Journal Of Applied and Computational Sciences in Mechanics

Journal Of Applied and Computational Sciences in Mechanics

Physics-Informed Learning-based model-free computed torque control for unknown nonlinear rigid dynamic systems

Document Type : Original Article

Author
Arman mardani
Department of Mechanical Engineering,-babol nooshirvani university of technology
10.22067/jacsm.2026.95250.1354
Abstract
This paper presents a new model-free control strategy for nonlinear rigid-body systems with unknown or highly complex dynamics. Unlike traditional Computed Torque Control (CTC), which depends on precise analytical models, this approach uses real-time data to estimate system behavior, eliminating the need for prior modeling. The method operates in two key stages. First, it dynamically estimates the Jacobian matrix by applying small perturbations to control inputs, linking task-space motion to joint torques. This Jacobian maps a virtual spring-damper force from the workspace to joint space, ensuring stable trajectory following. Second, a constrained Recursive Least Squares (RLS) algorithm learns a simplified linear representation of the system’s nonlinear dynamics, accounting for Coriolis, centrifugal, and gravitational effects. Crucially, this learning process incorporates physical constraints, maintaining energy conservation and other essential rigid-body properties. data-driven mechanisms. The result is a real-time compensator for nonlinearities that preserves stability and performance. Tests on a robotic manipulator confirm improved tracking accuracy and robustness over conventional CTC, especially in cases with unmodeled dynamics or parameter uncertainties.
Keywords
Numerical Jacobian Estimation
Perturbation-Based Learning
Nonlinear rigid-body systems
dynamic systems approximation
Subjects
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Journal Of Applied and Computational Sciences in Mechanics
Volume 38, 2-2 - Serial Number 46
Summer 2026
Pages 25-40