Reinforcement Learning-Based Model-Free Controller for Feedback Stabilization of Robotic Systems.

This article presents a reinforcement learning (RL) algorithm for achieving model-free control of robotic applications. The RL functions are adapted with the least-square temporal difference (LSTD) learning algorithms to develop a model-free state feedback controller by establishing linear quadratic regulator (LQR) as a baseline controller. The classical least-square policy iteration technique is adapted to establish the boundary conditions for complexities incurred by the learning algorithm.

New cadmium(II)-selective electrode based on a tetraazacyclohexadeca macrocyclic ionophore.

A poly(vinyl chloride)-based membrane of 3,4:11,12-dibenzo-1,6,9,14-tetraazacyclohexadecane with sodium tetraphenyl borate (NaTPB) as an anion excluder and dibutylphthalate (DBP), dibutyl(butyl)phosphonate (DBBP), tris(2-ethylhexyl)phosphate (TEHP) and tributyl phosphate (TBP) as a plasticizing solvent mediator was prepared and investigated as a Cd2+-selective electrode. The best performance was observed with the membrane having a ligand-PVC-DBP-NaTPB composition of 2:25:60:1, which worked well over a wide concentration range (1.6 x 10(-6) - 1.

A new macrocyclic polystyrene-based sensor for chromium (III) ions.

A new tetradentate dihydrogen perchlorate macrocyclic ligand (2,4,9,11-tetraphenyl-1,5,8,12-tetraazacyclotetradeca-1,4,8,11-tetraene dihydrogen perchlorate) was prepared and characterised. The macrocycle behaves as a selective chelating ion-exchanger for some metal ions. The polystyrene-based membrane electrode is found to exhibit quite promising selectivity for Cr3+ ions.