Investigation of Adaptive Controllers for Puma Trajectory Tracking.

摘要

Two robust model-based controllers and two decentralized adaptive controllers are experimentally evaluated. Algorithm evaluation is motivated by the need for controllers with good high speed tracking under varying payload conditions. The test case is a PUMA-560 robotic manipulator operating over the standard test suite. The model-based controllers are made robust by the addition of either an auxiliary input term or an adaptive feedforward compensator based on Lyapunov Theory. The model-based auxiliary input controller (MBAIC) adapts the gain matrices used in computing an additional torque to be combined with model-based feedforward and PD feedback torques. The adaptive model-based controllers adapt the assessment of the manipulator parameters used in calculating feedforward torque. The decentralized adaptive controllers are based on either Lyapunov stability or Popov hyperstability. These controllers calculate feedforward, feedback, and auxiliary torques based on trajectory errors and desired trajectory parameters. The gain matrices used to multiply these quantities are adapted. These auxiliary torque components are identical to those used in the MBAICs. Experimental evaluation provide insight into the potential and limitations of each method. The decentralized digital adaptive control algorithms produce an unsatisfactory tracking response. Both model-based control techniques improve the manipulator's tracking response.