Model-free control” and the corresponding “intelligent” PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques becom...

State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance. Humans, on the other hand, can very quickly exploit highly rewarding nuances of an environment upon first discovery. In the brain, such rapid learning is thought to depend on the hippocampus and its capacity for episodic memory. Here we investigate whether a simple model...

Model predictive control, MPC, is a model-based control philosophy that select control actions by on-line optimization of objective functions. Design methods based on MPC have found wide acceptance in industrial process control applications, and have been thoroughly studied by the academia. Most of the work so far have relied on linear models of different sophistication because of their advanta...

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...

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...