CAREER: Toward Unified Nonlinear Dynamics and Control of Robot Systems

这个研究项目追求两个互补的思想，以合成具有特殊而灵活的动态灵活性的机器人：(I)精确的物理模型和（ii）自适应学习。第一个想法是将机器人及其环境的精确物理模型整合到机器人的计算机控制系统中。通过了解其肢体的完整物理动力学模型，基于模型的机器人控制器可以自动补偿运动过程中作用于机器人的非线性动力学效应（例如惯性、科里奥利、摩擦、流体动力和重力）。第二个想法是让机器人定量地学习自身物理结构和环境的参数，并自适应地补偿这些参数的变化。其结果是卓越的速度，精度和灵活性。机器人手臂控制的理论问题（第1.3节）与水下航行器的位置问题（第1.4节）非常相似——工业机器人和水下航行器都是二阶拉格朗日系统，表现出高度结构化的非线性动力学。这些思想将应用于以下两个现实问题领域：工业机械臂的非线性自适应“学习”控制和基于模型的水下机器人控制

9625143 Whitcomb This program of research pursues two complementary ideas toward the synthesis of robots possessing exceptional yet flexible dynamical dexterity: (I) precise physical models and (ii) adaptive learning. The first idea is to incorporate precise physical models of the robot and its environment into a robot's computer control system. By knowing the full physical dynamical model of its limbs, a model-based robot controller can automatically compensate for the nonlinear dynamical effects (e.g., inertial, Coriolis, friction, hydrodynamic, and gravitational forces) acting on the robot during motion. The second idea is for robots to quantitatively learn the parameters of their own physical structure and their environment, and to adaptively compensate for changes in these parameters. The result is superior speed, precision, and dexterity. Theoretical problems in robot arm control (Section 1.3) are remarkably similar to the problems of underwater vehicle position (Section 1.4) -- both industrial robots and underwater vehicles are second-order Lagrangian systems exhibiting highly structured nonlinear dynamics. These ideas are to be employed in these two real-world problem domains: Nonlinear Adaptive "Learning" Control for Industrial Robot Arms Nonlinear Model-Based Control of Underwater Robot Vehicles Education Innovation in Robotics Teaching ***