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

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 both parametric and nonparametric uncertainties, while full constraints are considered enhancing operational safety. To ensure that system states within the predefined bounds, barrier Lyapunov function (BLF) used analysis, which reaches infinity when some its arguments approach limits. The proposed ILC incorporates BLF composite energy (CEF) ensures boundedness CEF closed-loop, thus, assuring those limits not transgressed. Through rigorous show under scheme, uniform convergences errors guaranteed. Simulation studies experimental validations conducted to illustrate efficacy scheme. Experimental results satisfies constraint requirements error less than 2.5% desired trajectory.