Practical Approaches for Robot Dynamic Model Implementation for Control and Simulation Purposes

A robot dynamic model is time variable, highly non-linear and characterized by coupling effects among the robot joints. Consequently, a derivation and implementation of a robot dynamic model, which is used for purposes of control, simulation, and mechanical design, often represents a challenging task. The last couple of decades saw a great amount of research with the aim to achieve better ease of use (development) and computational efficiency of robot dynamic algorithms. Recently, general-purpose robot modeler/simulator software that enables numerical calculation of robot inverse dynamics problem for user-developed robot model and input joint trajectories are being increasingly used by a wider range of robot developers for robot control purposes. In this study, two different practical approaches to account for robot dynamics for purposes of robot control, trajectory generation, mechanical design and simulation, are discussed. The first approach includes an efficient solution for forward dynamics using a novel modified recursive Newton–Euler algorithm, which is used for simulation, mechanical design, and trajectory generation. The second approach is based on modern software tools usage, for the purposes of simulation and control. Both strategies for implementation of robot dynamic model are based on developed 3D models of robots in CAD software and 3D modelers. Applied approaches are demonstrated in three different case studies. Discussion on the benefits of the presented approaches is given

4-Arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects

In a routine screening of our small-molecule compound collection we recently identified 4-arylazo-3,5-diamino-1H-pyrazoles as a novel group of ATP antagonists with moderate potency against CDK2-cyclin E. A preliminary SAR study based on 35 analogues suggests ways in which the pharmacophore could be further optimized, for example, via substitutions in the 4-aryl ring. Enzyme kinetics studies with the lead compound and X-ray crystallography of an inhibitor-CDK2 complex demonstrated that its mode of inhibition is competitive. Functional kinase assays confirmed the selectivity toward CDKs, with a preference for CDK9-cyclin T1. The most potent inhibitor, 4-[(3,5-diamino-1H-pyrazol-4-yl) diazenyl]phenol 31b (CAN508), reduced the frequency of S-phase cells of the cancer cell line HT-29 in antiproliferation assays. Further observed cellular effects included decreased phosphorylation of the retinoblastoma protein and the C-terminal domain of RNA polymerase II, inhibition of mRNA synthesis, and induction of the tumor suppressor protein p53, all of which are consistent with inhibition of CDK9. © 2006 American Chemical Society

Empirical mode decomposition of pressure signal for health condition monitoring in waterjet cutting

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach