arXiv subjects

Adrian Stein

Publications and source records attributed to Adrian Stein.

11 recordsLinked to original sources

arXiv2026

Global Sensitive-Based Input Shaping for UAV-Payload Precision Motion Control

This work presents a comprehensive analysis and design of global sensitivity-based input shapers for a 3D Unmanned Aerial Vehicle-payload system, emphasizing robustness against uncertainties in payload mass and rope length. The proposed approach also leverages the Shapley value concept in controller design to systematically account for uncertainties, thereby reducing the controller's sensitivity to unknown parameters. To validate the effectiveness of the methodology, numerical simulations are conducted, comparing the proposed controller against non-robust, robust, and minimax designs. The results demonstrate that the standard global sensitivity or Shapley-based input shapers improve performance and offer a promising framework for uncertainty-aware control in aerial payload transport.

eess.SY↗

arXiv2026

Input Shaping for Point-to-Point Motion with a Continuum Robot Arm

A cable-driven continuum robot arm is an underactuated mechanism and may suffer residual vibration at the end of a rest-to-rest maneuver. In this work, a time-delay filter is applied as an input shaper to the system to eliminate the excitation of vibratory modes. A non-robust and a robust time-delay filter are designed based on a linear system model and demonstrate improved response compared to a velocity-driven pulse input. Experimental results using the continuum robot validate the application of the input shaper, with reduced overshoot and settling time exemplifying the reduction in oscillation at the end of the maneuver. It is also shown that utilizing the robust shaper further improves the response of the arm in comparison to applying the non-robust shaper. These results are significant towards the precise and robust implementation of continuum robots in applications involving arbitrary end-effector trajectories.

cs.RO↗

arXiv2026

Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis

This work presents a game-theoretic framework for interpretable hyperparameter-objective interaction analysis rather than proposing a new optimization algorithm. In the proposed framework, Shapley Effects are employed for global sensitivity analysis, while Pareto front sets are utilized to identify effective hyperparameter configurations and support early-stage model evaluation. The resulting analysis reveals which players (hyperparameters) are most influential with respect to different objectives in a given game (application). Consequently, the proposed framework provides interpretable insights into objective-aware hyperparameter interactions, enabling practitioners to guide subsequent optimization, reduce the search space, and perform early-stage model evaluation. The effectiveness of the proposed framework is demonstrated using three distinct neural network architectures across different problem domains under multi-objective settings.

stat.ML↗

arXiv2026

Polynomial Chaos-based Input Shaper Design under Time-Varying Uncertainty

The work presented here investigates the application of polynomial chaos expansion toward input shaper design in order to maintain robustness in dynamical systems subject to uncertainty. Furthermore, this work intends to specifically address time-varying uncertainty by employing intrusive polynomial chaos expansion. The methodology presented is validated through numerical simulation of intrusive polynomial chaos expansion formulation applied to spring mass system experiencing time-varying uncertainty in the spring stiffness. The system also evaluates non-robust and robust input shapers through the framework in order to identify designs that minimize residual energy. Results indicate that vibration mitigation is achieved at a similar accuracy, yet at higher efficiency compared to a Monte Carlo framework.

eess.SY↗

arXiv2026

Adaptive Input Shaper Design for Unknown Second-Order Systems with Real-Time Parameter Estimation

We propose a feedforward input-shaping framework with online parameter estimation for unknown second-order systems. The proposed approach eliminates the need for prior knowledge of system parameters when designing input shaping for precise switching times by incorporating online estimation for a black-box system. The adaptive input shaping scheme accounts for the system's periodic switching behavior and enables reference shaping even when initial switching instants are missed. The proposed framework is evaluated in simulation and is intended for vibration suppression in motion control applications such as gantry cranes and 3D printer headers.

eess.SY↗

arXiv2025

Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification

This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a softmax layer enables multi-class confidence estimation. Quadcopter, fixed-wing, and helicopter aerial vehicles are considered as case studies. The results demonstrate high classification accuracy with reduced training time, offering a promising solution for system identification problems in domains where the underlying dynamics are well understood.

cs.LG↗

arXiv2023

Application-Oriented Co-Design of Motors and Motions for a 6DOF Robot Manipulator

