This article presents a current controller design for an open-end winding permanent magnet synchronous motor (OEW-PMSM) supplied by a common DC source. Conventional control approaches for OEW-PMSMs have typically treated the zero-sequence (n-axis) component as an independent variable, ignoring its dynamic influence on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> components. In this work, a unified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dqn</i> motor model is first established by incorporating mutual inductances and rotor position-dependent spatial harmonics across all axes. Then, based on the proposed model, a multivariable controller is analytically derived to achieve fully decoupled current dynamics while ensuring stable operation under load variations. The proposed controller accounts for interaxis coupling in its structure, enabling active regulation of the zero-sequence current. This leads to improved steady-state and transient performance. Experimental validation is conducted on a motor developed for electric vehicle applications. The results confirm the effectiveness of the proposed method over conventional control, demonstrating improved steady-state waveform quality, and a reduction in total harmonic distortion (THD) of the current from 6.61% to 4.86%. In addition, the transient response is significantly enhanced. Even during abrupt torque changes, the zero-sequence current remains stable, unaffected by disturbances in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d–q</i> currents.

Estimating contact force for a robot manipulator without an force/torque (F/T) sensor poses challenges due to uncertain torques, such as backlash and flexibility. To address this limitation, this article proposes a data-driven uncertain torque model and an overall gray-box structured approach. The contributions of this article are threefold. First, a joint domain unified neural networks (DUNNs)-based model is proposed to compensate for the uncertain torques. This model effectively captures uncertain torques beyond studies focusing solely on individual uncertainty. Second, the DUNNs model receives dynamic and joint domain information, enabling a single DUNNs model to estimate all joint uncertain torques through joint domain knowledge. This approach reduces the model size while maintaining performance. Third, the structure in which the DUNNs model works with conventional static friction models is introduced. This structure improves contact force estimation performance and enhances robustness against untrained data compared with the black-box model. Experimental results verify the method's effectiveness.