The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.

工业机器人应用日益严格的性能要求凸显了串行机器人的先进鲁棒控制设计的重要性，这些机器人通常会受到各种不确定性和外部干扰的影响。因此，本文提出并研究了在不确定串行机器人机械手的任务空间中鲁棒自适应模糊滑模控制器的设计和实现。滑模控制以其对系统参数变化和外部干扰的鲁棒性而著称，因此是实现串行机器人高精度控制任务的一种非常理想且具有成本效益的方法。所提出的控制器是基于模糊逻辑近似设计的，以实现高精度的轨迹跟踪，同时减弱不确定性的影响。在控制器中，高频不确定项通过模糊逻辑系统逼近，低频项根据参数自适应律实时自适应更新。所提出的控制算法的控制效力和有效性与最近提出的传统控制器进行了比较验证。测试结果表明，在相同的工作条件下，所提出的控制器具有更好的轨迹跟踪性能，并且比传统控制器更能抵抗大扰动。高频不确定项采用模糊逻辑系统逼近，低频项根据参数自适应律实时自适应更新。所提出的控制算法的控制效力和有效性与最近提出的传统控制器进行了比较验证。测试结果表明，在相同的工作条件下，所提出的控制器具有更好的轨迹跟踪性能，并且比传统控制器更能抵抗大扰动。高频不确定项采用模糊逻辑系统逼近，低频项根据参数自适应律实时自适应更新。所提出的控制算法的控制效力和有效性与最近提出的传统控制器进行了比较验证。测试结果表明，在相同的工作条件下，所提出的控制器具有更好的轨迹跟踪性能，并且比传统控制器更能抵抗大扰动。