Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 70, Issue 2, Pages 1802-1810Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2022.3159970
Keywords
Uncertainty; Trajectory; Muscles; Lyapunov methods; Convergence; Trajectory tracking; Safety; Barrier Lyapunov function (BLF); iterative learning control (ILC); pneumatic muscle (PM); state constraint
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This article proposes a new iterative learning control scheme for trajectory tracking of pneumatic muscle actuators with state constraints. By incorporating the barrier Lyapunov function with the composite energy function approach, the feasibility of boundedness and convergence of system states within the predefined limits are ensured.
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.
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