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Robust control and adaptive robust control of robot manipulators.

机译：机器人操纵器的鲁棒控制和自适应鲁棒控制。

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This dissertation proposes new approaches to uncertainty compensation in the applications to control of uncertain robot manipulators. The uncertainties considered here can be attributed to parameters which are unknown or cannot be measured exactly, to stick-slip friction which is highly nonlinear and hard to describe exactly, or to unmodeled external disturbances. This dissertation consists of two parts: Part I, Robust Control of Uncertain Robot Manipulators and Part II, Adaptive Robust Control of Uncertain Robot Manipulators.; Part I investigates new robust approaches to some common robotic operations, including positional regulation, trajectory tracking, motion/force control, and multi-robot coordination. In each case the uncertainties due to unknown parameters and friction, as well as the external disturbances can be compensated as long as their upper bounds are known. The approaches employ a novel nonlinear robust compensator--the time-varying smooth robust compensator. Unlike other commonly used robust compensators, the proposed one can simultaneously ensure smooth control input and asymptotic stability of the closed-loop system. Therefore, the control system employing such a smooth robust compensator has a wide range of applications, including space tasks and high precision manufacturing, where both motion smoothness and positioning accuracy are imperative.; In Part II, new approaches to compensate uncertainties are developed by using both adaptive control and robust control methods, combining their strengths and eliminating their disadvantages. The smooth adaptive robust friction compensation approach presented in chapter 5 relaxes the requirement of known upper bounds on uncertainties. In chapter 6, the integrated adaptive-robust control approach, which is able to reduce the conservatism of robust control and improve transient performance, is developed and applied to tracking control of a 1-DOF robotic system. In chapter 7, the algorithm is extended to tracking control of a n-DOF robot manipulator with smooth adaptive robust friction compensation. The new approach offers the ability to learn, to handle various uncertainties, to reject external disturbances, to ensure smooth control input, to reduce the conservatism of robust control action, to improve transient performance, and to achieve global asymptotic stability.
机译：本文提出了不确定性补偿在不确定机器人控制中的新方法。此处考虑的不确定性可归因于未知或无法精确测量的参数，高度非线性且难以准确描述的粘滑摩擦或未建模的外部干扰。本文由两部分组成：第一部分，不确定机器人的鲁棒控制；第二部分，不确定机器人的自适应鲁棒控制。第一部分研究了一些常见的机器人操作的新鲁棒方法，包括位置调节，轨迹跟踪，运动/力控制和多机器人协调。在每种情况下，只要已知其上限，就可以补偿由于未知参数和摩擦而导致的不确定性以及外部干扰。这些方法采用了一种新型的非线性鲁棒补偿器-时变平滑鲁棒补偿器。与其他常用的鲁棒补偿器不同，所提出的补偿器可同时确保闭环系统的平滑控制输入和渐近稳定性。因此，采用这种平滑鲁棒补偿器的控制系统具有广泛的应用，包括空间任务和高精度制造，其中运动平滑性和定位精度都是必须的。在第二部分中，通过使用自适应控制和鲁棒控制方法，结合了它们的优点和消除了它们的缺点，开发了补偿不确定性的新方法。第5章介绍的平滑自适应鲁棒摩擦补偿方法放宽了对不确定性的已知上限的要求。在第6章中，开发了一种集成的自适应鲁棒控制方法，该方法能够减少鲁棒控制的保守性并改善瞬态性能，并将其应用于1-DOF机器人系统的跟踪控制。在第7章中，该算法扩展到具有平滑自适应鲁棒摩擦补偿的n-DOF机器人操纵器的跟踪控制。新方法提供了学习，处理各种不确定性，拒绝外部干扰，确保平滑控制输入，减少鲁棒控制动作的保守性，改善瞬态性能以及实现全局渐近稳定性的能力。

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