Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.Tracking (position)

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Now showing 1 - 8 of 8
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    Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator
    (Robotics Society of Japan ar@rsj.or.jp, 2016) Vijay, M.; Jena, D.
    [No abstract available]
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    Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM
    (Elsevier Ltd, 2017) Satapathi, G.S.; Srihari, P.
    This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs. © 2017 Elsevier Ltd
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    Backstepping terminal sliding mode control of robot manipulator using radial basis functional neural networks
    (Elsevier Ltd, 2018) Vijay, M.; Jena, D.
    This paper examines an observer-based backstepping terminal sliding mode controller (BTSMC) for 3 degrees of freedom overhead transmission line de-icing robot manipulator (OTDIRM). The control law for tracking of the OTDIRM is formulated by the combination of BTSMC and neural network (NN) based approximation. For the precise trajectory tracking performance and enhanced disturbance rejection, NN-based adaptive observer backstepping terminal sliding mode control (NNAOBTSMC) is developed. To obviate local minima problem, the weights of both NN observer and NN approximator are adjusted off-line using particle swarm optimization. The radial basis function neural network-based observer is used to estimate tracking position and velocity vectors of the OTDIRM. The stability of the proposed control methods is verified with the Lyapunov stability theorem. Finally, the robustness of the proposed NNAOBTSMC is checked against input disturbances and uncertainties. © 2017 Elsevier Ltd
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    A Hybrid Global Maximum Power Point Tracking Technique with Fast Convergence Speed for Partial-Shaded PV Systems
    (Institute of Electrical and Electronics Engineers Inc., 2018) Goud, J.S.; Kalpana, R.; Singh, B.
    Photovoltaic (PV) systems exhibit multiple local and one global maximum power points (MPPs) in their P -V and I-V curves during partial shading conditions (PSC). Thus, to improve the efficiency of the system, a global maximum power point tracking (GMPPT) algorithm is necessary. This paper presents a hybrid GMPPT algorithm for constant voltage load applications using a single current sensor. The proposed method combines single current sensor hill climbing (SSHC) and artificial bee colony (ABC) algorithms to track the GMPP. The SSHC algorithm detects the event of PSC and tracks the MPP during uniform insolation conditions. The output current of the power electronic interface is measured effectively at selective duty cycles to identify the type of P-V curve pattern and, thus, initiate either SSHC or ABC. The search space for the ABC algorithm is reduced in the proposed technique to improve the convergence speed. The proposed GMPPT technique is simulated in MATLAB and validated through experimental prototypes for various PSCs. The proposed algorithm tracks the GMPP with excellent efficiency and fast speed. © 1972-2012 IEEE.
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    Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array
    (Institution of Engineering and Technology journals@theiet.org, 2018) Goud, J.S.; Kalpana, R.; Singh, B.; Kumar, S.
    This study presents a single current sensor based hybrid maximum power point tracking method to track the global maximum power point (GMPP) of the photovoltaic (PV) array during the mismatch insolation conditions. This method combines the artificial bee colony (ABC) and hill climbing (HC) algorithms to track the GMPP of a PV array. The proposed method uses the HC algorithm to identify the occurrence of mismatch insolation conditions on PV array. During the mismatch insolation conditions, the proposed method scans the battery charging current (Icharge) versus duty cycle (D) characteristics of the power electronic interface circuit to classify the type of shading pattern of P-V curve and also to identify the vicinity of the GMPP. Based on the kind of shading pattern of a P-V curve, the proposed method operates either ABC or HC algorithm to track the GMPP. To improve the convergence speed of the proposed method, the search space of the ABC algorithm is reduced. The proposed method is modelled and simulated in MATLAB software and its performance is validated experimentally for various mismatch insolation conditions. © The Institution of Engineering and Technology 2018.
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    Output power enhancement of solar PV panel using solar tracking system
    (Bentham Science Publishers B.V. P.O. Box 294 Bussum 1400 AG, 2019) Tripathi, A.K.; Mangalpady, M.; Murthy, C.S.N.
    Solar Photovoltaic (PV) energy conversion has gained much attention nowadays. The output power of PV panel depends on the condition under which the panel is working, such as solar radiation, ambient temperature, dust, wind speed and humidity. The amount of falling sunlight on the panel surface (i.e., solar radiation) directly affects its output power. In order to maximize the amount of falling sunlight on the panel surface, a solar tracking PV panel system is introduced. This paper describes the design, development and fabrication of the solar PV panel tracking system. The designed solar tracking system is able to track the position of the sun throughout the day, which allows more sunlight falling on the panel surface. The experimental results show that there was an enhancement of up to a 64.72% in the output power of the PV panel with reference to the fixed orientation PV panel. Further, this study also demonstrates that the full load torque of the tracking system would be much higher than the obtained torque, which is required to track the position of the sun. This propounds, that the proposed tracking system can also be used for a higher capacity PV power generation system. © 2019 Bentham Science Publishers.
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    A Global Maximum Power Point Tracking Technique of Partially Shaded Photovoltaic Systems for Constant Voltage Applications
    (Institute of Electrical and Electronics Engineers Inc., 2019) Goud, J.S.; Kalpana, R.; Singh, B.; Kumar, S.
    The P-V characteristics of photovoltaic (PV) array exhibit several maximum power points (MPP) during non-uniform insolation (i.e., during partial shading) conditions; there exists only one global MPP (GMPP), whereas others are referred to local MPP. This paper presents a technique to track the GMPP for the constant voltage or battery loads during partial shading conditions using a single sensor connected to the battery terminals. The proposed method introduces fast and efficient scanning based method, i.e., scanning Ibatt-D curve of power electronic interface at selective duty cycles to recognize the kind of the solar shading pattern (i.e., kind of P-V curve) on PV array and to find the GMPP neighborhood. Moreover, the proposed method overcomes the drawbacks of existing methods such as low convergence speed, increased number of sensors, and heavy computational complexity. The proposed GMPPT method is simulated in MATLAB/Simulink and validated through test results on a prototype for various non-uniform insolation conditions. The results have shown that this paper tracks the GMPP with best tracking efficiency and fast tracking speed. Further, the proposed method is compared with two P-V curve scanning based GMPPT methods and one global optimization based artificial bee colony method. © 2018 IEEE.
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    Visual Tracking Using Kernelized Correlation Filter with Conditional Switching to Median Flow Tracker
    (Taylor and Francis Ltd, 2020) Asha, C.S.; Narasimhadhan, A.V.
    The correlation filters (CF) have been extensively used in object tracking due to its robustness and attractive computational speed. However, the CF are more sensitive to object deformation because they are trained using the spatial features. Besides, updating the filter template with slightly drifted or occluded samples increase the probability of tracking failure. In contrast, the median flow tracker is complementary to the correlation techniques and is fast, robust to occlusion and deformation, but sensitive to illumination variation. In this paper, we exploit the advantage of correlation and optical flow based trackers to achieve drift free tracking. Hence, we apply the CF-based tracker to track an object and switch to the modified median flow tracker during the drift conditions. The combined model is optimized to cope up with the fast appearance change and recover from drifting. We also propose an adaptive feature selection process to select the most discriminative feature/features among colour name and histogram of oriented gradient features based on object separation from the background in intensity and colour channels. The proposed tracker updates the filter template dynamically, depending on the appearance of an object using an adaptive learning rate to track the object irrespective of occlusion, motion blur, and deformation. The scale of object is estimated using Lucas-Kanade homography method. The experiments are carried out using challenging video sequences from a standard object tracking benchmark dataset and show the best performance among the state-of-the-art techniques. © 2020, © 2020 IETE.