arXiv subjects

Miguel F. Arevalo-Castiblanco

Publications and source records attributed to Miguel F. Arevalo-Castiblanco.

8 recordsLinked to original sources

arXiv2026

Localized Thermal Management of Induction-Heated Microrobots for Hyperthermia Applications

Induction-heated microrobots face a fundamental thermal management challenge: the alternating magnetic fields required for localized hyperthermia simultaneously generate parasitic ohmic heating within the actuation coils. Conventional cooling approaches can fail because conductive cooling structures placed near the coil can couple to the time-varying field, generating additional electromagnetic losses. This paper addresses this architectural limitation by evaluating two liquid-cooling frameworks designed to isolate the electromagnetic workspace: an indirect configuration utilizing an external copper water block, and a direct architecture routing coolant internally through a hollow helical coil. Evaluated over a 100~s window with multi-point thermocouple telemetry, the indirect water-block configuration failed due to secondary inductive coupling with the block itself, acting as an unintended susceptor that drove the block temperature to an extreme peak of 805$^{\circ}$C. In contrast, the direct internal cooling architecture successfully extracted ohmic losses at the source, sustaining a localized ablation tip temperature of 63.4$^{\circ}$C while strictly restricting surrounding fluid and boundary surfaces below defined thresholds of 33.9$^{\circ}$C and 37.6$^{\circ}$C, respectively. These results establish coil-level fluidic integration as a key design principle for thermally isolated microrobot actuation and provide a hardware foundation for future spatially controlled hyperthermia.

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arXiv2025

Estimating Spatially-Dependent GPS Errors Using a Swarm of Robots

External factors, including urban canyons and adversarial interference, can lead to Global Positioning System (GPS) inaccuracies that vary as a function of the position in the environment. This study addresses the challenge of estimating a static, spatially-varying error function using a team of robots. We introduce a State Bias Estimation Algorithm (SBE) whose purpose is to estimate the GPS biases. The central idea is to use sensed estimates of the range and bearing to the other robots in the team to estimate changes in bias across the environment. A set of drones moves in a 2D environment, each sampling data from GPS, range, and bearing sensors. The biases calculated by the SBE at estimated positions are used to train a Gaussian Process Regression (GPR) model. We use a Sparse Gaussian process-based Informative Path Planning (IPP) algorithm that identifies high-value regions of the environment for data collection. The swarm plans paths that maximize information gain in each iteration, further refining their understanding of the environment's positional bias landscape. We evaluated SBE and IPP in simulation and compared the IPP methodology to an open-loop strategy.

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arXiv2024

Robust synchronization and policy adaptation for networked heterogeneous agents

We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings.

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arXiv2024

An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks

This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior.

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arXiv2022

Cooperative Predictive Cruise Control: A Bargaining Game Approach

This paper considers a cooperative cruise control problem from a predictive control perspective. Online decision-making is used to be executed during the driving process based on the information obtained from the network. We formalize a synchronization problem approach from a predictive control theory using bargaining games to find an operating agreement between the vehicles. Finally, we test these results in an emulation environment in a hardware-in-the-loop system.

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arXiv2020

Adaptive Synchronization of Heterogeneous Multi-Agent Systems: A Free Observer Approach

Adaptive synchronization protocols for heterogeneous multi-agent network are investigated. The interaction between each of the agents is carried out through a directed graph. We highlight the lack of communication between agents and the presence of uncertainties in each system among the conventional problems that can arise in cooperative networks. Two methodologies are presented to deal with the uncertainties: A strategy based on robust optimal control and a strategy based on neural networks. Likewise, an input estimation methodology is designed to face the disconnection that any agent may present on the network. These control laws can guarantee synchronization between agents even when there are disturbances or no communication from any agent. Stability and boundary analyzes are performed. Cooperative cruise control simulation results are shown to validate the performance of the proposed control methods.

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arXiv2020

Adaptive Control for Unknown Heterogeneous Vehicles Synchronization with Unstructured Uncertainty

The cooperative control applied to vehicles allows the optimization of traffic on the roads. There are many aspects to consider in the case of the operation of autonomous vehicles on highways since there are different external parameters that can be involved in the analysis of a network. In this paper, we present the design and simulation of adaptive control for a platoon with heterogeneous vehicles, taking into account that not all vehicles can communicate their control input, and in turn include structured nonlinear uncertainty input parameters.

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arXiv2020

Controlador LQR y SMC Aplicado a Plataformas Pendulares

A pendular platform is a robotic structure commonly used in the design of controllers given its nonlinear dynamics; This work presents the modeling, design and implementation of an optimal LQR controller and a Sliding Mode SMC controller applied to two commercial platforms, the Quanser rotary inverted pendulum (RotPen) and the Lego mobile inverted pendulum (NxtWay). The contribution of this work is to present a methodology of implementation of LQR and SMC controllers on pendular platforms, attending the respective restrictions of hardware and software in commercial prototypes. The article presents the behavior of the controller designed on the analytical model compared to its implementation.

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