Perturbation Observer-Based Dynamic Event-Triggered $H_\infty$ Impedance Control for Flexible Exoskeletons via Concurrent Learning

This article presents an enhanced dynamic event-triggered (DET) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> impedance control approach for a flexible exoskeleton robot, which incorporates a concurrent learning (CL) mechanism. The specified control objective is to regulate the impedance model of the exoskeleton to ensure robustness and stability under external perturbations. To achieve this, a fictitious desired input is introduced, and a novel impedance-based perturbation observer is developed, which involves the impedance of the system to estimate the unknown perturbations. Different from the conventional impedance control method, the feedforward learning control algorithms are introduced, and the specified impedance characteristic within the system is controlled by a hybrid control scheme that combines a DET <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> optimal control method with a CL mechanism. The unmatched parameter uncertainty is modeled and addressed through an auxiliary input. Moreover, for implementing the DET-CL policy, a single critic neural network is employed. During the learning process, both recorded and transient data are employed for relaxing the persistence of excitation condition. The stability of the entire flexible exoskeleton system is demonstrated. Finally, the proposed algorithm's effectiveness is illustrated through simulations and experimental results.