Robust reliable L2 – L∞ control for continuous‐time systems with nonlinear actuator failures

Sakthivel, R.; Ramya, L. Susana; Kaviarasan, B.; Santra, Srimanta; Leelamani, A.

doi:10.1002/cplx.21809

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…There are, however, faults that can combine the nonlinearity with states/inputs itself. Such faults are treated as mixed/hybrid faults and are more realistic [30]. These types of faults can occur even when the actuator has low efficiency, which is a major concern.…”

Section: Introductionmentioning

confidence: 99%

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Fault Tolerant Consensus of Multi Agent Systems Under Actuator Faults

Ramachandran

Juang

2022

IEEE Access

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This paper is concerned with the control of Lipschitz continuous multi-agent systems in the presence of actuator faults. The faults in a multi-agent system can potentially affect the mission and lead to instability. A linear matrix inequality based method is proposed to account for the faults so that a stable consensus can be maintained even when the agents are subject to hybrid faults and nonlinearities. Two illustrative examples are provided to show the design and feasibility of the proposed method.INDEX TERMS Observer, linear matrix inequality, hybrid actuator fault, Lipschitz nonlinear, multi agent system.

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Section: Introductionmentioning

confidence: 99%

“…These types of faults can occur even when the actuator has low efficiency, which is a major concern. For continuous time networked systems with hybrid actuator fault, suitable stabilization methods were discussed in [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Consensus of Multi Agent Systems Under Actuator Faults

Ramachandran

Juang

2022

IEEE Access

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“…For a control system where the peak value of the controlled output is required to be within a certain range, energy-to-peak control can serve as an excellent choice (Zhang, 2014). Recently, the issue of energy-to-peak control has been extensively investigated for various control systems, and a number of results have been reported in the literature; see, e.g., Hou et al (2013), Liang et al (2018), Rathinasamy et al (2016), Shen et al (2016) and Zhou et al (2016a). In the context of multi-agent systems with dynamically changing topologies, the energy-to-peak control problem was considered by Cui (2014), Mu et al (2014) and Shen et al (2014), where sufficient conditions for the control protocol design were provided in terms of linear matrix inequalities.…”

Section: Introductionmentioning

confidence: 99%

Energy-to-peak consensus for multi-agent systems with stochastic disturbances and Markovian switching topologies

Yan

Sang

Mu-yun

et al. 2018

Transactions of the Institute of Measurement and Control

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This paper is concerned with the problem of energy-to-peak consensus for a class of multi-agent systems with both stochastic disturbances and Markovian switching topologies. The objective is to design a control protocol in the form of dynamic output feedback, such that the multi-agent system reaches mean square consensus and has a prescribed energy-to-peak disturbance attenuation level. By using linear transformations, the problem is transformed into a standard energy-to-peak control problem. Then a new bounded real lemma is established. On the basis of this, an approach for the design of the control protocol is developed, by which the desired gain can be obtained via solving a number of linear matrix inequalities. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.

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“…In the second part of the paper, we combine the reduction approach with the approach of [17,18], in order to synthesize a high performance low order multi-H 2 controller for a time-delay system. Finally it should be said in this work we restrict to nominal model (1) -for the design of fault-tolerant schemes we refer to [22,39,41].…”

Section: Introductionmentioning

confidence: 99%

Reduced modelling and fixed‐order control of delay systems applied to a heat exchanger

Michiels

Hilhorst

Pipeleers

et al. 2017

IET Control Theory & Applications

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This paper presents an integrated approach for designing low-order multi-objective controllers for linear time-delay systems, combining recently developed methods for reduction of delay systems and fixed-order control design, respectively. First, as a benchmark problem for process control applications, a model of an experimental heat transfer setup is discussed in detail, which serves to motivate the adopted combination of model reduction and control technique. The former corresponds to a Krylov based reduction procedure, which is generalized to multiple-input-multiple-output systems with state, input and output delays, and allows the adaptive construction of an accurate low-order linear time-invariant approximation of the original linear time-delay system. Concerning the latter, a fixed-order controller is synthesized for the delay-free approximation, exploiting a recently proposed linear matrix inequality based framework for fixed-order controller design, and validated on the original linear time-delay system. In this way, the systematic overall design procedure, which is grounded in convex optimization, complements approaches based on directly optimizing stability and performance measures as a function of the controller parameters, which may lead to highly nonconvex and even non-smooth optimization problems. The successful design of a fixed-order multi-H 2 controller is validated on the benchmark problem, confirming the potential of the adopted approach for realistic industrial applications. IntroductionWe consider continuous-time models for control systems, described in terms of delay differential equations of the form(1) i = 1, . . . , my, where x(t) ∈ R n is the state, u i (t) ∈ R, i = 1, . . . , mu are the inputs, and y i (t) ∈ R, i = 1, . . . , my correspond to the outputs at time t. The quantities τ i , i = 1, . . . , mx, µ i , i = 1, . . . , mu and ν i , i = 1, . . . , my represent time-delays in the system's state, inputs and outputs, respectively, where the largest state delay is denoted by τmax = max i∈{1,...,mx} τ i . The inputs u i and outputs y i are grouped in vectors as follows:where we assume that the number of scalar inputs does not exceed the dimension of the system state, i.e., mu ≤ n. Without loss of generality, the input u and output y are subdivided asto distinguish between exogenous inputs u 1 and control inputs u 2 on the one hand, and regulated outputs y 1 and measured outputs y 2 on the other hand. The targeted class of delay systems described by (1) results from the physics based modeling of complex interconnections of components, where time-delay elements naturally appear in modeling propagation phenomena. The latter are due to the fact that the transfer of material, energy and information is mostly not instantaneous. For this class of systems, the experimental heat-transfer setup described in Section 3 serves as a benchmark problem for the control design. An analogous model has been derived in [25] for a greenhouse. More applications include chemical reactors [26], plate...

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Robust reliable L₂ – L_∞ control for continuous‐time systems with nonlinear actuator failures

Cited by 9 publications

References 34 publications

Fault Tolerant Consensus of Multi Agent Systems Under Actuator Faults

Fault Tolerant Consensus of Multi Agent Systems Under Actuator Faults

Energy-to-peak consensus for multi-agent systems with stochastic disturbances and Markovian switching topologies

Reduced modelling and fixed‐order control of delay systems applied to a heat exchanger

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