Adaptive Neural Network Control with Fuzzy Compensation for Upper Limb Exoskeleton in Active Spacesuit

Abstract

In order to meet the maneuverability requirements of spacesuits for future manned planetary exploration, the concept of an active spacesuit based on the joint-assisted exoskeleton technology is presented. First, by studying the kinematic characteristics of the operator wearing the simulated spacesuit in different gravity environments, we developed a prototype of the upper limb exoskeleton. Then, the resistance moment of the simulated spacesuit was roughly obtained to match the operator’s motion range, being utilized to design the resistance moment model. Considering the unknown resistance moment effects in the active spacesuit and the uncertainties of the exoskeleton’s dynamics model, an adaptive neural network control with fuzzy compensation was developed to drive the upper limb exoskeleton’s tracking desired trajectories. Experimental studies were carried out using an upper limb exoskeleton to illustrate that the proposed method has excellent trajectory tracking performance and good adaptive ability to the gravity environment changes.

Jiles-Atherton-Based Hysteresis Identification of Joint Resistant Torque in Active Spacesuit Using SA-PSO Algorithm

“…Among these, the structure effect is caused by the stretching, squeezing, and friction of the spacesuits' soft material, and the volume effect and pressure effect are caused by changes in the internal volume and pressure of the spacesuits [32]. Based on the previous work [10], we have measured the spacesuit resistant torque of the mock spacesuit by a single-joint measuring device, and the angle range of joints is needed be determined in advance. When the motion range of the shoulder joint and the elbow joint is set as 0.35-1.22 rad and 0.52-1.57 rad, the first-order transition curves, which are defined as bending from the minimum angle to different maximum angles (0.52 rad, 0.7 rad, 0.87 rad, and 1.05 rad and 0.87 rad, 1.05 rad, 1.22 rad, and 1.4 rad) and then stretching back to the same minimum angle (0.35 rad and 0.52 rad), are shown in Figure 2.…”

“…Update the inertia weight 𝜔 using (10), and the learning factors c 1 and c 2 using ( 11), (12) Set the temperature drop function T(k) using (11) Calculate the probability p i (k) using ( 14)…”

“…In order to eliminate the influence of joint resistant torque, some structure schemes have been proposed for spacesuits, such as the MCP (mechanical counter pressure) spacesuit [4,5], the power assist elbow [6], the X1 exoskeleton [7], the elbow-assisted exoskeleton [8], and the hard thigh-hip joint [9]. On the basis of those schemes, we have proposed the concept of active spacesuit [10], that is to say, a joint-assisted exoskeleton is directly worn outside spacesuits to enhance the operational capability, as well as assist astronauts to complete various maneuvering missions. Furthermore, it is well known that astronauts mainly rely on the upper limbs to complete the orbital missions, so active spacesuit designed in this paper would provide assistance to astronauts' upper limbs.…”

Model‐free based adaptive finite time control with multilayer perceptron neural network estimation for a 10 DOF lower limb exoskeleton

“…11 Researchers have also developed different control laws for exoskeletons, as reported in the literature. Some of these control laws are based on artificial intelligence tools such as neural networks, [12][13][14][15] fuzzy logic, 16 neuron-fuzzy, 17,18 sliding modes, 13,[19][20][21][22] Kalman filters, 23 backstepping, 13,[24][25][26] and PID. 27 Despite significant progress in exoskeleton research, several challenges are still encountered during their operation.…”

“…Researchers have also developed different control laws for exoskeletons, as reported in the literature. Some of these control laws are based on artificial intelligence tools such as neural networks, 12–15 fuzzy logic, 16 neuron‐fuzzy, 17,18 sliding modes, 13,19–22 Kalman filters, 23 backstepping, 13,24–26 and PID 27 …”