We present a robust adaptive model predictive control (MPC) framework for nonlinear continuous-time systems with bounded parametric uncertainty and additive disturbance. utilize general contraction metrics (CCMs) to parameterize homothetic tube around nominal prediction that contains all uncertain trajectories. Furthermore, we incorporate adaptation using set-membership estimation. As result, t...

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling demonstrated skills. To reproduce a learned skill with real robot, feedback controller is required to cope perturbations and react dynamic changes in environment. this letter, we propose generalized control approach that merges movemen...

This short note points out an improvement on the robust stability analysis for electrically driven robots given in the paper. In the paper, the author presents a FAT-based direct adaptive control scheme for electrically driven robots in presence of nonlinearities associated with actuator input constraints. However, he offers not suitable stability analysis for the closed-loop system. In other w...