Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motio2-s2.0-85126069392ns for humanoid robots. However, the effect of uncertainties on constraints satisfaction has only been studied using Robust MPC (RMPC) approaches, which account for the worst-case realization of bounded disturbances at each time instant. In this paper, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. We show that SMPC offers more flexibility to the user (or a high level decision maker) by tolerating small (user-defined) probabilities of constraint violation. Therefore, SMPC can be tuned to achieve a constraint satisfaction probability that is arbitrarily close to 100%, but without sacrificing performance as much as tube-based RMPC. We compare SMPC against RMPC in terms of robustness (constraint satisfaction) and performance (optimality). Our results highlight the benefits of SMPC and its interest for the robotics community as a powerful mathematical tool for dealing with uncertainties.
Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance / Gazar, Ahmad; Khadiv, Majid; Del Prete, Andrea; Righetti, Ludovic. - (2021), pp. 61-68. (Intervento presentato al convegno HUMANOIDS 2020 tenutosi a Munich nel 19th-21st July 2021) [10.1109/humanoids47582.2021.9555783].
Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance
Gazar, Ahmad
Primo
;Del Prete, AndreaPenultimo
;
2021-01-01
Abstract
Linear Model Predictive Control (MPC) has been successfully used for generating feasible walking motio2-s2.0-85126069392ns for humanoid robots. However, the effect of uncertainties on constraints satisfaction has only been studied using Robust MPC (RMPC) approaches, which account for the worst-case realization of bounded disturbances at each time instant. In this paper, we propose for the first time to use linear stochastic MPC (SMPC) to account for uncertainties in bipedal walking. We show that SMPC offers more flexibility to the user (or a high level decision maker) by tolerating small (user-defined) probabilities of constraint violation. Therefore, SMPC can be tuned to achieve a constraint satisfaction probability that is arbitrarily close to 100%, but without sacrificing performance as much as tube-based RMPC. We compare SMPC against RMPC in terms of robustness (constraint satisfaction) and performance (optimality). Our results highlight the benefits of SMPC and its interest for the robotics community as a powerful mathematical tool for dealing with uncertainties.File | Dimensione | Formato | |
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