Training task planning-based adaptive assist-as-needed control for upper limb exoskeleton using neural network state observer.

To improve the motivation and enthusiasm of subjects during active rehabilitation training, this paper proposes a novel training task planning-based adaptive assist-as-needed (TTP-AAAN) control algorithm for an upper limb exoskeleton. The overall controller contains an outer control loop to determine the required assistive force, and an inner control loop to drive the exoskeleton to track subject motion and to provide desired assistive force obtained from the outer control loop. In the outer control loop, a motion intention and task performance evaluation (MITPE) strategy is established to learn the motor capability of the subject. Based on the obtained evaluation result, the radius and frequency of multi-periodic trajectory tracking task, and the gain of the assistive force are adaptively adjusted by using the adaptive central pattern generator (ACPG) algorithm. Then, in the inner control loop, an asymmetric barrier Lyapunov function-based adaptive output feedback (ABLF-AOF) controller, in combination with a neural network (NN) state observer, is developed. The exoskeleton tracking errors are constrained by the asymmetric barrier Lyapunov function, and the state variables and uncertainty terms of the exoskeleton are simultaneously estimated by the NN state observer. Experiments are carried out with an upper limb exoskeleton to demonstrate the effectiveness of the proposed control strategy. The experimental results show that the developed control scheme can provide assistance and achieve task parameter adaption for the subjects with different motion patterns. In addition, the proposed controller has better training performance than task performance-based adaptive velocity assist-as-needed (AAN) controller and minimal AAN controller. [ABSTRACT FROM AUTHOR]