Abstract
The new trajectory regulating model reference adaptive controller (TRMRAC) has been proposed in this brief. The intermediate reference model in the TRMRAC is self-regulated to enhance the stability and robustness of adaptation, with the trajectories of the controlled system proven to be ultimately uniformly bounded. The developed controller is implemented and simulated in a multivariable robotic arm system with its system dynamics approximated by a neural network, showing superior stability characteristics even under unmodeled actuator dynamics and input saturation. To demonstrate its practicality, a nested version of the controller was tested on a quadcopter for quaternion attitude tracking, showing enhanced robustness over the conventional model reference adaptive control strategy.
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