arXiv subjects

Alejandro I. Maass

Publications and source records attributed to Alejandro I. Maass.

10 recordsLinked to original sources

arXiv2026

Expected String Stability of Human-Led Vehicle Platoons under Stochastic Communication Delays (Full Version)

This paper studies expected $\mathcal{L}_2$ string stability of event-triggered vehicle platoons in which a human driver leads a chain of cooperatively controlled autonomous followers under stochastic communication delays. The leader's driving behavior propagates through the string via vehicle-to-vehicle (V2V) communication, so human-induced disturbances must not amplify along the platoon. Unlike deterministic approaches based on worst-case delay bounds, we derive string-stability conditions depending on the full delay distribution through integral inequalities. The closed-loop platoon is modeled as a stochastic hybrid system capturing vehicle dynamics, communication events, and event-triggering. This framework certifies string stability even when delays exceed deterministic admissible bounds with nonzero probability. Results are evaluated under several delay distributions using the MATLAB HyEQ simulator.

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arXiv2025

Observer Design for Networked Linear Systems with Fast and Slow Dynamics under Measurement Noise

This paper addresses the emulation-based observer design for networked control systems (NCS) with linear plants that operate at two time scales in the presence of measurement noise. The system is formulated as a hybrid singularly perturbed dynamical system, enabling the systematic use of singular perturbation techniques to derive explicit bounds on the maximum allowable transmission intervals (MATI) for both fast and slow communication channels. Under the resulting conditions, the proposed observer guarantees that the estimation error satisfies a global exponential derivative-input-to-state stability (DISS)-like property, where the ultimate bound scales proportionally with the magnitudes of the measurement noise and the time derivative of the control input. The effectiveness of the approach is illustrated through a numerical example.

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arXiv2025

Modeling and Control of Magnetic Forces between Microrobots

The independent control of multiple magnetic microrobots under a shared global signal presents critical challenges in biomedical applications such as targeted drug delivery and microsurgeries. Most existing systems only allow all agents to move synchronously, limiting their use in applications that require differentiated actuation. This research aims to design a controller capable of regulating the radial distance between micro-agents using only the angle ψof a global magnetic field as the actuation parameter, demonstrating potential for practical applications. The proposed cascade control approach enables faster and more precise adjustment of the inter-agent distance than a proportional controller, while maintaining smooth transitions and avoiding abrupt changes in the orientation of the magnetic field, making it suitable for real-world implementation. A bibliographic review was conducted to develop the physical model, considering magnetic dipole-dipole interactions and velocities in viscous media. A PID controller was implemented to regulate the radial distance, followed by a PD controller in cascade to smooth changes in field orientation. These controllers were simulated in MATLAB, showing that the PID controller reduced convergence time to the desired radius by about 40%. When adding the second controller, the combined PID+PD scheme achieved smooth angular trajectories within similar timeframes, with fluctuations of only \pm 5^\circ. These results validate the feasibility of controlling the radial distance of two microrobots using a shared magnetic field in a fast and precise manner, without abrupt variations in the control angle. However, the model is limited to a 2D environment and two agents, suggesting future research to extend the controller to 3D systems and multiple agents.

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arXiv2025

Stabilization of singularly perturbed networked control systems over a single channel

This paper studies the emulation-based stabilization of nonlinear networked control systems with two time scales. We address the challenge of using a single communication channel for transmitting both fast and slow variables between the plant and the controller. A novel dual clock mechanism is proposed to schedule transmissions for this purpose. The system is modeled as a hybrid singularly perturbed dynamical system, and singular perturbation analysis is employed to determine individual maximum allowable transmission intervals for both fast and slow variables, ensuring semi-global practical asymptotic stability. Enhanced stability guarantees are also provided under stronger assumptions. The efficacy of the proposed method is illustrated through a numerical example.

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arXiv2024

Stochastic Lp string stability analysis in predecessor-following platoons under packet losses

In this paper, we study (homogeneous) predecessor-following platoons in which the vehicle-to-vehicle (V2V) communications are affected by random packet losses. We model the overall platoon as a stochastic hybrid system and analyse its string stability via a small-gain approach. For nonlinear platoons, we illustrate how the different elements of the platoon have an impact on string stability, such as platoon topology and vehicle scheduling. For linear time-invariant platoons, we provide an explicit string stability condition that illustrates the interplay between the channel success probability, transmission rate, and time headway constant. Lastly, we illustrate our results by numerical simulations.

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arXiv2024

On stochastic string stability with applications to platooning over additive noise channels

This paper addresses the string stabilization of vehicular platooning when stochastic phenomena are inherent in inter-vehicle communication. To achieve this, we first provide two definitions to analytically assess the string stability in stochastic scenarios, considering the mean and variance of tracking errors as the platoon size grows. Subsequently, we analytically derive necessary and sufficient conditions to achieve this notion of string stability in predecessor-following linear platoons that communicate through additive white noise channels. We conclude that the condition ensuring string stability with ideal communication is essentially the same that achieves stochastic string stability when additive noise channels are in place and guarantees that the tracking error means and variances converge.

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arXiv2022

Transmit power policies for stochastic stabilisation of multi-link wireless networked control systems

Transmit power control is one of the most important issues in wireless networks, where nodes typically operate on limited battery power. Reducing communicating power consumption is essential for both economic and ecologic reasons. In fact, transmitting at unnecessarily high power not only reduces node lifetime, but also introduces excessive interference and electromagnetic pollution. Existing work in the wireless community mostly focus on designing transmit power policies by taking into account communication aspects like quality of service or network capacity. Wireless networked control systems (WNCSs), on the other hand, have different and specific needs such as stability, which require transmit power policies adapted to the control context. Transmit power design in the control community has recently attracted much attention, and available works mostly consider linear systems or specific classes of non-linear systems with a single-link view of the system. In this paper, we propose a framework for the design of stabilising transmit power levels that applies to much larger classes of non-linear plants, controllers, and multi-link setting. By exploiting the fact that channel success probabilities are related to transmit power in a non-linear fashion, we first derive closed-loop stability conditions that relate channel probabilities with transmission rate. Next, we combine these results together with well-known and realistic interference models to provide a design methodology for stabilising transmit power in non-linear and multi-link WNCSs.

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arXiv2022

Tracking and regret bounds for online zeroth-order Euclidean and Riemannian optimisation

We study numerical optimisation algorithms that use zeroth-order information to minimise time-varying geodesically-convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant cases. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalisation of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimise the algorithm's performance. Our results illustrate how the manifold geometry in terms of the sectional curvature affects these bounds. Additionally, we provide dynamic regret bounds for this online optimisation setting. To the best of our knowledge, these are the first regret bounds even for the Euclidean version of the problem. Lastly, via numerical simulations, we demonstrate the applicability of our algorithm on an online Karcher mean problem.

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arXiv2021

Zeroth-order optimisation on subsets of symmetric matrices with application to MPC tuning

This paper provides a zeroth-order optimisation framework for non-smooth and possibly non-convex cost functions with matrix parameters that are real and symmetric. We provide complexity bounds on the number of iterations required to ensure a given accuracy level for both the convex and non-convex case. The derived complexity bounds for the convex case are less conservative than available bounds in the literature since we exploit the symmetric structure of the underlying matrix space. Moreover, the non-convex complexity bounds are novel for the class of optimisation problems we consider. The utility of the framework is evident in the suite of applications that use symmetric matrices as tuning parameters. Of primary interest here is the challenge of tuning the gain matrices in model predictive controllers, as this is a challenge known to be inhibiting industrial implementation of these architectures. To demonstrate the framework we consider the problem of MIMO diesel air-path control, and consider implementing the framework iteratively ``in-the-loop'' to reduce tracking error on the output channels. Both simulations and experimental results are included to illustrate the effectiveness of the proposed framework over different engine drive cycles.

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arXiv2020

Active Learning for Linear Parameter-Varying System Identification

Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.

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