Control for Time‐Varying Delay Systems by Integrating Semi‐Discretization and Hysteresis‐Based Switching

This paper investigates a super-twisting disturbance observer (STDO)-based intelligence adaptive fault-tolerant formation control structure for cluster aerospace unmanned systems (CAUSs) subject to the external disturbances and actuator faults with switching topology. Firstly, in order to surmount the effects of the actuator unknown control gain and the deviation faults, a number of adaptive laws are developed. Secondly, benefiting from the precisely performance of STDO, we introduced a compensation term into the controller design to overcome the influence of the high dynamic external disturbances. Moreover, in order to achieve the communication and update of the information, CAUS controller maintains the leader-following consensus under the switching communication topology, which can promote the adaptability of the time-varying actual task demands. Numerical simulation results are demonstrated to illustrate the effectiveness and advantages of the proposed STDO-based intelligence adaptive formation control method for the CAUS.

Section: Introductionmentioning

confidence: 99%

Super‐twisting disturbance observer‐based intelligence adaptive fault‐tolerant formation control for a class of CAUS with switching topology

Gao

Ning

Wang

et al. 2021

“…Time delay systems have always attracted researchers interest [1][2][3]. Compensation of long input delay in control systems can be traced back to "Smith Predictor" in 1959 [4], but Smith's original result was not applicable to unstable system.…”

Section: Introductionmentioning

confidence: 99%

Predictor control for nonlinear systems actuated via transport PDEs with time/space varying propagation speeds

Cai

Yang

Liu

et al. 2021

We consider predictor controls for two kinds of cascaded systems of nonlinear ordinary differential equations (ODEs) and transport partial differential equations (PDEs) with time/space varying propagation speeds. Since the propagation speeds depend on time/space variables, how to get the prediction states of PDEs is a key challenge. We solve this challenge in this paper. We construct two infinite backstepping transformations to transform the original systems into target systems and design predictor controls for these two kinds of cascaded systems, respectively. We prove stability of the closed‐loop systems using Lyapunov‐like arguments and derive alternative representations of cascaded systems as nonlinear systems with time/space varying input delays and also establish equivalent predictor feedbacks for the delay systems.

“…Because delays affect the stability of the system under consideration, the presence and effects of delays cannot be ignored. Various kinds of delay differential equations (DDEs) arise in a wide variety of physical systems and real-world phenomena such as mechanical vibrations, transmission lines, signal processing, control systems, communication networks, and supply chains [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of a class of nonlinear multidelay systems with piecewise constant delays

Marzban

2020

This paper deals with the parameter estimation of an important class of nonlinear time-delay systems with a piecewise constant delay function. The developed procedure is based on a generalization of the hybrid of block-pulse functions and Taylor polynomials. The operational matrix of integration related to the generalized hybrid functions is constructed. Furthermore, an upper bound with respect to the L 2 -norm for the generalized hybrid functions is acquired, and convergence of the generalized hybrid functions is demonstrated. The merits of the hybrid basis are implemented to convert the problem under examination into an algebraic system. The methodology of least squares is adopted for solving the acquired algebraic system. An illustrative example is included in order to validate the efficiency and accuracy of the designed method. It is shown that the proposed approach provides accurate estimates for the desired parameters. The current approach reduces the computation complexity of the identification process.