Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots
With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are s...
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MDPI AG
2025-01-01
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| author | Tianbo Yang Yuchuang Tong Zhengtao Zhang |
| author_facet | Tianbo Yang Yuchuang Tong Zhengtao Zhang |
| author_sort | Tianbo Yang |
| collection | DOAJ |
| description | With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model’s inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach. |
| format | Article |
| id | doaj-art-6e19aa52d7c94ad68bbc8d9de7cd8726 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-6e19aa52d7c94ad68bbc8d9de7cd87262025-01-24T13:24:39ZengMDPI AGBiomimetics2313-76732025-01-011013010.3390/biomimetics10010030Flexible Model Predictive Control for Bounded Gait Generation in Humanoid RobotsTianbo Yang0Yuchuang Tong1Zhengtao Zhang2Institute of Automation, Chinese Academy of Sciences, Beijing 100089, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100089, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100089, ChinaWith advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model’s inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach.https://www.mdpi.com/2313-7673/10/1/30gait generationflexible C-T modelstable inversionmodel predictive control |
| spellingShingle | Tianbo Yang Yuchuang Tong Zhengtao Zhang Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots Biomimetics gait generation flexible C-T model stable inversion model predictive control |
| title | Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots |
| title_full | Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots |
| title_fullStr | Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots |
| title_full_unstemmed | Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots |
| title_short | Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots |
| title_sort | flexible model predictive control for bounded gait generation in humanoid robots |
| topic | gait generation flexible C-T model stable inversion model predictive control |
| url | https://www.mdpi.com/2313-7673/10/1/30 |
| work_keys_str_mv | AT tianboyang flexiblemodelpredictivecontrolforboundedgaitgenerationinhumanoidrobots AT yuchuangtong flexiblemodelpredictivecontrolforboundedgaitgenerationinhumanoidrobots AT zhengtaozhang flexiblemodelpredictivecontrolforboundedgaitgenerationinhumanoidrobots |