This paper addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by plant uncertainties and external disturbances. Adaptive neural network systems are employed to approximate the behaviors of uncertain mechanical and electrical dynamics. A reduced-order observer is constructed to estimate the velocity signals. Only the measurements of link position and armature current are required for feedback. Consequently, an adaptive neural network-based dynamic feedback tracking controller without velocity measurements is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking errors can be made as small as possible. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics