Abstract
To achieve ideal force control of a functional autonomous exoskeleton, sensitivity amplification control is widely used in human strength augmentation applications. The original sensitivity amplification control aims to increase the closed-loop control system sensitivity based on positive feedback without any sensors between the pilot and the exoskeleton. Thus, the measurement system can be greatly simplified. Nevertheless, the controller lacks the ability to reject disturbance and has little robustness to the variation of the parameters. Consequently, a relatively precise dynamic model of the exoskeleton system is desired. Moreover, the human-robot interaction (HRI) cannot be interpreted merely as a particular part of the driven torque quantitatively. Therefore, a novel control methodology, so-called probabilistic sensitivity amplification control, is presented in this paper. The innovation of the proposed control algorithm is two-fold: distributed hidden-state identification based on sensor observations and evolving learning of sensitivity factors for the purpose of dealing with the variational HRI. Compared to the other state-of-the-art algorithms, we verify the feasibility of the probabilistic sensitivity amplification control with several experiments, i.e., distributed identification model learning and walking with a human subject. The experimental result shows potential application feasibility.
Highlights
In recent decades, with the dramatic progress in computing, control and sensing, significant types of prostheses, orthoses and exoskeletons have been designed and developed to assist and support human limbs for various tasks
The sensitivity amplification control (SAC) control scheme has provided a solution for the human performance augmentation exoskeletons
The Gaussian process has a substantial impact in many fields, the crucial limit is that the computation expense scales in O( N 3 ) for training, while regarding prediction, the computed expense is O( N 2 ) if caching the kernel matrix
Summary
With the dramatic progress in computing, control and sensing, significant types of prostheses, orthoses and exoskeletons have been designed and developed to assist and support human limbs for various tasks. The crucial advantage of cognitive-based sensors is that the pilot or the patient intention can still be read, even if the subject cannot provide necessary joint torques. These types of the sensor systems are widely utilized in rehabilitation and medical application. The sensors mounted between the pilot and the exoskeleton can simplify the control algorithm, a crucial issue that should be addressed is that the time-delay effect cannot be avoided. The sensitivity amplification control (SAC) control scheme has provided a solution for the human performance augmentation exoskeletons Such an algorithm suffers from weak robustness, and it barely destroys the control system.
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