arXiv subjects

Jaime A. Moreno

Publications and source records attributed to Jaime A. Moreno.

12 recordsLinked to original sources

arXiv2026

On Composite Adaptive Continuous Finite-Time Control of a class of Euler-Lagrange systems

In this paper, we propose several set-point control schemes for achieving finite-time regulation in a class of Euler--Lagrange systems with $n$ degrees of freedom and uncertain potential energy. The proposed controllers are based on composite adaptive control approaches. Each control scheme consists of two main components: a Proportional--Derivative (PD)-based nonlinear feedback term and a finite-time parameter estimation law. The estimation laws rely on the Dynamic Regressor Extension and Mixing (DREM) technique, which can be designed using either the Kreisselmeier or the least-squares dynamic regressor extensions. These results extend recent advances in finite-time adaptive control for Euler-Lagrange systems. To the best of the authors' knowledge, the composite adaptive control formulation proposed here, which does not employ well-known Slotine and Li adaptive control structure, has not been studied in detail yet. The properties of the proposed controllers are rigorously analyzed using Lyapunov methods. The performance of the controllers is thoroughly evaluated through extensive simulation studies.

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arXiv2025

Strong nonlinear detectability and moving horizon estimation for nonlinear systems with unknown inputs

This paper considers state estimation for general nonlinear discrete-time systems subject to measurement noise and possibly unbounded unknown inputs. To approach this problem, we first propose the concept of strong nonlinear detectability. This condition is sufficient and necessary for the existence of unknown input state estimators (UISEs), which reconstruct states from noisy sampled measurements and yield bounded estimation error even for unbounded unknown inputs. Based on the proposed detectability notion, a UISE is designed via a moving horizon estimation strategy using a full-order model as well as past and current measurements. Next, we tighten this detectability notion to design a two-stage MHE-based UISE, which is computationally more efficient than the MHE-based UISE using full-order models. In a simulation example with a plant growth process, both variants of MHE-based UISEs are compared with a conventional MHE to illustrate the merits of the developed methods.

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arXiv2025

Generalized Super-Twisting Observer for a class of interconnected nonlinear systems with uncertainties

The Generalized Super-Twisting Observer (GSTO) is extended for a strongly observable class of nonlinearly interconnected systems with bounded uncertainties/perturbations. A nonsmooth strong Lyapunov function is used to prove the finite-time convergence of the proposed observer to the true system's trajectories, in the presence of the uncertainties. A case study on the interaction between two food production systems is presented, comparing the proposed observer with the High Gain observer. The results emphasize the critical role of the GSTO's discontinuous term in achieving exact estimation.

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arXiv2024

Global Observability Analysis of a Growth Model for Insects Farming

The Hermetia illucens insects or the black soldier fly has been attracting a growing interest in the food and feed industry. For its high nutritional value on the one hand, and because it is an adequate species for insects in controlled environmental agriculture systems, on the other. Therefore, several models describing this larvae's behaviour have been developed in the literature. Due to the complex nature of living organisms, systems of controlled environment agriculture are characterised by their strong nonlinearities. In this paper, we present a three dimensional nonlinear model describing the black soldier fly dry biomass weight dynamic changes due to the temperature's influence. In practice, this biomass weight is not measured in real time. This becomes problematic for applying feedback control strategies that assume full information of the states. Thus, this work investigates the observability of the dry biomass of a Hermetia illucens farming batch. The instantaneous and global observability of the aforementioned model is proven by constructing an injective transformation between the state space and a higher dimensional space where the transformed states are observable.

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arXiv2024

A robust consensus + innovations-based distributed parameter estimator

While distributed parameter estimation has been extensively studied in the literature, little has been achieved in terms of robust analysis and tuning methods in the presence of disturbances. However, disturbances such as measurement noise and model mismatches occur in any real-world setting. Therefore, providing tuning methods with specific robustness guarantees would greatly benefit the practical application. To address these issues, we recast the error dynamics of a continuous-time version of the widely used consensus + innovations-based distributed parameter estimator to reflect the error dynamics induced by the classical gradient descent algorithm. This paves the way for the construction of a strong Lyapunov function. Based on this result, we derive linear matrix inequality-based tools for tuning the algorithm gains such that a guaranteed upper bound on the L2-gain with respect to parameter variations, measurement noise, and disturbances in the communication channels is achieved. An application example illustrates the efficiency of the method.

