H&amp;#x221E; based stabilization and disturbance attenuation for nonlinear differential repetitive processes with an iterative learning control application

Emelianov, Mikhail; Pakshin, Pavel; Gałkowski, Krzysztof; Rogers, Eric

doi:10.1109/isic.2014.6967604

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Among recent possible applications for repetitive process control theory in the nonlinear model setting are metal deposition processes [15] and wind turbine control [16]. This paper starts from the results in [17] and establishes new results on the stabilization of nonlinear differential repetitive processes by a nonstandard application of vector Lyapunov functions. The analysis is then extended to stabilization and disturbance attenuation as measured by an H ∞ norm.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of differential repetitive processes

Emelianov¹,

Pakshin²,

Gałkowski

et al. 2015

Autom Remote Control

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Abstract-Differential repetitive processes are a subclass of 2D systems that arise in modeling physical processes with identical repetitions of the same task and in the analysis of other control problems such as the design of iterative learning control laws. These models have proved to be efficient within the framework of linear dynamics, where control laws designed in this setting have been verified experimentally, but there are few results for nonlinear dynamics. This paper develops new results on the stability, stabilization and disturbance attenuation, using an H ∞ norm measure, for nonlinear differential repetitive processes. These results are then applied to design iterative learning control algorithms under model uncertainty and sensor failures described by a homogeneous Markov chain with a finite set of states. The resulting design algorithms can be computed using linear matrix inequalities.

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Section: Introductionmentioning

confidence: 99%

Stabilization of differential repetitive processes

Emelianov¹,

Pakshin²,

Gałkowski

et al. 2015

Autom Remote Control

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show abstract

“…11 Nonlinear system control has always been a difficult and hot topic in the control field because nonlinear models have many unique dynamic characteristics and are difficult to be established. Although there are some results on nonlinear batch processes, 12,13 this topic has not been studied deeply and extensively because of the complexity and immaturity of nonlinear systems. If a method can be found to make use of the linear control theory and take into account the strong nonlinear characteristic of batch processes, great progress will be made for the advanced control theory for batch processes, which will further facilitate the fault-tolerant control for nonlinear systems.…”

mentioning

confidence: 99%

“…[30][31][32]34 In recent years, research on nonlinear batch processes has also emerged. 12,13,24 However, the situation when nonlinear batch processes encounter system faults and the corresponding fault-tolerant control has not drawn enough interest.…”

mentioning

confidence: 99%

Design of fuzzy iterative learning fault‐tolerant control for batch processes with time‐varying delays

Wang

et al. 2018

Optim Control Appl Methods

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In this paper, a new two-dimensional (2D) fuzzy composite iterative learning fault-tolerant control strategy using a 2D Takagi-Sugeno fuzzy model is proposed for batch processes with time delay and actuator faults. Firstly, based on the local-sector nonlinearity method, a 2D Takagi-Sugeno fuzzy model representing the nonlinear batch process with actuator faults is constructed with a series of linear models and nonlinear membership functions. Then, a 2D fuzzy feedback control-based iterative learning fault-tolerant control strategy is proposed under the constructed model. Using the 2D Lyapunov stability theory, sufficient conditions for system asymptotic stability are given. The fault-tolerant control law is then designed, guaranteeing system asymptotic stability along time and batches even if the system fails. Finally, the traditional control algorithm, the pure iterative learning control algorithm, and the feedback control-based iterative learning control algorithm proposed in this paper are compared on the level control of the three-tank system, which proves the effectiveness of the proposed method.

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H∞ based stabilization and disturbance attenuation for nonlinear differential repetitive processes with an iterative learning control application

Cited by 2 publications

References 6 publications

Stabilization of differential repetitive processes

Stabilization of differential repetitive processes

Design of fuzzy iterative learning fault‐tolerant control for batch processes with time‐varying delays

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