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The prediction of PM2.5 is difficult because the variation of PM2.5 concentration is a nonlinear dynamic process. Therefore, a recurrent fuzzy neural network prediction method is proposed to predict the PM2.5 concentration in this paper. Firstly, the partial least squares (PLS) algorithm is used to select key input variables as a preprocessing step. Then, a recurrent fuzzy neural network model is...
A stair-like generalized predictive control (GPC)strategy based on multiple models switching for a 4-wheel-drive with 4 hub motors is presented in this paper. It ensures that the vehicle can change its motion softly and meet real-time optimal control requirements of systems with jumping parameters. The strong nonlinear dynamics and interaction characteristics of electric vehicle result in large model...
This paper has proposed a design for networked control systems with random time delays and packet dropout in the forward communication channel by using fuzzy theories. First, a simple predictive model was built by means of fuzzy cluster modeling technology and neural network approximation. Then, a prediction oriented fuzzy sliding mode controller was presented to obtain the future control actions'...
This paper focuses on evaluation of the sampling strategy under different conditions. We first implement a framework for modeling and tracking spatio-temporal scalar fields with multiple robots, using Gaussian Process, Voronoi partition, discrete and continuous optimization algorithms. Then, the sampling conditions are divided into four cases from two perspectives (asynchronous / synchronous sampling...
The complexity and abstraction of the electromagnetic environment make the visual perception of the battlefield electromagnetic situation very difficult, which also highlights the importance of developing an integrated electromagnetic situation simulation system which integrates the electromagnetic environment visualization, the equipment interference analysis and the military action deduction. To...
For some big cities where congestion occurs frequently, this paper proposes a novel predictive congestion control algorithm, focusing on imposing hard constraints on links for avoiding congestion. Traditionally the urban traffic network is often optimized with some micro-integrated global performance indexes, which equalize the effect of congestion and the emergency of congestion may be ignored. However,...
In order to design a proper control strategy for CO2 capture process from fossil fuel power plants, a dynamic simulation model developed on general process modelling system (gPROMS) is presented first as well as its derived linear dynamic model. Then aiming at the real CO2 capture process characteristics of great inertia and frequent measurable disturbances, a linear dynamic matrix control with feedforward...
The technique of post-combustion CO2 capture is one of the most direct and effective means of reducing CO2 emissions from coal-fired power plants. However, owing to the complex behavior of CO2 capture system, such as large inertia, strong coupling among multi-variables, and strict constraints, the conventional PID controllers cannot meet the flexible operation requirement. For this reason, this paper...
Distributed energy systems with solar photovoltaic (PV) power grow rapidly in recent years in China. Accurate power forecasting is important for deep utilization and new exploitation of PV power. This study advances power forecasting modelling of PV systems using a method based on similar day selection and M5' model trees. A one-day-ahead power output forecast model is derived for an experimental...
The Burning Zone temperature of rotary kiln has the characteristics of strong coupling, nonlinearity and large delay, it is difficult to detect the temperature stability and accuracy. Based on knowledge reduction ability of rough set and nonlinear adaptive ability of support vector machines(SVM), a burning zone temperature soft sensor model based on RS-LSSVM is proposed and applied in the rotary kiln...
Model inaccuracies or parameter uncertainties are unavoidable in the practical control systems, while the uncertain properties could be modeled and estimated by the grey system. Among many grey models, fractional grey model is recently proposed and popularly used in many model analysis and prediction problems. In this paper, the structure uncertainties and external disturbances are considered using...
This paper investigates the Robust model predictive tracking control for constrained networked control systems with Markovian packet dropout. By introducing a packet dropout compensation strategy and an augmented Markov jump linear model with polytopic uncertainties, the effects of data loss on the system performance are considered simultaneously. Input, and output constraints are also incorporated...
Soft sensor has been widely used for estimating product quality or other important process variables when online analyzers are not available. In order to cope with estimation performance deterioration when process variables abruptly change, a new soft sensor modeling method based on auxiliary error neuro-fuzzy model is proposed. The model mean square error (MSE) is used as an evaluating index in traditional...
In order to realize the optimal control of variable frequency circulating pumps for ground source heat pump (GSHP) system it is necessary to build the prediction model of the total power consumption of GSHP system based on running data. Firstly the power consumption analysis of GSHP system with bilateral variable flow is presented. Then a Hyberball Cerebellar Model Articulation Controller (HCMAC)...
The ELM is used to predict the delay, and combines with implicit generalized predictive control (IGPC) to compensate time delay in this paper. The random time-delay in the networked control system (NCS) can usually deteriorate the control performance and stability of networked control system. In order to solve this problem, this paper puts forward the networked time delay in a short time based on...
Aiming at the problem that the capacity of lithium battery is difficult to monitor on-line, an indirect health factor method based on time interval to equal discharging voltage difference is employed. Partial correlation coefficient analysis method is used to prove strong correlation between actual capacity and time interval to equal discharging voltage difference. Glowworm swarm optimization algorithm...
How to operate a BFG/coal co-firing boiler in high efficiency is challenging for a gas/solid multi-fuel combustion system. Taking operation data from a real boiler, this study proposes an operation optimization strategy of BFG/coal co-firing boiler based on deep learning. Firstly, the thermal efficiency model is constructed based on deep learning with all the actual sampling data, which outperform...
The endpoint carbon content is one of the most important target in BOF (Basic Oxygen Furnace) steelmaking production. In this study, a real time carbon content prediction method is proposed for second blow period. Firstly, the traditional time-based exponential decarburization model is translated into an oxygen-based exponential decarburization model. Then, a valid data driven method named case-based...
This paper investigates the data-driven networked control problem for a class of nonlinear systems with random packet dropouts in the feedback and forward channels. To compensate for the two-channel packet dropouts, a new data-driven networked predictive control method is proposed by using a dynamic linearization approach. In the controller design, a general dynamic linearization data model, i.e.,...
Accurate short-term wind power prediction can improve the trade and the dispatch level of wind power. To predict the short-term wind power, we investigated the empirical mode decomposition (EMD) of numerical weather prediction (NWP) and genetic algorithm (GA) optimization of support vector regression (SVR). First, the wind speed data from NWP is decomposed into the EMD components, including multiple...
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