Design and Experimental Characterization of Artificial Neural Network Controller for a Lower Limb Robotic Exoskeleton

Abstract

This study aims to develop a lower limb robotic exoskeleton with the use of artificial neural networks for the purpose of rehabilitation. First, the PID control with iterative learning controller is used to test the proposed lower limb robotic exoskeleton robot (LLRER). Although the hip part using the flat brushless DC motors actuation has good tracking results, the knee part using the pneumatic actuated muscle (PAM) actuation cannot perform very well. Second, to compensate this nonlinearity of PAM actuation, the artificial neural network (ANN) feedforward control based on the inverse model trained in advance are used to compensate the nonlinearity of the PAM. Third, a particle swarm optimization (PSO) is used to optimize the PID parameters based on the ANN-feedforward architecture. The developed controller can complete the tracking of one gait cycle within 3.6 s for the knee joint. Among the three controllers, the controller of the ANN-feedforward with PID control (PSO tuned) performs the best, even when the LLRER is worn by the user and the tracking performance is still very good. The average Mean Absolute Error (MAE) of the left knee joint is 1.658 degrees and the average MAE of the right knee joint is 1.392 degrees. In the rehabilitation tests, the controller of ANN-feedforward with PID control is found to be suitable and its versatility for different walking gaits is verified during human tests. The establishment of its inverse model does not need to use complex mathematical formulas and parameters for modeling. Moreover, this study introduces the PSO to search for the optimal parameters of the PID. The architecture diagram and the control signal given by the ANN compensation with the PID control can reduce the error very well.

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

“…On the other hand, NNs have also been applied in the context of RC, albeit to a lesser extent compared to RL. These models are known for their capacity to approximate complex functions and effectively learn from high-dimensional data (Lin and Sie, 2023). When applied to RC tasks, NNs can capture intricate patterns and relationships in sensor data or articulation configurations, facilitating more nuanced and sophisticated control strategies.…”

“…Still, it remains relevant due to its ability to effectively classify data in binary setting, and thanks to the "kernel trick" it is possible also to classify elements that originally aren't linearly separable. Conversely, NN exhibits a more diverse range of applications in LC (Lin and Sie, 2023). NNs are well-suited for those tasks as they learn intricate relationships between input signals and locomotion types.…”

“…This study Lin and Sie (2023) focuses on the development of a lower-limb exoskeleton for rehabilitation purposes, using artificial neural networks to improve its control system. Firstly, the authors testes the proposed lower-limb robotic exoskeleton robot (LLRER) using a PID control with an iterative learning controller.…”

A systematical review on the control of lower-limb exoskeleton

“…However, various control theories and applications of intelligent exoskeletons directly affect the performance of exoskeleton robots. In recent years, many researchers focus on neural network, which is effectively optimized in dealing with complex systems and controlling the stability of the system [4]. Consequently, it is particularly critical to control and coordinates the exoskeleton interacting with humans.…”