A synthetic approach for robust constrained iterative learning control of piecewise affine batch processes

Liu, Tao; Wang, Youqing

doi:10.1016/j.automatica.2012.08.026

Cited by 69 publications

(50 citation statements)

References 29 publications

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“…In , based on the results of robust control for 2‐D Roesser system , a series of new algorithms for robust ILC design for discrete‐time linear systems based on LMI techniques are presented. Under this framework, the ILC design can also be considered for a class of linear systems with time delay , piece affine nonlinear systems , advanced PI control and so on. However, the existing 2‐D analysis approach is only applicable to the ILC design for systems without data dropouts.…”

Section: Introductionmentioning

confidence: 99%

An iterative learning control design approach for networked control systems with data dropouts

Hou

Jin

et al. 2015

Intl J Robust & Nonlinear

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Summary In network‐based iterative learning control (ILC) systems, data dropout often occurs during data packet transfers from the remote plant to the ILC controller. This paper considers the problem of controller design for such ILC processes. Packet missing is modeled by stochastic variables satisfying the Bernoulli random binary distribution, which renders such an ILC system to be a stochastic one. Then, the design of ILC law is transformed into the stabilization of a 2‐D stochastic system described by the Roesser model. A sufficient condition for mean‐square asymptotic stability is established by means of a linear matrix inequality technique, and formulas can be given for the control law design simultaneously. This result is further extended to more general cases where the system matrices also contain uncertain parameters. The effectiveness and merits of the proposed method are illustrated by a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

An iterative learning control design approach for networked control systems with data dropouts

Hou

Jin

et al. 2015

Intl J Robust & Nonlinear

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“…While inverter is running in parallel, each power module is regulated according to ωi and Ei by Pi and Qi, which realizes the load current sharing, and guarantees the uniform distribution of electric stress among the modules, and realizes current balance of each module. When the input voltage and load current change, each module can the ensure the stability of output voltage, and has good transient characteristics in current sharing at the same time [11] .…”

Section: The No Interconnection Line Control Based On Power Droop Chamentioning

confidence: 99%

Robust Iterative Learning Control of Single-phase Grid-connected Inverter

Yang

2014

Int. J. Autom. Comput.

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Abstract:For the single phase inductance-capacitance-inductance (LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. Iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively.

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“…Subsequent research has included uncertainty in the dynamic model and uncertain initial conditions at the start of each trial [9]. Also there has been research reported on an extension to nonlinear batch processes operating in a series of piecewise affine operating regions and, with industrial applications in mind, allowing the presence of constraints on the input and output [10].…”

Section: Introductionmentioning

confidence: 99%

Parameter‐dependent Lyapunov function‐based robust iterative learning control for discrete systems with actuator faults

Ding

Cichy

Gałkowski

et al. 2016

Adaptive Control & Signal

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SUMMARYThis paper considers iterative learning control for a class of uncertain multiple-input multiple-output discrete linear systems with polytopic uncertainties and actuator faults. The stability theory for linear repetitive processes is used to develop control law design algorithms that can be computed using linear matrix inequalities. A class of parameter dependent Lyapunov functions are used with the aim of enlarging the allowed polytopic uncertainty range for successful design. The effectiveness and feasibility of the new design algorithms is illustrated by a gantry robot case study.

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A synthetic approach for robust constrained iterative learning control of piecewise affine batch processes

Cited by 69 publications

References 29 publications

An iterative learning control design approach for networked control systems with data dropouts

An iterative learning control design approach for networked control systems with data dropouts

Robust Iterative Learning Control of Single-phase Grid-connected Inverter

Parameter‐dependent Lyapunov function‐based robust iterative learning control for discrete systems with actuator faults

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