This paper focuses on the load shifting problem in a household scenario with a large-capacity battery. We propose a novel Model Predictive control （MPC） framework to control the charge/discharge power of battery, he...
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ISBN:
(纸本)9781467355339
This paper focuses on the load shifting problem in a household scenario with a large-capacity battery. We propose a novel Model Predictive control （MPC） framework to control the charge/discharge power of battery, hence to shave the peak load. Being different from other studies, the framework is designed on the base of individual habit of energy consumption, as it is envisioned that the individual habit is critical for choosing the suitable energy services. In this paper, the habit is modeled as a Markov process and gradually learned by an iterative algorithm;thus, the habit can be utilized for the prediction of future energy consumption. Then, the rolling optimization is applied for the optimal control of the charge/discharge power of battery. It is shown by numerical simulations that the proposed approach can significantly reduce the peak load.
In this paper, T-G-P model is built to find maximum power point according to light intensity and temperature, making it easier and more clearly for photovoltaic system to track the MPP. A predictive controller conside...
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As well-known, model predictive control is closely related to optimal control. This paper studies relationships between them and provides a unified framework for optimality analysis of model predictive controllers (MP...
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As well-known, model predictive control is closely related to optimal control. This paper studies relationships between them and provides a unified framework for optimality analysis of model predictive controllers (MPC). The optimality is evaluated by comparing total performance of MPC with finite and infinite horizon optimal cost. Based on relaxed value iteration method, upper and lower bounds of optimality evaluation functions are expressed explicitly in terms of optimization horizon. These results reveal detailed characteristics on performance of closed-loop MPC systems due to using “receding horizon optimization” implementation style.
Interacting Multiple Model(IMM) filter faces significant outlier-caused *** this paper,the Bayesian probability update in IMM is found equivalent to Dempster's Rule of Combination which cannot handle evidence conf...
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ISBN:
(纸本)9781479900305
Interacting Multiple Model(IMM) filter faces significant outlier-caused *** this paper,the Bayesian probability update in IMM is found equivalent to Dempster's Rule of Combination which cannot handle evidence conflicts caused by ***,a novel robust MM(RMM) filter is proposed through introducing expert rules about mode evolvement and presenting the Likelihood Temporal Ratio(LTR) and building the Induced Combination Rule(ICR).Simulations about target tracking show the effectiveness of the proposed method.
To track the wide range of operating points of fast time-varying processes, a novel multiple model off-line predictive control algorithm is presented. The proposed method is a combination of multiple model strategy an...
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ISBN:
(纸本)9781467355339
To track the wide range of operating points of fast time-varying processes, a novel multiple model off-line predictive control algorithm is presented. The proposed method is a combination of multiple model strategy and predictive control. Firstly, we locally describe the original nonlinear system around an operating point employing linear time varying (LTV) model. Then the offline model predictive control (OMPC) algorithm is adopted to design local controller for LTV model, whose low computation burden makes it be able to control the fast time-varying process. To track the wide range of operating point, the multiple-model strategy is exploited. By estimating the stable region of local OMPC and selecting appropriate middle operating points, the stable switch between controllers can be guaranteed. Finally, a numerical simulation is given to illustrate the implementation and effectiveness of the proposed method.
This paper designs a mixed H/Hmodel predictive algorithm for systems with energy-bounded *** formulation of H/Hperformance in previous work is improved by introducing a slack ***,we decouple the formulation of H/Hperf...
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ISBN:
(纸本)9781479900305
This paper designs a mixed H/Hmodel predictive algorithm for systems with energy-bounded *** formulation of H/Hperformance in previous work is improved by introducing a slack ***,we decouple the formulation of H/Hperformance from the formulation of input and state constraints by dilated LMI *** two aspects are combined to develop a less conservative control *** feasibility of the algorithm is able to be proved and stability of the controlled system is *** example shows the effectiveness of the proposed algorithm.
Improving the computational efficiency for model predictive control (MPC) becomes the focus of recent researches. This paper proposes a fast algorithm to solve the quadratic programming (QP) problem of MPC for systems...
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ISBN:
(纸本)9781467355339
Improving the computational efficiency for model predictive control (MPC) becomes the focus of recent researches. This paper proposes a fast algorithm to solve the quadratic programming (QP) problem of MPC for systems with only input constraints. This algorithm improves the efficiency of the fast gradient approach. Inspired by the multiplexed way, this algorithm searches a better solution of QP problem along the direction of one input channel in every iteration. Meanwhile, in order to further reduce the online computational complexity, the proposed algorithm applies the aggregation strategy to each input channel. Due to the aggregation strategy and the determined optimization direction, the proposed algorithm reduces the number of vector multiplications of each iteration, i.e. with less online computational complexity than previous works. Applying the proposed algorithm, simulations for a system with 3 inputs show that computational efficiency of this algorithm can reach a level of tens of microseconds in Matlab environment.
Many researches on the prediction of the penetration rate of the tunneling boring machine (TBM) have been carried out. The prediction of the penetration rate will contribute to reduce the danger of TBM construction, d...
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ISBN:
(纸本)9781467355339
Many researches on the prediction of the penetration rate of the tunneling boring machine (TBM) have been carried out. The prediction of the penetration rate will contribute to reduce the danger of TBM construction, decrease the cost and provide the support for the construction planning. Most methods for predicting penetration rate rely on the fixed equation relationship between the input parameters and output parameters. In this paper, we build up a dynamic model. We mainly make use of the partial least squares regression algorithm (PLS) and the structure of the fuzzy-neuron network (FNN) to build up the model. All the data should be normalized. We randomly select 120 groups of the data from TBM construction as the training data and 33 groups of data as the testing data. The training and testing results are analyzed by the mean square error and the correlation coefficient. At the same time, we compare the prediction of the PLS-FNN model with the prediction of the FNN model. The simulation result shows that the PLS-FNN model has the good performance for the prediction.
A saturation allowed scheduled anti-windup design method is proposed in this paper for the linear systems subject to input saturation. We introduced the scheduled controller design method into the anti-windup scheme, ...
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A saturation allowed scheduled anti-windup design method is proposed in this paper for the linear systems subject to input saturation. We introduced the scheduled controller design method into the anti-windup scheme, in which a family of controllers are designed. These controllers are activated depending on the response of the system, rather than only considering the worst noise case off-line. Also, the delayed activation scheme is introduced into the scheduled antiwindup mechanism, i.e., the physical actuator is activated when it really need to, and then the most aggressive controller is activated. In this proposed scheme, since the saturation is allowed and no hard constraint is needed to imposed on the controller input, we can makes fuller utilization of the available actuator capacity. The proposed anti-windup scheme provides the system stability and guaranteed peak-to-peak gain. The control strategy is carried out under the scheme of state feedback and the main results are presented in linear matrix inequality forms. The advantage of the proposed saturated allowed scheduling anti-windup scheme against the traditional saturation avoid method is illustrated through a numerical example.
This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas b...
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This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas but also the finite angular resolution of antenna arrays. The probability mass function (PMF) of rank is studied and derived based on the Stirling number of the second kind. As a result, the closed-form expression of the capacity of a FS MIMO channel model is obtained. Furthermore, asymptotic analyses such as the large-scale system analysis, infinite scatterer analysis, and the multiplexing gain analysis are investigated.
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