In this paper, we consider the problem of feedback controller design via eigenvalue assignment for linear time-invariant systems of linear delay differential equations (DDEs) with a single delay. Unlike ordinary differential equations (ODEs), DDEs have an infinite eigenspectrum, and it is not feasible to assign all closed-loop eigenvalues. However, we can assign a critical subset of them using a solution to linear systems of DDEs in terms of the matrix Lambert W function. The solution has an analytical form expressed in terms of the parameters of the DDE, and is similar to the state transition matrix in linear ODEs. Hence, one can extend controller design methods developed based upon the solution form of systems of ODEs to systems of DDEs, including the design of feedback controllers via eigenvalue assignment. We present such an approach here, illustrate it using some examples, and compare with other existing methods.

No abstract

This article is concerned with the stabilization problem for nonlinear networked control systems which are represented by polynomial fuzzy models. Two communication features including signal transmission delays and data missing are taken into account in a network environment. To solve the network‐induced communication problems, a novel sampled‐data fuzzy controller is designed to guarantee that the closed‐loop system is asymptotically stable. The stability and stabilization conditions are presented in terms of sum of squares (SOS), which can be numerically solved via SOSTOOLS. Finally, a simulation example is provided to demonstrate the feasibility of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 74–81, 2015

No abstract

No abstract

No abstract

No abstract

Incentive-based demand response can fully mobilize a variety of demand-side resources to participate in the electricity market, but the uncertainty of user response behavior greatly limits the development of demand response services. This paper first constructed an implementation framework for incentive-based demand response and clarified how load-serving entity aggregates demand-side resources to participate in the power market business. Then, the characteristics of the user's response behavior were analyzed; it is found that the user's response behavior is variable, and it has a strong correlation on the timeline. Based on this, a prediction method of user response behavior based on long short-term memory (LSTM) is proposed after the analysis of the characteristics of the LSTM algorithm. The proposed prediction method was verified by simulation under the simulation environment setup by TensorFlow. The simulation results showed that, compared with the traditional linear or nonlinear regression methods, the proposed method can significantly improve the accuracy of the prediction. At the same time, it is verified by further experiments that the proposed algorithm has good performance in various environments and has strong robustness. INDEX TERMS Artificial neural networks, machine learning algorithms, state estimation, power demand, activity recognition, consumer behavior.