Iterative learning control for networked stochastic systems with random packet losses

Shen, Dong; Wang, Youqing

doi:10.1080/00207179.2014.986762

Cited by 21 publications

(43 citation statements)

References 12 publications

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“…For the extension from global Lipschitz condition to local Lipschitz condition, a possible way is to adopt similar techniques in [18,19]. For the extension from global Lipschitz condition to local Lipschitz condition, a possible way is to adopt similar techniques in [18,19].…”

Section: Problem Formulationmentioning

confidence: 99%

“…∞ ] T and derive the associated matrix Γ from (19) as a block lower-triangular matrix with its elements being the parameters of (19). Then, we have ‖U k+1 ‖ ∞ ≤ ‖Γ‖ ∞ ‖U k ‖ ∞ .…”

Section: Remarkmentioning

confidence: 99%

“…For example, unmanned aerial vehicles (UAVs) can be used for surveillance of some specified area and the surveillance routine is usually repeatable. Several papers have been dedicated to the design and analysis of ILC algorithm under random data dropouts environments [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Another similar example is the trajectory-keeping control in satellite formation flying [9].…”

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Jin¹,

Shen²

2017

Asian Journal of Control

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This paper discusses the iterative learning control (ILC) for nonlinear systems under a general networked control structure, in which random data dropouts occur independently at both measurement and actuator sides. Both updating algorithms are proposed for the computed input signal at the learning controller and the real input signal at the plant, respectively. The system output is strictly proved to converge to the desired reference with probability one as the iteration number goes to infinity. A numerical simulation is provided to verify the effectiveness of the proposed mechanism and algorithms.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Jin¹,

Shen²

2017

Asian Journal of Control

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“…Regarding the packet dropout issue, to date, a few NILC explorations have been carried out [13][14][15][16][17][18][19][20]. Much attention has been paid to the packet loss of system output, and the handling strategy of dropped system output is that the ILC rule does not update the control signal when the corresponding system output data are lost.…”

Section: Introductionmentioning

confidence: 99%

Networked Iterative Learning Control Design for Nonlinear Systems with Stochastic Output Packet Dropouts

Liu

Ruan

2017

Asian Journal of Control

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This paper develops two proportional-type (P-type) networked iterative learning control (NILC) schemes for a class of discrete-time nonlinear systems whose stochastic output packet dropouts are modeled as 0-1 Bernoulli stochastic sequences. In constructing the NILC schemes, two kinds of compensation algorithm of the dropped outputs are given. One is to replace the instant-wise dropped output data with the synchronous desired output data; the other is to substitute the dropped data with the consensus-instant output data used at the previous iteration. By adopting the lifting technique, it is derived that under certain conditions the expectations of the tracking errors incurred by the proposed NILC schemes converge to zero along the iteration axis. Numerical experiments are carried out for validity and effectiveness.

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“…Compared with standard control scheme, the distinguishing feature of the ILC dynamic sequence of operations is to use the information from previous trials to update the control signal applied on the next one; the major advantage of ILC 2 Mathematical Problems in Engineering is the ability to improve system performance from trial to trial and include temporal information from previous trials that would be noncausal in standard systems. Over the past few decades, ILC has drawn significant research attention and increasingly been employed in many industrial processes, such as traffic system [13], networked stochastic system [14], robotic manipulator system [15], multiagent system [16], chemical pharmaceutical crystallization [17], and industrial injection molding batch processes [18].…”

Section: Introductionmentioning

confidence: 99%

Output Information Based Fault-Tolerant Iterative Learning Control for Dual-Rate Sampling Process with Disturbances and Output Delay

Tao

Liu

Yang

2018

Mathematical Problems in Engineering

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For a class of single-input single-output (SISO) dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D) repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs) technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method.

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Iterative learning control for networked stochastic systems with random packet losses

Cited by 21 publications

References 12 publications

Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides

Networked Iterative Learning Control Design for Nonlinear Systems with Stochastic Output Packet Dropouts

Output Information Based Fault-Tolerant Iterative Learning Control for Dual-Rate Sampling Process with Disturbances and Output Delay

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