Robust Iterative Learning Control for Pneumatic Muscle With Uncertainties and State Constraints

Kun Qian, Zhenhong Li, Samit Chakrabarty, Zhiqiang Zhang, Sheng Quan Xie

Research output: Contribution to journalArticlepeer-review


In this article, we propose a new iterative learning control (ILC) scheme for trajectory tracking of pneumatic muscle (PM) actuators with state constraints. A PM model is constructed in three-element form with both parametric and nonparametric uncertainties, while full state constraints are considered for enhancing operational safety. To ensure that system states are within the predefined bounds, the barrier Lyapunov function (BLF) is used in the analysis, which reaches infinity when some of its arguments approach limits. The proposed ILC incorporates the BLF with the composite energy function (CEF) approach and ensures the boundedness of CEF in the closed-loop, thus, assuring that those limits are not transgressed. Through rigorous analysis, we show that under the proposed ILC scheme, uniform convergences of PM state tracking errors are guaranteed. Simulation studies and experimental validations are conducted to illustrate the efficacy of the proposed scheme. Experimental results show that the proposed ILC satisfies the state constraint requirements and the tracking error is less than 2.5% of the desired trajectory.

Original languageEnglish
Pages (from-to)1802-1810
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Issue number2
Early online date22 Mar 2022
Publication statusPublished - 1 Feb 2023


  • Barrier Lyapunov function (BLF)
  • iterative learning control (ILC)
  • pneumatic muscle (PM)
  • state constraint


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