arXiv subjects

Amirhossein Asadi

Publications and source records attributed to Amirhossein Asadi.

2 recordsLinked to original sources

arXiv2026

Reduced-Order Backstepping Control of Nonlinear Mechanical Systems with Input Delay via Galerkin Projection

This paper addresses trajectory tracking for a class of nonlinear mechanical systems with input delay. The delayed actuator channel is represented as an equivalent transport partial differential equation, thereby transforming the original delayed system into a coupled plant-channel representation. A finite-dimensional approximation of the delay channel is then constructed through Galerkin projection, resulting in an explicit ODE model of the delayed actuator dynamics. Using this reduced-order plant-channel model, a backstepping controller is developed, and Lyapunov analysis establishes uniform exponential stability of the nominal tracking error dynamics for the reduced-order Galerkin model. To enhance practical implementation, a command-filtered version is also introduced to avoid direct differentiation of virtual controls, for which a corresponding uniform ultimate boundedness result is obtained. Simulation studies are provided to validate the proposed design and illustrate the effectiveness of the method.

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arXiv2026

Safety-Aware Dimension Selection for Reduced-Order Model Predictive Control

Model reduction can substantially reduce the computational burden of model predictive control (MPC) for high-dimensional systems, but a reduced dimension chosen for performance may be insufficient for safety-critical operation. This paper introduces a safety-aware dimension-selection framework in which the prediction model adapts to the order of the model required to maintain a certified finite-horizon safety condition. Dynamic active subspaces provide a performance-oriented ordering of reduced coordinates, while finite-horizon error bounds robustly enforce discrete-time control barrier function constraints. The controller selects the smallest prediction order for which the resulting safety-critical MPC problem remains feasible. We establish full-order horizon safety, recursive feasibility, and closed-loop invariance of the safe set under the proposed reduced-order formulation. The proposed approach therefore adapts the prediction complexity to safety requirements, avoiding unnecessary high-dimensional prediction during benign operation.

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