arXiv subjects

Simone Pirrera

Publications and source records attributed to Simone Pirrera.

6 recordsLinked to original sources

arXiv2026

Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization

This paper studies the continuous-time dynamics generated by control-theoretic Lagrangian methods for equality-constrained optimization. In particular, we consider dynamics induced by proportional-integral and feedback linearization controllers, which have recently been proposed as alternatives to primal-dual gradient methods. Unlike global convergence results for these dynamics, which rely on strong convexity of the objective function or boundedness assumptions, we exploit the geometric structure induced by the constraints. Specifically, we show global exponential convergence for non-convex problems that satisfy a suitable convexity property when restricted to the constraint manifold.

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arXiv2025

Real-Time Single-Iteration Model Predictive Control for Wave Energy Converters

This paper proposes a novel real-time algorithm for controlling wave energy converters (WECs). We begin with the economic model predictive control (MPC) problem formulation and apply a novel, first-order optimization algorithm inspired by recently developed control-based algorithms for constrained optimization to define the controller dynamics according to the single-iteration MPC approach. We theoretically analyse the convergence of the employed algorithm and the computational complexity of the obtained controller. Results from simulations using a benchmark WEC system indicate that the proposed approach significantly outperforms standard MPC, thanks to the inherent ability to handle faster sampling rates.

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arXiv2025

A Fully Analog Implementation of Model Predictive Control with Application to Buck Converters

This paper proposes a novel approach to design analog electronic circuits that implement Model Predictive Control (MPC) policies for dynamical systems described by affine models. Effective approaches to define a reduced-complexity Explicit MPC form are combined and applied to realize an analog circuit comprising a limited set of low-latency, commercially available components. The practical feasibility and effectiveness of the proposed approach are demonstrated through its application in the design of a novel MPC-based controller for DC-DC Buck converters. We formally analyze the stability of the resulting system and conduct extensive numerical simulations to demonstrate the control system's performance in rejecting line and load disturbances.

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arXiv2025

Robust Load Disturbance Rejection in PWM DC-DC Buck Converters

This paper presents a novel approach to robust load disturbance rejection in DC-DC Buck converters. We propose a novel control scheme based on the design of two nested feedback loops. First, we design the controller in the outer loop using H infinity optimal control theory, and we show, by means of mu-analysis, that such a controller provides robust stability in the presence of uncertainty affecting the physical parameters of the circuit. Then, we introduce an inner feedback loop to improve the system's response to output load disturbances. As far as the inner loop is considered, we propose a novel load estimation-compensation (LEC) scheme, and we discuss under what conditions the insertion of such an inner loop preserves the robust stability of the entire control system. The LEC scheme is compared with the other two linear structures based on well-established disturbance rejection methods. The advantages of LEC in terms of both complexity of implementation and obtained performances are discussed and demonstrated by means of numerical simulation. Finally, we present experimental results obtained through the implementation of the proposed control scheme on a prototype board to demonstrate that the proposed approach significantly enhances disturbance rejection performances with respect to the approach commonly used in DC-DC buck converters.

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arXiv2025

A constrained optimization approach to nonlinear system identification through simulation error minimization

This paper introduces a novel approach to system identification for nonlinear input-output models that minimizes the simulation error and frames the problem as a constrained optimization task. The proposed method addresses vanishing gradient issues, enabling faster convergence than traditional gradient-based techniques. We present an algorithm based on feedback linearization control of Lagrange multipliers and conduct a theoretical analysis of its performance. We prove that the algorithm converges to a local minimum, and it enhances computational efficiency by exploiting the problem's structure. Numerical experiments demonstrate that our approach outperforms gradient-based methods in both computational effort and estimation accuracy.

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arXiv2025

Set-membership identification of continuous-time MIMO systems via Tustin discretization

In this paper, we deal with the identification of continuous-time systems from sampled data corrupted by unknown but bounded errors. A significant challenge in continuous-time identification is the estimation of the input and output data derivatives. In this paper, we propose a novel method based on set-membership techniques and Tustin discretization, which overcomes the derivative measurement problem and the presence of bounded errors affecting all the measured signals. First, we derive the proposed method and prove that it becomes an affordable polynomial optimization problem. Then, we present some numerical results based on simulation and experimental data to explore the effectiveness of the proposed method.

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