In this paper, a neuroadaptive fault-tolerant tracking control method is proposed for a class of time-delay pure-feedback systems in the presence of external disturbances and actuation faults. The proposed controller can achieve prescribed transient and steady-state performance, despite uncertain time delays and output constraints as well as actuation faults. By combining a tangent barrier Lyapunov-Krasovskii function with the dynamic surface control technique, the neural network unit in the developed control scheme is able to take its action from the very beginning and play its learning/approximating role safely during the entire system operational envelope, leading to enhanced control performance without the danger of violating compact set precondition. Furthermore, prescribed transient performance and output constraints are strictly ensured in the presence of nonaffine uncertainties, external disturbances, and undetectable actuation faults. The control strategy is also validated by numerical simulation.

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This paper investigates the distributed shortest-distance problem of multiagent systems where agents satisfy the same continuoustime dynamics. The objective of multiagent systems is to find a common point for all agents to minimize the sum of the distances from each agent to its corresponding convex region. A distributed consensus algorithm is proposed based on local information. A sufficient condition also is given to guarantee the consensus. The simulation example shows that the distributed shortest-distance consensus algorithm is effective for our theoretical results.

Rapid and accurate load forecasting is essential for renewable yet highly stochastic power (such as wind and solar power) to be massively utilized in practice. While there are many load forecasting methods reported in the literature, most of which, however, do not literally guarantee the convergence of forecasting error. This paper proposes a new error correcting approach for load forecasting in power systems by using trajectory tracking stability theory. In principle, the proposed method is not an autonomous but heuristic correcting approach to assess and improve the results of other existing models. This method is able to ensure the convergence of forecasting error in theory and is independent of system model, making it more feasible and cost-effective for forecasting performance improvement. Simulation experiments confirm the effectiveness of the proposed method for multiple existing models and forecasting horizons.

We can build the three-dimensional structure model based on the Gambit software and achieve the distribution of flow field in the pipe and reflux flow condition at the position of transducer in regard to the real position of transducer according to the Fluent software. Under the framework, define the reflux length based on the distance of reflux along the channel and evaluate the effect of reflux on flow field. Then we can correct the power factor with the transmission speed difference method in the ideal condition and obtain the matching expression of power correction factor according to the practice model. In the end, analyze the simulation experience and produce the sample table based on the proposed model. The comparative analysis of test results and simulation results demonstrates the validity and feasibility of the proposed simulation method. The research in this paper will lay a foundation for further study on the optimization of ultrasonic flowmeter, enhance the measurement precision, and extend the application of engineering.

Overcoming the coupling among variables is greatly necessary to obtain accurate, rapid and independent control of the real nonlinear systems. In this paper, the main methodology, on which the method is based, is dynamic neural networks (DNN) and adaptive control with the Lyapunov methodology for the time-varying, coupling, uncertain, and nonlinear system. Under the framework, the DNN is developed to accommodate the identification, and the weights of DNN are iteratively and adaptively updated through the identification errors. Based on the neural network identifier, the adaptive controller of complex system is designed in the latter. To guarantee the precision and generality of decoupling tracking performance, Lyapunov stability theory is applied to prove the error between the reference inputs and the outputs of unknown nonlinear system which is uniformly ultimately bounded (UUB). The simulation results verify that the proposed identification and control strategy can achieve favorable control performance.

High precision short-term load forecasting is crucial for enhancing safe and effective operation of power systems. This study presents a new method for short-term load forecasting using the concept of trajectory tracking. Unlike most existing forecasting methods, the proposed one is essentially model independent in that the corresponding forecasting algorithms are derived without the need for the specific load models. Furthermore, based upon Lyapunov stability theory, the prediction error of the proposed method is shown to converge with sufficient accuracy one gives rise to better forecasting performance.Peer ReviewedPostprint (published version