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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA)
Manoharan Kavitha
Saveetha University
Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering

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Physics-informed reinforcement learning for adaptive high-frequency injection in encoderless low-voltage PMSM drives Surendar Aravindhan; Manoharan Kavitha; J. Karthika
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp873-884

Abstract

It is difficult to control permanent magnet synchronous motor (PMSM) drives running at extra-low voltages with encoderless control because the back-EMF signal to estimate rotor position is weak, and this requires the injection of high-frequency (HF) signals. Traditional methods use constant or manually tuned injection levels, and these tend to cause large torque ripple, inaccurate estimation when under dynamic loading, and an inability to counteract parameter drift. The paper is related to the issue of online optimal HF injection amplitude choice in the encoderless 48 V PMSM drives and proposes a physics-inspired reinforcement learning (PIRL) system. This is aimed at obtaining the right low-speed positioning and reducing the torque ripple and power losses on different operating conditions. The suggested approach incorporates directly into the reinforcement learning reward terms the PMSM electromagnetic voltage equations, which restrict exploration to physically consistent space and enhance stability in the learning process. The PIRL agent is trained in a deep deterministic policy gradient architecture in a MATLAB/Simulink-Python co-simulation environment, based on which the PIRL agent adjusts the injection amplitude of HF in real time. Simulation outcomes show that the suggested methodology reaches approximately four times faster convergence with conventional reinforcement learning and reaches up to 65 percent of torque ripple reduction without a disturbed position estimation when operated in a speed range of 0-500 rpm. The findings show that physics-informed learning offers an efficient and energy-saving solution to adaptive encoderless control in extra-low-voltage PMSM drives, which has better resilience to changes in parameters with a low computational cost.
Human state digital twin architecture for physiology constrained adaptive robotic autonomy Jaganathan Nirmaladevi; S. Saranya; Nidhi Mishra; Fazal Noorbasha; Manoharan Kavitha
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp503-518

Abstract

Time-dependent human cognitive and physical conditions should be considered in adaptive autonomy in collaborative robotics to ensure safety, performance, and operator comfort. But most of the current methods depend on heuristic levels of workload or individual physiological measures and fail to combine human-state estimation with the control structure. This paper suggests a human state digital twin (HSDT) framework of physiological-constrained adaptive robotic autonomy. This framework is a combination of multimodal physiological measures of electrocardiography-based heart-rate variability, electromyography, electrodermal activity, and task context to predict latent human states such as cognitive workload, fatigue, and stress. These estimates are included in a twin-constrained adaptive autonomy controller, which varies the control authority of the robot in real time. The simulated collaborative manipulation situation is tested in the proposed framework in different workload and fatigue conditions. It has demonstrated better tracking performance, less torque ripple, reduced operator stress indicators and less torque ripple. The paper presents a controlled and reconfigurable physiology-aware adaptive autonomy control architecture and a robotics-based digital twin control design framework of human-aware control design.
Co-Authors Fazal Noorbasha J. Karthika Jaganathan Nirmaladevi Nidhi Mishra S. Saranya Surendar Aravindhan