In this paper,a framework of model predictive optimization and control for quadruped whole-body locomotion is presented,which enables dynamic balance and minimizes the control effort.First,we propose a hierarchical co...In this paper,a framework of model predictive optimization and control for quadruped whole-body locomotion is presented,which enables dynamic balance and minimizes the control effort.First,we propose a hierarchical control scheme consisting of two modules.The first layer is to find an optimal ground reaction force(GRF)by employing inner model predictive control(MPC)along a full motor gait cycle,ensuring the minimal energy consumption of the system.Based on the output GRF of inner layer,the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control.In inner MPC,an objective function about GRF is designed by using a model with relatively long time horizons.Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function.By using a two-layered MPC architecture,we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation.Finally,we perform extensive experiments with a quadruped robot,including the crawl and trotting gaits,to verify the proposed control framework.展开更多
This study proposes a novel adaptive neural dynamic-based hybrid control strategy for stable subsatellite retrieval of two-body tethered satellite systems.The retrieval speed is given analytically,ensuring a libration...This study proposes a novel adaptive neural dynamic-based hybrid control strategy for stable subsatellite retrieval of two-body tethered satellite systems.The retrieval speed is given analytically,ensuring a libration-free steady state.To mitigate the potential libration motion,a general control input signal is generated by an adaptive neural-dynamic(AND)algorithm and executed by adjusting the retrieval speed and thruster on the subsatellite.To address the limited retrieval speed and improve the control performance,the thruster controller is manipulated according to a novel advanced state fuzzy control law based on higher-order libration states,whereas the remaining control input is allocated to the speed controller.The Lyapunov stability of the control strategy is demonstrated analytically.Numerical simulations validate the proposed control strategy,demonstrating well-allocated control inputs for both controllers and good control performance.展开更多
基金supported in part by the National Natural Science Foundation of China(62133013,U22A2060)Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM01Z024)。
文摘In this paper,a framework of model predictive optimization and control for quadruped whole-body locomotion is presented,which enables dynamic balance and minimizes the control effort.First,we propose a hierarchical control scheme consisting of two modules.The first layer is to find an optimal ground reaction force(GRF)by employing inner model predictive control(MPC)along a full motor gait cycle,ensuring the minimal energy consumption of the system.Based on the output GRF of inner layer,the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control.In inner MPC,an objective function about GRF is designed by using a model with relatively long time horizons.Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function.By using a two-layered MPC architecture,we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation.Finally,we perform extensive experiments with a quadruped robot,including the crawl and trotting gaits,to verify the proposed control framework.
基金funded by the National Natural Science Foundation of China(Grant No.12102487)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515012339)Shenzhen Science and Technology Program(Grant No.ZDSYS20210623091808026)。
文摘This study proposes a novel adaptive neural dynamic-based hybrid control strategy for stable subsatellite retrieval of two-body tethered satellite systems.The retrieval speed is given analytically,ensuring a libration-free steady state.To mitigate the potential libration motion,a general control input signal is generated by an adaptive neural-dynamic(AND)algorithm and executed by adjusting the retrieval speed and thruster on the subsatellite.To address the limited retrieval speed and improve the control performance,the thruster controller is manipulated according to a novel advanced state fuzzy control law based on higher-order libration states,whereas the remaining control input is allocated to the speed controller.The Lyapunov stability of the control strategy is demonstrated analytically.Numerical simulations validate the proposed control strategy,demonstrating well-allocated control inputs for both controllers and good control performance.
