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In this paper, the design problem of unknown input observer for a class of nonlinear discrete time Markov jump systems has been studied. The state equation of the system with unknown input and actuator faults is considered. The unknown input observer has a relatively novel structure and can be applied to nonlinear discrete time Markov jump systems to estimate the system state and fault at the same time. Second, the design feasibility conditions of the unknown input observer based on Lyapunov function are given. Furthermore, the conditions are transformed into a set of linear matrix inequality conditions, which can be used to solve the parameters easily by using the related software toolbox. Finally, an example of nonlinear tunneling diode circuit is given to verify the feasibility and effectiveness of the proposed method.

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction has been widely concerned. The dynamic behavior of wind power time series is the external performance of complex nonlinear and multi-scale phenomena. Before choosing an appropriate prediction model, it is of great significance to analyze the characteristics of wind power time series for wind power grid system. At the same time, different sampling time scales of wind power also have an important impact on its dynamic characteristics. In this study, the chaotic dynamics behaviour of wind power time series at different time scales is discussed. The research methods include stationarity and white noise judgment, power spectral density analysis, autocorrelation function analysis, probability distribution, the Hurst index, 0–1 test algorithm for chaos, correlation dimension, maximum Lyapunov exponent, Kolmogorov entropy, recurrence plot, and information entropy method are adopted to study the chaotic dynamic behavior of wind power time series at different time scales. The actual wind power data of a wind farm at different time scales are taken as the research object for the case study. The case study results draw the following conclusions: (a) The wind power time series is non-stationary and non-white noise. (b) The direct-current and low frequency components parts store the main energy. (c) In the long-term, wind power is unpredictable. (d) The output level of wind power depends on the time scales. (e) The wind power time series obeys the fractal Brownian motion. (f) Wind power time series has chaotic characteristic. (g) Wind power time series has fractal characteristics. (h) As the time scales changes, so does the maximum prediction horizon. (i) With the decrease of time scales, the information loss rate has also declined. (j) Wind power at large time scales is more chaotic. The research results of this study have certain theoretical value and practical significance for grasping the fluctuation law of wind power and improving the prediction accuracy of wind power.

This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

Reasonable information scheduling strategies in the networked control system (NCS) can improve the quality of service of the network, reduce the conflict of information transmission in the network, and improve the overall performance of the NCS. In order to improve the performance of the NCS, a deadband feedback-based scheduling approach for the NCS with a variable sampling period is proposed. For the NCS with multi control loops, considering the limitation of network bandwidth resources, the dynamic real-time adjustment of a multi-loop sampling period is achieved through network utilization prediction, network bandwidth configuration and sampling period calculation. Furthermore, deadband feedback scheduling is combined with a variable sampling period algorithm. Deadband is set in the sensor and controller nodes to effectively adjust the information flow of the forward channel and the feedback channel. The proposed scheduling approach can reduce the impact of network conflict and network delay on system stability, make the network resources allocated reasonably, save network data traffic, and improve the overall performance of the NCS. A NCS with five control loops is used as the simulation object and carried out by True Time toolbox. The simulation results show that the proposed scheduling approach can improve output control performance of the system, reduce integral absolute error value of the control loops, and improve network utilization. The overall control performance of the system is improved.

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

The time-delay in a networked control system is difficult to predict accurately. In order to improve prediction performance, a time-delay prediction method is proposed. Firstly, the approximation c...

The effective control of coke oven flue temperature is discussed in this paper. A predictive control method based on orthogonal neural network for coke oven flue temperature is proposed. Firstly, t...

The accurate prediction of crop water requirement is of great significance for the development of regional agriculture. Based on the wavelet transform, a combined prediction approach for crop water requirement is proposed. Firstly, the Mallat wavelet transform algorithm is used to decompose and reconstruct the crop water requirement series. The approximate and detail components of the original series can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst index. Then, according to the different characteristics of the components, the particle swarm optimization algorithm optimized support vector machine is used to predict the approximate component, and the autoregressive moving average model is used to predict the detail components. Three-fold cross-validation is used to improve the generalization ability of the forecasting model. Finally, combined with the prediction value of each prediction model, the final prediction value of crop water requirement is obtained. The crop water requirement data from 1983 to 2018 in Liaoning Province of China are collected as the research object. The simulation results indicate that the proposed combined prediction approach has high prediction accuracy for crop water requirement. The comparison of performance indicators shows that the root mean square error of the proposed prediction approach reduced by 45.40% to 57.16%, mean absolute error reduced by 32.96% to 52.07%, mean absolute percentile error reduced by 33.02% to 52.37%, relative root mean square error reduced by 45.26% to 57.38%, square sum error reduced by 70.18% to 80.42%, and the Theil inequality coefficient reduced by 59.02% to 80.77%. R square increased by 16.46% to 54.77%, and the index of agreement increased by 3.82% to 23.37%. The results of Pearson's test and the DM test show that the association strength between the actual value and the prediction value of the crop water requirement is stronger. Moreover, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for crop water requirement is verified. The proposed prediction approach has great significance for the rational use of water resources, planning and management, promoting social and economic sustainable development.

Considering the blind parameters selection and the high dimension of input data in least squares support vector machine (LSSVM) modeling process, a kernel principal component analysis (KPCA)-based LSSVM forecasting method optimized by improved grey wolf optimization (GWO) algorithm is proposed. As an excellent forecasting model, the regression forecasting performance of LSSVM is greatly affected by parameters selection of the model. An improved GWO algorithm with better performance is proposed to determine the optimal parameters of LSSVM. This improved GWO algorithm improves the optimization precision and global optimization ability of the standard GWO algorithm. The parameters of LSSVM model are taken as the optimization object that is optimized by improved GWO algorithm. At the same time, the input variables of LSSVM are correlated and redundant. KPCA algorithm can eliminate the correlation and redundancy between input variables. The reduction of input variables reduces the complexity and training time of modeling process, and the coupling between input variables, to improve the prediction accuracy of LSSVM. The dynamic liquid level of beam pump is chosen as the research object. The proposed forecasting method is applied to the prediction of dynamic liquid level. The simulation comparison on actual collected dynamic liquid level data is performed. The simulation results show that the proposed forecasting method has better predictive performance for the dynamic liquid level.

In order to improve the performance of networked control system, a variable sampling period scheduling method for networked control system under resource constraints is presented based on network o...

Characteristic analysis of short-term wind speed time series is very meaningful for wind power system. The dynamic behavior of short-term wind speed time series is an external manifestation under t...

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.