed with satisfactory tracking performance on the cloud during a periodically updated preprocessing phase, and manipulation queries on the robots given changes in the workspace can achieve real time trajectory tracking and obstacle avoidance. Finally, experiments are given to validate the path tracking performance and computational efficiency.
In this paper, a simplified non-linear model of ultra-supercritical units and a soft sensor model for net calorific value of coal are studied. The generalized predictive control algorithm is applied to the control sch...
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In this paper, a simplified non-linear model of ultra-supercritical units and a soft sensor model for net calorific value of coal are studied. The generalized predictive control algorithm is applied to the control scheme, and the net calorific value is taken into account in feedforward of predictivecontroller to enhance the accuracy of prediction. Simulation results show that the control scheme can effectively inhibit the disturbance of coal quality, thereby improving the variable load rate and stability of the unit. The results have been successfully applied to a 660 MW thermal power unit, and have achieved good control performance.
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