The maximum correntropy criterion (MCC) algorithm depends upon two fundamental parameters, i.e., step-size and kernel width. Previous studies of parameteroptimization in the MCC mainly focus on a single parameter (ma...
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The maximum correntropy criterion (MCC) algorithm depends upon two fundamental parameters, i.e., step-size and kernel width. Previous studies of parameteroptimization in the MCC mainly focus on a single parameter (mainly the kernel width), lacking optimization research concerning both parameters. To this end, this letter investigates a novel optimization scheme simultaneously involving step-size and kernel width. The optimization framework is based on making the power of weight error vector undergo the steepest attenuation. Under the premise of maintaining the same evolutionary trend for time-varying step-size and kernel width, we formulate a constrained parameter optimization problem, where the step-size is subject to a kernel width induced constraint. By taking this approach, the original bivariate optimization can be transformed into a univariate optimization problem, which facilitates optimization solving. We further develop an existing reset scheme to make it suitable for kernel width to ensure a good tracking capability. In addition, we investigate the convergence behavior of the optimized algorithm. Simulation results demonstrate that the developed optimization scheme is beneficial for performance improvement, and the resulting algorithm outperforms some state-of-art MCC-based algorithms.
This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locall...
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This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration.
In this paper, an attempt has been made to design Internal Model Control (IMC) based Fractional Order Controller for Non-Minimum phase system using constrained minimization optimization technique. The IMC scheme has s...
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ISBN:
(纸本)9781467365406
In this paper, an attempt has been made to design Internal Model Control (IMC) based Fractional Order Controller for Non-Minimum phase system using constrained minimization optimization technique. The IMC scheme has some advantage over classical feedback control structure. Implementing IMC, one can very clearly analyze the effect of process characteristics such as time delays and Right Hand Plane (RHP) zeros on overall controllability of the process. Also IMC is much easier to tune as compared to the controller involved in the standard feedback control structure. Moving a step further, an effort has been made in the present work to enhance the flexibility of the above discussed IMC by introducing the non-integer order filter. Moreover to show the greatness of IMC based fractional order controller over integer order IMC controller, a comparative study has been made. Also for better disturbance rejection, various schemes of filter design have been proposed which includes both direct mathematical approach as well as optimization technique approach.
In this paper, an attempt has been made to design Internal Model Control (IMC) based Fractional Order Controller for Non-Minimum phase system using constrained minimization optimization technique. The IMC scheme has s...
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In this paper, an attempt has been made to design Internal Model Control (IMC) based Fractional Order Controller for Non-Minimum phase system using constrained minimization optimization technique. The IMC scheme has some advantage over classical feedback control structure. Implementing IMC, one can very clearly analyze the effect of process characteristics such as time delays and Right Hand Plane (RHP) zeros on overall controllability of the process. Also IMC is much easier to tune as compared to the controller involved in the standard feedback control structure. Moving a step further, an effort has been made in the present work to enhance the flexibility of the above discussed IMC by introducing the non-integer order filter. Moreover to show the greatness of IMC based fractional order controller over integer order IMC controller, a comparative study has been made. Also for better disturbance rejection, various schemes of filter design have been proposed which includes both direct mathematical approach as well as optimization technique approach.
This paper introduces a system identification approach to overcome the problem of insufficient data when developing and parameterising an agroforestry system model. Typically, for these complex systems the number of a...
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This paper introduces a system identification approach to overcome the problem of insufficient data when developing and parameterising an agroforestry system model. Typically, for these complex systems the number of available data points from actual systems is less than the number of parameters in a (process-based) model. In this paper, we follow a constrained parameter optimization approach, in which the constraints are found from literature or are given by experts. Given the limited a priori systems knowledge and very limited data sets, after decomposition of the parameter estimation problem and after model adaptation, we were able to produce an acceptable correspondence with validation data from a real-world agroforestry experiment. (C) 2011 Elsevier Ltd. All rights reserved.
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