In this paper, the modelling of a class of distributed water supply networks is considered, where the modelling of pipes, valves, pump stations and reservoirs are presented separately. Two laws, nodal mass balance and...
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Repetitive model predictive control is an effective method to track a periodic signal as well as reject a periodic disturbance. However, since repetitive controller uses discrete-time model, there always exists ripple...
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Repetitive model predictive control is an effective method to track a periodic signal as well as reject a periodic disturbance. However, since repetitive controller uses discrete-time model, there always exists ripple when controlling a continuous system with periodic disturbance. This paper proposes a new way for repetitive model predictive control by using multi-rate model. Due to the multi-rate model, the model predictive controller can optimize the system outputs during the period between two sampling times, and then provide a more delicate control action, which can result in the reduced ripple. The simulation demonstrates its effectiveness.
This paper proposes an incremental smooth support vector regression (ISSVR) method for TS fuzzy modeling. Under certain assumptions on membership functions, we propose an optimization problem for TS fuzzy modeling bas...
This paper proposes an incremental smooth support vector regression (ISSVR) method for TS fuzzy modeling. Under certain assumptions on membership functions, we propose an optimization problem for TS fuzzy modeling based on the structural risk minimization principle. We show that this problem is an SVR problem with non-positive definite kernels. It cannot be solved using conventional SVR. Then we establish a connection between this TS fuzzy modeling problem and smooth support vector regression (SSVR), which is a smoothing strategy for solving SVR. The problem is always solvable using SSVR because SSVR puts no restrictions on the kernel. Then we apply an incremental approach to the SSVR by selecting informative samples from the training dataset. Taking advantage of SSVR, more forms of membership functions can be used in our model compared with conventional methods. Experiments show that the proposed ISSVR-based TS fuzzy model has good generalization ability with small number of fuzzy rules.
This paper proposes a novel MPPT method, which will provide a correct tracking direction under the condition of non-abrupt atmospheric changes, especially non-irradiation conditions. We also present a model predictive...
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This paper proposes a novel MPPT method, which will provide a correct tracking direction under the condition of non-abrupt atmospheric changes, especially non-irradiation conditions. We also present a model predictive control method based on a cost function, which help to derive the predictions for each possible switching state. The control scheme obtained is capable of controlling the dc-link current to a desired MPPT reference current, while injecting sinusoidal current to the grid with reduced total harmonic distortion. Finally, the simulation results illustrate the effectiveness of our proposed method.
As the development of plugin hybrid electric vehicles (PHEV), this kind of hybrid vehicles have great potential in increasing the efficiency of energy and reducing carbon emission. The plug-in-feature not only means c...
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As the development of plugin hybrid electric vehicles (PHEV), this kind of hybrid vehicles have great potential in increasing the efficiency of energy and reducing carbon emission. The plug-in-feature not only means charging quickly and conveniently, but also brings power grid a great challenge in dynamic stability. Considering the information asymmetric characteristic in electricity market, we propose a contract based charging strategy with profit maximization of power sector as the object while guaranteeing the PHEV users' non-negative utility. We prove the result of the strategy via simulations based on meticulous modeling of PHEV.
Residential demand response is of greater importance in the smart grid. While under the condition of dynamic energy price and uncertain energy request from the consumers, it is a particular problem to explicitly model...
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Residential demand response is of greater importance in the smart grid. While under the condition of dynamic energy price and uncertain energy request from the consumers, it is a particular problem to explicitly model this uncertainty and schedule the devices. In this paper we use markov model to depict this uncertainty, and formulate an optimization problem to minimize the average total cost in an infinite horizon. Because it's hard to get the transition probability information about the energy price and energy request, we derive a parameterized online learning algorithm to determine the power allocation of deferable electricity appliance and control the storage. A numerical example show that this algorithm is effective and can significantly reduce the long term cost.
In this paper a multi-period planning and scheduling problem under demand correlations is addressed. A stochastic optimization model using restricted time structure is formulated. This model involves the minimization ...
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In this paper a multi-period planning and scheduling problem under demand correlations is addressed. A stochastic optimization model using restricted time structure is formulated. This model involves the minimization of the operation cost subject to constraints for the satisfaction of crude mix demands with a prespecified level of probability. A chance constrained programming that converts the stochastic programming to a deterministic one is developed for solving the problem. The deterministic problem is then solved by a proposed approximation algorithm iteratively. Finally, case studies are effectively solved by the proposed approaches. The results shows that the cost can be effectively reduced compared with the operations without considering the demand correlation.
In this paper, a multi-level model-based control strategy is applied to control large scale urban traffic networks. Structure of the strategy contains two levels, i.e., a coordinator in the upper level and model predi...
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In this paper, a multi-level model-based control strategy is applied to control large scale urban traffic networks. Structure of the strategy contains two levels, i.e., a coordinator in the upper level and model predictive controllers in the lower level. The coordinator aims to keep the distribution of vehicles in the network homogeneous, and the two-level structure makes the computational burden acceptable. The simulation shows the performance of multilevel model-based control is close to that of centralized model predictive control and better than decentralized control. Meanwhile, the computational complexity is greatly reduced comparing with centralized control.
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments o...
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ISBN:
(纸本)9781479929153
Current freeway traffic flow prediction techniques pay attention to time series prediction or introduce the upstream adjacent road segments in the short-term prediction model. In this paper, all of the road segments on the freeway are considered as candidates of the independent variables fed into the prediction model. A spatio-temporal multivariate adaptive regression splines (MARS) approach is proposed for the road network analysis and to predict the short-term traffic volume at the observation stations on the freeway. The actual traffic data are collected from a series of observation stations along a freeway in Portland every 15 minutes. In the first phase, the macroscopic dependency relationships of the stations on the freeway are investigated via MARS method. Subsequently the stations most related to the object station are selected and fed into the MARS prediction model to generate the short-term volume. The experiments are carried out on the actual traffic data and the results indicate that the proposed spatio-temporal MARS model can generate superior prediction accuracy in contrast with the historical data based MARS model, the parametric ARIMA, and the nonparametric PPR methods.
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