‐based optimal sparse sliding mode control for networked control systems

Argha, Ahmadreza; Li, Li; Su, Steven W.

doi:10.1002/rnc.3852

Cited by 10 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sliding mode control is a branch of the robust control algorithm. Its most notable feature is that it has complete invariance to un-modeled dynamics, parameter perturbation and external disturbance [2][3][4][5]. This provides a new research idea for solving the problems of nonlinear systems with uncertainties, which includes the problem of full envelope control of aeroengines [6,7].…”

Section: Introductionmentioning

confidence: 99%

RBF neural network sliding mode control of aero-engine distributed control systems

Peng

Wang

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

View full text Add to dashboard Cite

In this paper, the study of sliding mode control for nonlinear aero-engine distributed control system with time delay and packet loss is carried out. First, the system is modeled as a kind of Markov jump system with unmatched disturbance. Then, a linear sliding mode surface is proposed which is robust to unmatched disturbance. Linear matrix inequality method is used to solve the sliding surface parameter which makes the system mean square asymptotically stable. The RBF neural network is combined with sliding mode control theory in controller designing where the RBF neural network is used to solve the control input that satisfies the sliding mode reaching condition. Simulation results show that the designed controller can make the system stable in a fast time under the conditions of time delay and packet loss.

show abstract

Section: Introductionmentioning

confidence: 99%

RBF neural network sliding mode control of aero-engine distributed control systems

Peng

Wang

et al. 2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

View full text Add to dashboard Cite

show abstract

“…Otherwise, designing a control network with the minimum number of communication channels while satisfying a global performance requirements [12,1] should be considered as another choice for the distributed control systems. Actually, a trade off between the sparsity of the feedback gain matrices and the control performance should be explored [10,4,21,8,6].…”

mentioning

confidence: 99%

Optimal sparse output feedback for networked systems with parametric uncertainties

Argha¹,

Su²,

Ye³

et al. 2019

Numerical Algebra, Control &Amp; Optimization

Self Cite

View full text Add to dashboard Cite

This paper investigates the design of block row/column-sparse distributed static output H 2 feedback control for interconnected systems with polytopic uncertainties. The proposed approach is applicable to the networked systems with publisher/subscriber communication topology. We added two additional terms into the optimisation index function to penalise the number of publishers and subscribers. To optimally select a subset of available publishers and/or subscribers in the network, we introduced both an explicit scheme and an iterative process to handle this problem. We demonstrated the effectiveness by using a numerical example. The example showed that the simultaneous identification of favourable networks topologies and design of controller strategy can be achieved by using the proposed method.2010 Mathematics Subject Classification. Primary: 90C22, 90C35; Secondary: 93A15.

show abstract

Static output feedback fault tolerant control using control allocation scheme

Argha

Celler

2018

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

Summary This paper describes two novel schemes for fault tolerant control using robust suboptimal static output feedback design methods. These schemes can also be employed as actuator redundancy management for overactuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix (i) is not of full‐rank such that it can be factorized into two matrices and (ii) it does not involve uncertainty, these schemes can be applied to systems whose control input matrix cannot be factorized and/or involve uncertainty. The so‐called virtual control, in these schemes, is calculated using suboptimal H2‐based static output feedback design schemes constructed to be robust against uncertainties emanating from inherent input matrix uncertainty and visibility of the control allocator to the controller. Then, using two proposed control allocation schemes (fixed and on‐line), the obtained virtual control signal is redistributed among remaining (redundant or nonfaulty) set of actuators. As the proposed schemes are modular‐based, they can be employed as real‐time fault tolerant control schemes with no need to reconfigure the controller in the case of actuator faults or failures. The effectiveness of the proposed schemes is discussed and compared with numerical examples.

show abstract

‐based optimal sparse sliding mode control for networked control systems

Cited by 10 publications

References 31 publications

RBF neural network sliding mode control of aero-engine distributed control systems

RBF neural network sliding mode control of aero-engine distributed control systems

Optimal sparse output feedback for networked systems with parametric uncertainties

Static output feedback fault tolerant control using control allocation scheme

Contact Info

Product

Resources

About