A comparison between virtual constraint-based and model predictive-based limit cycle walking control in successful trip recovery

Document Type : Research Article


Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran.


Falling is one of the main causes of the injuries among healthy adults. The foremost causes of the falls are: slipping and tripping. Understanding the phenomenon of human balance recovery against these disturbances is a very important issue in the field of biomechanics as well as in the robotics. Previous studies have shown that human or animal movements can be reproduced (predicted) using engineering techniques and computational facilities. The prediction of movements can be related to an optimization problem. In the present study, control and prediction of human movements in successful trip recovery are addressed. To formulate the optimization problem, a hybrid dynamic model of the human body with seven degrees of freedom is considered. The tripping perturbation is modeled as an instantaneous contact of the swing leg with an obstacle and the dynamics of impact are derived. Two optimization based methods are used to control and predict the gait: (i) virtual constraint-based limit cycle optimization (VCLCO) (ii) model predictive based limit cycle optimization (MPLCO). The simulated results are compared with the human-observed experimental data from the literature. The results show that the MPLCO method provides more human-like predictions than the VCLCO method in the kinematic level. MPLCO can predict proper actions to keep away violating constraints in the future. The theoretical results are in agreement with the results of experimental studies on movement adjustments during trip recovery.


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