@article{Trieu Minh_Tamre_Musalimov_Kovalenko_Rubinshtein_Ovchinnikov_Moezzi_2020, title={Model Predictive Control for Modeling and Simulation of Human Gait Motions}, volume={3}, url={https://journals.tultech.eu/index.php/ijitis/article/view/41}, DOI={10.15157/IJITIS.2020.3.1.326-345}, abstractNote={<p>Human motion is a complex activity of the central nervous system (CNS) and muscles. Performance of a human motion can be decomposed into three components: estimation of trajectory; calculation of required signal for muscles; and performance of movement. The CNS conducts the first two tasks and the muscles perform the third task. This paper presents the development of a mathematical model and a Matlab Simulink plant for human gait movement. An internal model predictive control (MPC) is setup and plays as the human CNS to estimate the trajectory and to calculate the required signal for muscles to perform the movement. MPC calculates the required torques for each joint and generate optimal trajectories subject to human physical constraints for muscles. Results of simulation are analyzed and compared to the real human gait motions captured by a real motion capture system (Vicon). Finally, conclusions and recommendations from this research are withdrawn.</p>}, number={1}, journal={International Journal of Innovative Technology and Interdisciplinary Sciences}, author={Trieu Minh, Vu and Tamre, Mart and Musalimov, Victor and Kovalenko, Pavel and Rubinshtein, Irina and Ovchinnikov, Ivan and Moezzi, Reza}, year={2020}, month={Jan.}, pages={326–345} }