Optimization of Driving Energy Consumption for Wearable Industrial Lower Limb Exoskeleton Based on Improved Chameleon Algorithm and Human-machine Dynamics

Abstract

In order to optimize the range of the wearable industrial lower limb exoskeleton during operation, firstly, the kinematic analysis of the lower limb exoskeleton is carried out, and the driving force model of each joint is derived through Lagrange ’s equation. Secondly, the motion stability constraint is derived by combining ZMP theory and D’Alembert’s principle. Finally, an optimization model with the optimal joint driving energy consumption as the objective function is constructed based on the continuous periodic motion of the lower limb exoskeleton. An improved Chameleon Swarm Algorithm (TNECSA) based on Tent chaos mapping, Niching behavior, and elite perturbation mechanism is designed to solve the model. Firstly, the Tent chaos mapping is used to generate high-quality initial feasible solutions. The small habitat technique is introduced to maintain the diversity of the population and expand the search range. Based on the elite perturbation mechanism, the elite individuals are perturbed by the sine cosine search operator to avoid the algorithm from falling into the local optimum. The designed algorithm is compared with other cutting-edge population intelligence algorithms in cross-sectional simulations respectively. The results validate the feasibility of the developed model and the improved algorithm in the human-machine dynamics optimization problem of exoskeleton robots.