Search results

This paper aims to present a fast, economical and practical attitude control design approach for flight vehicles operating within wide envelopes.

Based on a linear disturbance observer, an enhanced proportional-derivative (PD) control scheme is proposed. Utilizing the data from the onboard gyro, the observer can treat the entire response of the system, with the exception of the control term, as a disturbance, and use the estimation of the disturbance to cancel out this response and thereby to effectively simplify the control channel. Using the stability margin tester, the explicit graphical tuning rules are given in a consistent way for the longitudinal dynamics based on the induction method. Mathematical simulations are performed for a highly maneuverable flight vehicle to test the proposed method, which are compared with the traditional PD and H8 control algorithms.

The proposed strategy for attitude control can be reformulated as a static-dynamic control algorithm and the robust synthesis method can be employed to determine the control parameters according to a specific performance configuration. The remarkable control performance robustness can be achieved as shown in the comparative simulations.

There is a sole parameter, steady gain, needed to be scheduled and it can be estimated with a high accuracy.

This paper applies the linear active disturbance rejection control scheme to flight control scenario. The proposed method can reduce the design and implementation complexity of attitude control for flight vehicles operating within a wide envelope, which originates from diverse time-varying flight dynamics. The new method converts the attitude control problem to a sole parameter gain scheduling problem, and there is no complicated and time-consuming multi-dimension interpolation needed for the control parameters.

The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.

The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL.

RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error.

The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise.

Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

Ant colony algorithm is widely used in recent years as a heuristic algorithm. It provides a new way to solve complicated combinatorial optimization problems. Having been enlightened by the behavior of ant colony's searching for food, positive feedback construction and distributed computing combined with certain heuristics are adopted in the algorithm, which makes it easier to find better solution. This paper introduces a series of ant colony algorithm and its improved algorithm of the basic principle, and discusses the ant colony algorithm application situation. Finally, several problems existing in the research and the development prospect of ACO are reviewed.

The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy.

In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation.

It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process.

The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.