Method to filter the updated input of P-type ILC controllers with communication delays and noises

Huang, Lixun; Guo, Xinyang; Sun, Lijun; Zhang, Qiuwen; Liu, Weihua; Zhang, Zhe

doi:10.1080/00207721.2023.2249161

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2024

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Cited by 3 publications

References 34 publications

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Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

Le,

Shen,

Sun

et al. 2024

2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

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Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

Le,

Shen,

Sun

et al. 2024

2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

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A gradient-descent iterative learning control algorithm for a non-linear system

He,

2024

Transactions of the Institute of Measurement and Control

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Original iterative learning control (OILC) has been proved a powerful tool in dealing with the model-free control problems by repetitively corrections based on the control error. However, the steady-state error under widely-used proportional-type original iterative learning control (P-type OILC) is highly corresponded to the proportional learning gain, making the algorithm parameter-determined. Therefore, a new gradient-descent iterative learning control (GDILC) algorithm is proposed to achieve a parameter-free approach by simulating the gradient-descent process. First, GDILC problem is formulated mathematically. Next, the idea of the algorithm is proposed, the analyses of the convergence and the steady-state error are conducted and the algorithm is implemented. GDILC will generate a random correction with a gradient-descent upper bound, rather than a correction proportional to the error in P-type OILC. Finally, illustrative and application simulations are conducted to validate the algorithm. Results show that the algorithm will be convergent after adequate iterations under proper corrections. The steady-state error will be less affected by the algorithm parameters under GDILC than that under OILC.

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Convergence of a State Feedback-based Iterative Learning Control Scheme for Nonlinear Systems with Random Data Dropouts

Zhang,

Liu

2024

2024 43rd Chinese Control Conference (CCC)

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Method to filter the updated input of P-type ILC controllers with communication delays and noises

Cited by 3 publications

References 34 publications

Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

A gradient-descent iterative learning control algorithm for a non-linear system

Convergence of a State Feedback-based Iterative Learning Control Scheme for Nonlinear Systems with Random Data Dropouts

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Method to filter the updated input of P-type ILC controllers with communication delays and noises

Cited by 3 publications

References 34 publications

Finite Horizon ℋ∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

Finite Horizon ℋ∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

A gradient-descent iterative learning control algorithm for a non-linear system

Convergence of a State Feedback-based Iterative Learning Control Scheme for Nonlinear Systems with Random Data Dropouts

Contact Info

Product

Resources

About

Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol

Finite Horizon ℋ_∞ State Estimation with Packet Losses over Half-Duplex Relay Networks : Handling Amplify-and-Forward Protocol