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arXiv2023

Minimizing the Homogeneous $\mathcal{L}_2$-Gain of Homogeneous Differentiators

The differentiation of noisy signals using the family of homogeneous differentiators is considered. It includes the high-gain (linear) as well as robust exact (discontinuous) differentiator. To characterize the effect of noise and disturbance on the differentiation estimation error, the generalized, homogeneous $\mathcal{L}_2$-gain is utilized. Analog to the classical $\mathcal{L}_p$-gain, it is not defined for the discontinuous case w.r.t. disturbances acting on the last channel. Thus, only continuous differentiators are addressed. The gain is estimated using a differential dissipation inequality, where a scaled Lyapunov function acts as storage function for the homogeneous $\mathcal{L}_2$ supply rate. The fixed differentiator gains are scaled with a gain-scaling parameter similar to the high-gain differentiator. This paper shows the existence of an optimal scaling which (locally) minimizes the homogeneous $\mathcal{L}_2$-gain estimate and provides a procedure to obtain it. Differentiators of dimension two are considered and the results are illustrated via numerical evaluation and a simulation example.

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arXiv2022

MIMO Super-Twisting Controller using a passivity-based design

A novel MIMO homogeneous Super-Twisting Algorithm is proposed in this paper for nonlinear systems with relative degree one, having a time and state-varying uncertain control matrix. The uncertainty is represented by a constant but unknown left matrix factor. Sufficient conditions for stability with full-matrix control gains are established, in contrast to the usual scalar gains. For this a smooth Lyapunov function, based on a passivity interpretation, is used. Moreover, continuous and homogeneous approximations of the classical discontinuous Super-Twisting algorithm are obtained, using a unified analysis method.

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arXiv2021

Multivariable Super-Twisting Algorithm for Systems with Uncertain Input Matrix and Perturbations

This paper proposes a Lyapunov approach to the design of a multivariable generalized Super-Twisting algorithm (MGSTA), which is able to control a system with perturbations and uncertain control matrix, both depending on time and the system states. The presented procedure shows that, under reasonable assumptions for the uncertainties, it is always possible to find a set of constant gains for the MGSTA in order to ensure global and robust finite-time stability of the system's outputs. Simulation results on an omnidirectional mobile robot illustrate the performance of the MGSTA.

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arXiv2020

Arbitrary Order Fixed-Time Differentiators

Differentiation is an important task in control, observation and fault detection. Levant's differentiator is unique, since it is able to estimate exactly and robustly the derivatives of a signal with a bounded high-order derivative. However, the convergence time, although finite, grows unboundedly with the norm of the initial differentiation error, making it uncertain when the estimated derivative is exact. In this paper we propose an extension of Levant's differentiator so that the worst case convergence time can be arbitrarily assigned independently of the initial condition, i.e. the estimation converges in \emph{Fixed-Time}. We propose also a family of continuous differentiators and provide a unified Lyapunov framework for analysis and design.

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arXiv2016

Fundamental limitations of network reconstruction

Network reconstruction is the first step towards understanding, diagnosing and controlling the dynamics of complex networked systems. It allows us to infer properties of the interaction matrix, which characterizes how nodes in a system directly interact with each other. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g., adjacency pattern, sign pattern and degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here we rigorously derive necessary conditions to reconstruct any property of the interaction matrix. These conditions characterize how uncertain can we be about the coupling functions that characterize the interactions between nodes, and how informative does the measured temporal data need to be; rendering two classes of fundamental limitations of network reconstruction. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations shed light on the design of better network reconstruction algorithms, which offer practical improvements over existing methods.

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arXiv2015

Discontinuous integral control for mechanical systems

For mechanical systems we present a controller able to track an unknown smooth signal, converging in finite time and by means of a continuous control signal. The control scheme is insensitive against unknown perturbations with bounded derivative. The controller consists of a non locally Lipschitz state feedback control law, and a discontinuous integral controller, that is able to estimate the unknown perturbation and to compensate for it. To complete an output feedback control a continuous observer for the velocity is added. It is shown that the closed loop consisting of state feedback, state observer and discontinuous integral controller has an equilibrium point that is globally, finite time stable, despite of perturbations with bounded derivative. The proof is based on a new smooth Lyapunov function.

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arXiv2015

Non-linear Gradient Algorithm for Parameter Estimation: Extended version

Gradient algorithms are classical in adaptive control and parameter estimation. For instantaneous quadratic cost functions they lead to a linear time-varying dynamic system that converges exponentially under persistence of excitation conditions. In this paper we consider (instantaneous) non-quadratic cost functions, for which the gradient algorithm leads to non-linear (and non Lipschitz) time-varying dynamics, which are homogeneous in the state. We show that under persistence of excitation conditions they also converge globally, uniformly and asymptotically. Compared to the linear counterpart, they accelerate the convergence and can provide for finite-time or fixed-time stability.

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