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• Sensing and computational framework for estimating the nature of the drilled rock. • Force/velocity on bit computed using top-drive measurements. • Three algorithms: Seismic-While-Drilling, direct estimation, Neural networks. • Validation on simulations against a reliable model. • Extension to non-linear side forces, coupled axial-torsional oscillations. In this paper, we consider the axial motion of a directional multi-sectional drill-string. The drill-string dynamics are represented by a distributed dynamical model (wave equations) coupled with an ordinary differential equation at the downhole boundary (bit-rock interaction). The interaction between the drill-bit and rock can introduce severe vibrations in the drill-string and result in safety and performance issues. Consequently, the performance of drilling is interwoven with our knowledge of the subsurface. To address these problems, we propose a sensing and computational framework for estimating the drill-string dynamics and the specific intrinsic energy of the rocks while drilling. By exploiting the derived models' particular structure, we combine the drill-string dynamics modeling with top-drive hook-load (force) and hook-speed (velocity) measurements to estimate the force-on-bit without requiring the knowledge of the sub-surface. Then, we record and model the seismic radiation patterns of drill-bit rock interactions near the surface (i.e., seismic while drilling). Such an idea allows deriving an appropriate estimation of the rocks' intrinsic energy while drilling. We introduce two alternative rock property estimation algorithms based on direct parameter estimation and machine learning concepts to complete the analysis. The different approaches are tested and validated in simulations. We discuss their respective advantages and drawbacks. Finally, we show how to extend our methodologies in the presence of non-linear Coulomb friction terms and of coupled axial–torsional oscillations. [ABSTRACT FROM AUTHOR]

The paper presents a controller design for systems suffering from multi-harmonic periodic disturbance and substantial input time-delay. It forms an alternative approach to Repetitive Control where the goal is to stabilize a close-loop that encapsulates an explicit time-delay model of the periodic signal. The proposed controller design is based on the Internal Model Control (IMC) framework, and it consists of the inverse system model and a tuneable distributed delay with an overall length related to the period of the disturbance. The properness of the controller can be ensured by utilizing a low-pass filter, however, such a component is shown to be unnecessary when the relative order of the system model is one. This fact makes the alternative approach especially suitable for systems approximated by a first-order model with input time-delay, leading to a straightforward controller design thanks to its simple structure and attainable conditions. Stability of the configuration is guaranteed by an ideal IMC framework. For further performance and robustness requirements for the non-ideal case the tuning of the controller is posed as a weighted- H ∞ optimization problem where frequency-, spectral- and time-domain requirements are formulated as constraints. The overall control design is experimentally verified on a laboratory setup that has high-order dynamics approximated by a first-order model with input delay. [ABSTRACT FROM AUTHOR]