Intelligent Control of Robot Manipulator

  • Chenguang YangEmail author
  • Hongbin MaEmail author
  • Mengyin Fu


This chapter presents a series of intelligent control schemes for robot manipulator control. A controller using fuzzy logic to infer the learning parameters is developed to improve the performance of the dual-adaptive controller. A new task space/joint space hybrid control scheme for bimanual robot with impedance and force control is also introduced. The task space controller adapts end-point impedance, to compensate for interactive dynamics, and the joint space controller adapts the impedance to improve robustness against external disturbances. An adaptive model reference control is designed for robots to track desired trajectories and for the closed-loop dynamics to follow a reference model, which is derived using the LQR optimization technique to minimize both the motion tracking error and the transient acceleration for a smooth trajectory. A new discrete-time adaptive controller for robot manipulator with uncertainties from the unknown or varying payload is introduced, based on the idea of one-step guess. The history information is used to estimate the unknown fixed or time-varying payload on the end effector.


Adaptive Discrete-time Controller Task Space Controller Tracking Error Desired Reference Trajectory Unknown Payload 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Science Press and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Key Lab of Autonomous Systems and Networked Control, Ministry of EducationSouth China University of TechnologyGuangzhouChina
  2. 2.Centre for Robotics and Neural SystemsPlymouth UniversityDevonUK
  3. 3.School of AutomationBeijing Institute of TechnologyBeijingChina
  4. 4.State Key Lab of Intelligent Control and Decision of Complex SystemBeijing Institute of TechnologyBeijingChina

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