Harish J. Palanthandalam-Madapusi

Roshan A. Chavan, Sujay D. Kadam, Abhijith Rajiv et al.

The idea of posing a command following or tracking control problem as an input reconstruction problem is explored in the paper. For a class of square MIMO systems with known dynamics, by pretending that reference commands are actual outputs of the system, input reconstruction methods can be used to determine control action that will result in a system following desired reference commands. A feedback controller which is a combination of an unbiased state estimator and an input reconstructor that ensures unbiased tracking of reference commands is proposed. Simulations and real-time implementation are presented to demonstrate utility of the proposed idea. Conditions under which proposed controller may be used for non-square systems are also discussed.

Shreyas Kumar, Barat S, Debojit Das et al.

Delicate snap-fit assemblies, such as inserting a lens into an eye-wear frame or during electronics assembly, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. We address these challenges with two key contributions. First, we introduce SnapNet, a lightweight neural network that detects snap-fit engagement from joint-velocity transients in real-time, showing that reliable detection can be achieved using proprioceptive signals without external sensors. Second, we present a dynamical-systems-based dual-arm coordination framework that integrates SnapNet driven detection with an event-triggered impedance modulation, enabling accurate alignment and compliant insertion during delicate snap-fit assemblies. Experiments across diverse geometries on a heterogeneous bimanual platform demonstrate high detection accuracy (over 96% recall) and up to a 30% reduction in peak impact forces compared to standard impedance control.