Cooperative Locomotion Via Supervisory Predictive Control and Distributed Nonlinear Controllers
Cooperative Locomotion Via Supervisory Predictive Control and Distributed Nonlinear Controllers
复制标题
通过监督预测控制和分布式非线性控制器进行协作运动
DOI：
10.1115/1.4052917
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Akbari Hamed, Kaveh
中科院分区：
文献类型：
--
作者：
Kim, Jeeseop;Akbari Hamed, Kaveh
本文提出了一种分层的非线性控制算法的实时规划和控制的腿式机器人，协作携带物体的合作运动。提出了一种新的网络降阶模型，称为互联线性倒立摆（LIP）动力学，研究合作运动。所提出的算法的更高层次采用了监督控制器，基于事件的模型预测控制（MPC），有效地计算最佳的降阶轨迹的互连LIP动态。所提出的算法的较低层次采用分布式非线性控制器，以减少减少和全阶复杂模型的合作运动之间的差距。特别是，分布式控制器的开发基于二次规划（QP）和虚拟约束施加的全阶动力学模型的每个代理渐近跟踪降阶轨迹，同时在腿端有可行的接触力。本文数值研究所提出的控制算法的有效性，通过全阶模拟的一组协作四足机器人，每个共22度的自由。本文最后研究了所提出的控制算法对有效载荷质量和地面高度分布变化的不确定性的鲁棒性。数值研究表明，合作代理可以运输未知的有效载荷，其质量高达57%，97%和137%的单代理的质量与一队的两条腿，三条腿和四条腿的机器人。
This paper presents a hierarchical nonlinear control algorithm for the real-time planning and control of cooperative locomotion of legged robots that collaboratively carry objects. An innovative network of reduced-order models subject to holonomic constraints, referred to as interconnected linear inverted pendulum (LIP) dynamics, is presented to study cooperative locomotion. The higher level of the proposed algorithm employs a supervisory controller, based on event-based model predictive control (MPC), to effectively compute the optimal reduced-order trajectories for the interconnected LIP dynamics. The lower level of the proposed algorithm employs distributed nonlinear controllers to reduce the gap between reduced- and full-order complex models of cooperative locomotion. In particular, the distributed controllers are developed based on quadratic programing (QP) and virtual constraints to impose the full-order dynamical models of each agent to asymptotically track the reduced-order trajectories while having feasible contact forces at the leg ends. The paper numerically investigates the effectiveness of the proposed control algorithm via full-order simulations of a team of collaborative quadrupedal robots, each with a total of 22 degrees-of-freedom. The paper finally investigates the robustness of the proposed control algorithm against uncertainties in the payload mass and changes in the ground height profile. Numerical studies show that the cooperative agents can transport unknown payloads whose masses are up to 57%, 97%, and 137% of a single agent's mass with a team of two, three, and four legged robots.