This work investigates an application-driven co-design problem where the motion and motors of a six degrees of freedom robotic manipulator are optimized simultaneously, and the application is characterized by a set of tasks. Unlike the state-of-the-art which selects motors from a product catalogue and performs co-design for a single task, this work designs the motor geometry as well as motion for a specific application. Contributions are made towards solving the proposed co-design problem in a computationally-efficient manner. First, a two-step process is proposed, where multiple motor designs are identified by optimizing motions and motors for multiple tasks one by one, and then are reconciled to determine the final motor design. Second, magnetic equivalent circuit modeling is exploited to establish the analytic mapping from motor design parameters to dynamic models and objective functions to facilitate the subsequent differentiable simulation. Third, a direct-collocation-based differentiable simulator of motor and robotic arm dynamics is developed to balance the computational complexity and numerical stability. Simulation verifies that higher performance for a specific application can be achieved with the multi-task method, compared to several benchmark co-design methods.

cs.RO↗

arXiv2023

From Playground Swings to Sway Control of Cranes: An Active Pendulum Experiment

Dynamics is a core discipline in Mechanical and Aerospace Engineering programs and with the ubiquitous nature of control in modern day applications, the field of mechatronics has gained popularity. Mechatronics refers to the field of engineering which integrates the engineering disciplines of mechanical, control, electronics and computing. To create a testbed to illustrate a tabletop mechatronics system, the paper details the design, and fabrication of an active pendulum whose length can be changed in real-time using solenoids. This permits illustrating two concepts: (1) damping of pendulum oscillations which emulates the sway of a crane and (2) amplification of the oscillations which emulates the pumping of a playground swing. The paper describes the steps prior to experimental validation which include: modeling, system identification, signal processing, and controller implementation. Numerical simulations are used to prototype the controller and eventually to compare the simulation results to the experimental ones. The results of all the experiments illustrate a close match between the simulated and experimental results. To permit reproduction of the experiment, the design details and code to implement the controllers are posted in a public repository.

physics.ed-ph↗

arXiv2023

Minimum Time Control of a Gantry Crane System with Rate Constraints

This paper focuses on the development of minimum time control profiles for point-to-point motion of a gantry crane system in the presence of uncertainties in modal parameters. Assuming that the velocity of the trolley of the crane can be commanded and is subject to limits, an optimal control problem is posed to determine the bang-off-bang control profile to transition the system from a point of rest to the terminal states with no residual vibrations. Both undamped and underdamped systems are considered and the variation of the structure of the optimal control profiles as a function of the final displacement is studied. As the magnitude of the rigid body displacement is increased, the collapse and birthing of switches in the optimal control profile are observed and explained. Robustness to uncertainties in modal parameters is accounted for by forcing the state sensitivities at the terminal time to zero. The observation that the time-optimal control profile merges with the robust time-optimal control is noted for specific terminal displacements and the migration of zeros of the time-delay filter parameterizing the optimal control profile are used to explain this counter intuitive result. A two degree of freedom gantry crane system is used to experimentally validate the observations of the numerical studies and the tradeoff of increase in maneuver time to the reduction of residual vibrations is experimentally illustrated.

eess.SY↗

arXiv2023

Shapley Effect Estimation using Polynomial Chaos

This paper presents an approach for estimating Shapley effects for use as global sensitivity metrics to quantify the relative importance of uncertain model parameters. Polynomial Chaos expansion, a well established approach for developing surrogate models is proposed to be used to estimate Shapley effects. Polynomial Chaos permits the transformation of a stochastic process to a deterministic model which can then be used to efficiently evaluate statistical moments of the quantity of interest. These moments include conditional variances which are algebraically mapped to Shapley effects. The polynomial chaos based estimates of Shapley effects are validated using Monte Carlo simulations and tested on the benchmark Ishigami function and on the dynamic SEIR epidemic model and the Bergman Type 1 diabetes model. The results illustrate the correct ranking of uncertain variables for the Ishigami function in contrast to the Sobol indices and illustrates the time-varying rank ordering of the model parameters for the dynamic models.

stat.AP↗

arXiv2013

Bimodal substrate biasing to control γ-Al2O3 deposition during reactive magnetron sputtering

Al2O3 thin films have been deposited at substrate temperatures between 500°C to 600°C by reactive magnetron sputtering using an additional arbitrary substrate bias to tailor the energy distribution of the incident ions. The films were characterized by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The film structure being amorphous, nanocrystalline, or crystalline was correlated with characteristic ion energy distributions. The evolving crystalline structure is connected with different levels of displacements per atom (dpa) in the growing film as being derived from TRIM simulations. The boundary between the formation of crystalline films and amorphous or nanocrystalline films was at 0.9 dpa for a substrate temperature of 500°C. This threshold shifts to 0.6 dpa for films grown at 550°C.

physics.plasm-ph↗