This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitab...
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This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme.
Saving energy without causing discomfort and without de-manding human intervention is the need of the day. It is important to develop sensor systems which not only satisfy user requirements, but also take energy consu...
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Accurate cloud image forecasting is very necessary for sky image based solar power forecasting. To provide a means that can track the cloud deformation process and then forecast the cloud shape and position in a futur...
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Accurate cloud image forecasting is very necessary for sky image based solar power forecasting. To provide a means that can track the cloud deformation process and then forecast the cloud shape and position in a future sky image, a cloud image forecasting approach is proposed in this paper. Firstly, the cloud pixels in sky images are identified using Otsu’s method. Secondly, a mathematical description method of cloud location, shape, and deformation is suggested, so that the key information of cloud and its deformation process can be extracted from original images and then digitized. Thirdly, genetic algorithm (GA) is applied to optimize the cloud deformation process according to digitized historical cloud information. In the end, the cloud shape and position in a future image is forecasted using linear extrapolation. The feasibility and effectiveness of the proposed method are validated by simulation.
Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estima...
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Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.
In this paper, we consider the H∞ control problem for a class of 2-D Takagi-Sugeno fuzzy described by the second Fornasini-Machesini local state-space model with time-delays and missing measurements. The state delays...
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Brushless DC (BLDC) motors have been widely used in industry. However, one of the main drawbacks of using BLDC motors is the undesirable torque ripple. The torque ripple can cause mechanical vibration, acoustic noise ...
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ISBN:
(纸本)9781509060016
Brushless DC (BLDC) motors have been widely used in industry. However, one of the main drawbacks of using BLDC motors is the undesirable torque ripple. The torque ripple can cause mechanical vibration, acoustic noise and bearing damage that reduces the lifetime of the machine. This paper proposes a simple repetitive control scheme to attenuate torque ripple of BLDC motors. In this method, the ripple is considered as an undesirable repeated signal that will be minimized by the repetitive control scheme. The control input used in the proposed method is controlled transistors switching using space vector pulse width modulation technique. Simulation results show the proposed repetitive control technique results in the apparent reduction of the torque ripple when compared to a conventional control scheme.
Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve the...
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This paper concerns the integration of demand balance control and traffic flow coordination control in a hierarchical framework for complex urban traffic networks. At the first level, a complex traffic network is firs...
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Due to varying and intermittent nature of wind resource, grid connected wind farms pose significant technical challenges to power grid on power quality and voltage stability. Wind farm Q-V characteristic curve at the ...
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ISBN:
(纸本)9781509057252
Due to varying and intermittent nature of wind resource, grid connected wind farms pose significant technical challenges to power grid on power quality and voltage stability. Wind farm Q-V characteristic curve at the point of interconnection (POI) can offer valuable information for voltage control actions and provide essential indication about voltage stability. Data driven analytics is a promising approach to determine characteristics of a large complex system, physical model of which is difficult to obtain. In this paper, the data driven analytics is used to determine Q-V curve of grid connected wind farms based on measurement data recorded at the POI. Different curve fitting models, such as Polynomial, Gaussian and Rational, are evaluated and best fit is determined based on different graphical and numerical evaluation metrics. A case study is conducted using field measurement data at two grid connected wind farms currently in operation in Newfoundland and Labrador, Canada. It is found that the Gaussian (degree 2) model describes the Q-V relationship most accurately for the two wind farms. The obtained functions and processed data can be used in the voltage controller design. The plotted QV curve can also be used to determine the reactive margin at the POI for voltage stability evaluation. As a generic method, the proposed approach can be employed to determine Q-V characteristic curve of any grid connected large wind farms.
Managing a large volume of multimedia data, which contain various modalities (visual, audio, and text), reveals the need for a specialized multimedia database system (MMDS) to efficiently model, process, store and ret...
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