Frontiers of Information Technology & Electronic Engineering
>> 2020,
Volume 21,
Issue 5
doi:
10.1631/FITEE.1900418
Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks
Affiliation(s): Department of Automation, Tsinghua University, Beijing 100084, China; Department of Electrical and Computer Engineering, Florida International University, Miami 33174, USA; Move Robotics Technology Co., Ltd., Shanghai 201306, China; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; less
Received: 2019-08-21
Accepted: 2020-05-18
Available online: 2020-05-18
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Abstract
Accurate acceleration acquisition is a critical issue in the system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and s. To this end, a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme, a novel exponential-type is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover, a fractional power sliding mode control law is designed to realize , where the convergence time is irrelevant to initial states or external disturbance, and depends only on the chosen parameters. To further enhance observer robustness, an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.
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