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A Dual-Discrete Model Predictive Control-based MPPT for PV systems
摘要： This paper presents a method that overcomes the problem of the confusion during fast irradiance change in the classical MPPTs as well as in model predictive control (MPC)-based MPPTs available in the literature. The previously introduced MPC-based MPPTs take into account the model of the converter only, which make them prone to the drift during fast environmental conditions. Therefore, the model of the PV array is also considered in the proposed algorithm, which allows it to be prompt during rapid environmental condition changes. It takes into account multiple previous samples of power, and based on that is able to take the correct tracking decision when the predicted and measured power differ (in case of drift issue). After the tracking decision is taken, it will be sent to a second part of the algorithm as a reference. The second part is used for following the reference provided by the first part, where the pulses are sent directly to the converter, without a modulator or a linear controller. The proposed technique is validated experimentally by using a buck converter, fed by a PV simulator. The tracking efficiency is evaluated according to EN50530 standard in static and dynamic conditions. The experimental results show that the proposed MPC-MPPT is a quick and accurate tracker under very fast changing irradiance, while maintaining high tracking efficiency even under very low irradiance.
关键词： Buck converter,dc-dc power conversion,Photovoltaic systems,Double cost function,Maximum power point tracking,Drift,EN50530 standard,MPC
更新于2025-09-23 15:22:29
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Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system
摘要： Efficiency has been a major factor in the growth of photovoltaic (PV) systems. Different control techniques have been explored to extract maximum power from PV systems under varying environmental conditions. This paper evaluates the performance of a new improved control technique known as model predictive control (MPC) in power extraction from PV systems. Exploiting the ability of MPC to predict future state of controlled variables, MPC has been implemented for tacking of maximum power point (MPP) of a PV system. Application of MPC for maximum power point tracking (MPPT) has been found to result into faster tracking of MPP under continuously varying atmospheric conditions providing an efficient system. It helps in reducing unwanted oscillations with an increase in tracking speed. A detailed step by step process of designing a model predictive controller has been discussed. Here, MPC has been applied in conjunction with conventional perturb and observe (P&O) method for controlling the dc-dc boost converter switching, harvesting maximum power from a PV array. The results of MPC controller has been compared with two widely used conventional methods of MPPT, viz. incremental conductance method and P&O method. The MPC controller scheme has been designed, implemented and tested in MATLAB/Simulink environment and has also been experimentally validated using a laboratory prototype of a PV system.
关键词： maximum power point tracking (MPPT),prediction model,Model predictive control (MPC),cost function,photovoltaic (PV),renewable energy
更新于2025-09-23 15:21:01
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Fast Visual Odometry Based Sparse Geometric Constraint for RGB-D Camera
摘要： Pose estimation is a basic requirement for the autonomous behavior of robots. In this article we present a robust and fast visual odometry method to obtain camera poses by using RGB-D images. We first propose a motion estimation method based on sparse geometric constraint and derive the analytic Jacobian of the geometric cost function to improve the convergence performance, then we use our motion estimation method to replace the tracking thread in ORB-SLAM for improving its runtime performance. Experimental results show that our method is twice faster than ORB-SLAM while keeping the similar accuracy.
关键词： 3D reconstruction,pose estimation,geometric cost function,fast visual odometry,iterative optimization
更新于2025-09-19 17:15:36
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Location Ambiguity Resolution and Tracking Method of Human Targets in Wireless Infrared Sensor Network
摘要： Human tracking has attracted extensive attention by using low-cost pyroelectric infrared sensor network in recent years. This paper presents a location ambiguity resolution and tracking method for human targets in wireless, distributed and binary infrared sensor network. The tracking system can detect the human targets in the detection space, and activate the sensor detection lines dynamically. A bearing-crossing location method is designed. The intersections of all activated detection lines are called primary measurement points for human location, and some of them are false measurement points. The ambiguity of this bearing-crossing location method is discussed and a two-level bearing-crossing algorithm is proposed based on quartic K-means clustering and joint cost function. For the first level, an anti-logic algorithm is designed to get the initial effective measurement points, then these points are assigned to different targets using K-means clustering. For the second level, the final effective points are obtained by using a special joint cost function, and they are assigned to different targets using K-means clustering once again to get the final locating results. The cost value is used as a weight to adjust the covariance parameter in Kalman filter for target tracking as well. The experimental results show that the average tracking error of human targets is less than 0.8 m in a 10 m×10 m space, which verify the proposed location ambiguity resolution and tracking method.
关键词： Wireless Infrared sensor network,Cost function,Multiple human tracking,Binary pyroelectric infrared sensor network,Location ambiguity,Bearing-crossing location,Quadratic K-means clustering
更新于2025-09-10 09:29:36
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[IEEE 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Real Time Stereo Matching Using Two Step Zero-Mean SAD and Dynamic Programing
摘要： Dense depth map extraction is a dynamic research field in a computer vision that tries to recover three-dimensional information from a stereo image pair. A large variety of algorithms has been developed. The local methods based on block matching that are prevalent due to the linear computational complexity and easy implementation. This local cost is used on global methods as graph cut and dynamic programming in order to reduce sensitivity to local to occlusion and uniform texture. This paper proposes a new method for matching images based on a two-stage of block matching as local cost function and dynamic programming as energy optimization approach. In our work introduce the two stage of the zero-mean sum of absolute differences (ZSAD) combined with dynamic programming: the smoothness and ordering constraints are used to optimize correspondences. Stereo matching accuracy and runtime are the fundamental metrics to evaluate the stereo matching methods. The real-time has become a reality through the complexity reduction of the calculation and the use of parallel high-performance graphics hardware. In this paper we evaluate the developed method on using Middlebury stereo benchmark and, we propose a GPU CUDA implementation in order to accelerate our algorithm and reach the real time.
关键词： dynamic programming,stereo matching,GPU,CUDA implementation,cost function,block matching
更新于2025-09-04 15:30:14