Autonomous driving paper index
Coordination dynamics and model-based neural network synchronous controls for redundantly full-actuated parallel manipulator
One-line summary
Abstract Redundantly full-actuated parallel manipulator takes number of actuations exceeding its degree of freedom, and actuation coordination makes its basis of stable operation.
Engineering notes
Key topics: autonomous driving, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract Redundantly full-actuated parallel manipulator takes number of actuations exceeding its degree of freedom, and actuation coordination makes its basis of stable operation. This paper studies the coordination dynamics of general redundantly full-actuated parallel manipulator and derives coordination dynamics models for driving force coordination and internal force regulation respectively. Associated with coordination dynamics models, two neural network synchronous control methods are proposed for each situation correspondingly. Self-learning synchronous algorithms for those methods are designed additionally. Manipulator 6 P US+UPU is taken as a prototype for co-simulations and experiments. Results reveal that the two methods proposed above could improve actuation coordination and internal force precision of redundantly full-actuated parallel manipulator respectively. This paper provides new dynamics-based control methods for the research and control application of parallel manipulator with redundant actuation.
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