Jose Maria Manzano

@uloyola.es

Lecturer - Engineering Department
Universidad Loyola



                       

https://researchid.co/jmanzano
2

Scopus Publications

20

Scholar Citations

1

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Input-to-state stable predictive control based on continuous projected kinky inference
    Jose Maria Manzano, David Muñoz de la Peña, and Daniel Limon
    Wiley

  • Input-to-state stable predictive control based on continuous projected kinky inference
    Jose Maria Manzano, David Muñoz de la Peña, and Daniel Limon
    Wiley

  • CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
    Beatrice Sonzogni, José María Manzano, Marco Polver, Fabio Previdi, and Antonio Ferramosca
    Elsevier BV

  • Deployment of a Smart Irrigation Control System with Capacity-Based Moisture Sensors on a Production Farm
    Luis Orihuela, Erid Pacheco, Jorge Bareiro, Alejandro Tapia, and Jose M. Manzano
    American Society of Civil Engineers (ASCE)


  • Insulin on Board safety constraint effect in a CHoKI-based MPC for Artificial Pancreas
    Beatrice Sonzogni, José María Manzano, Fabio Previdi, and Antonio Ferramosca
    Elsevier BV

  • Reducing flow heterogeneity in drip irrigation networks using genetic algorithms
    Alejandro Tapia Córdoba and Jose Maria Manzano
    Wiley
    AbstractIrrigation based on non‐compensated drip emitters is extremely common in agriculture, especially due to its simplicity, robustness and competitive cost. Nevertheless, because of friction losses in the pipe, together with irregular terrain, these systems often suffer from uneven water distribution in the drip emitters, which not only results in inefficient use of water resources but also might lead to inadequate irrigation in certain parts of the field. This work proposes to design the topology of the irrigation network to compensate for these discharge differences. To this end, a graph‐based mathematical model is developed to determine the discharge flows at different emitters for any network topology. This model is employed to formulate an integer nonlinear optimization problem, for which a messy genetic algorithm is proposed. The methodology is validated on an example problem, which is based on a rectangular agricultural crop of 49 fruit trees. The results revealed a 70% reduction in the coefficient of variation of the irrigation discharge rates, which was employed as a metric of irrigation uniformity. This caused a 75% reduction in the water excess. The results demonstrate that the uniformity can be improved simply by choosing a proper connectivity layout to build the pipe network.

  • Deployment and verification of custom autonomous low-budget IoT devices for image feature extraction in wheat
    F. Martinez, James B. Romaine, J. M. Manzano, Carmelina Ierardi, and Pablo Millán Gata
    Institute of Electrical and Electronics Engineers (IEEE)

  • Stochastic Model Predictive Control for Irrigation: Addressing Solar and Rain Uncertainties to Enhance Sustainable Productivity
    P. Velarde, G.B. Caceres, and J.M. Manzano
    IEEE
    This work addresses a challenging agricultural control problem: to take into account environmental uncertainties (precipitation and solar radiance) in irrigation policies. To tackle these uncertainties, a stochastic model predictive control approach is designed, wherein each type of uncertainty is addressed using two different techniques tailored to effectively counteract them. Simulation experiments were conducted using real-world data spanning various types of days to validate the efficacy of the proposed approach. The results were benchmarked against other methods, showcasing the significant advantages of the proposed approach in terms of accuracy and robustness in agricultural irrigation control in the face of uncertainties. Therefore, this probabilistic approach also offers an effective solution to manage uncertainties and water resources, enhancing the productivity and sustainability of the sector.

  • CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
    Beatrice Sonzogni, José María Manzano, Marco Polver, Fabio Previdi, and Antonio Ferramosca
    Elsevier BV




  • Efficient FPGA Parallelization of Lipschitz Interpolation for Real-Time Decision-Making
    J. M. Nadales, J. M. Manzano, A. Barriga, and D. Limon
    Institute of Electrical and Electronics Engineers (IEEE)
    One of the main open challenges in the field of learning-based control is the design of computing architectures able to process data in an efficient way. This is of particular importance when time constraints must be met, as, for instance, in real-time decision-making systems operating at high frequencies or when a vast amount of data must be processed. In this respect, field-programmable gate array (FPGA)-based parallel processing architectures have been hailed as a potential solution to this problem. In this article, a low-level design methodology for the implementation on FPGA platforms of Lipschitz interpolation (LI) algorithms is presented. The proposed design procedure exploits the potential parallelism of the LI algorithm and allows the user to optimize the area and energy resources of the resulting implementation. Besides, the proposed design allows to know in advance a tight bound of the error committed by the FPGA due to the representation format. Therefore, the resulting implementation is a highly parallelized and a fast architecture with an optimal use of the resources and consumption and with a fixed numerical error bound. These facts flawlessly suit the desirable specifications of learning-based control devices. As an illustrative case study, the proposed algorithm and architecture have been used to learn a nonlinear model predictive control law applied to self-balance a two-wheel robot. The results show how computational times are several orders of magnitude reduced by employing the proposed parallel architecture, rather than sequentially running the algorithm on an embedded ARM-CPU-based platform.

  • Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles
    Guillermo Bejarano, José María Manzano, José Ramón Salvador, and Daniel Limon
    Elsevier BV


  • Results on spatio-temporal estimation of temperature and soil moisture in la Colmena (Paraguay)
    J.M. Manzano, Luis Orihuela, Erid Pacheco, and Mario Pereira
    Elsevier BV

  • Irrigation control by mimicry
    J.M. Manzano, J. Bareiro, G.B. Cáceres, J.R. Salvador, and P. Millán
    Elsevier BV

  • Experimental validation of robust non-linear state observers for autonomous surface vehicles equipped with position sensors
    Thalia A. Morel, Guillermo Bejarano, Jose Maria Manzano, and Luis Orihuela
    IEEE
    This paper describes the application and experimental validation of two different extended state observers for autonomous surface vehicles. Only the vessel position and orientation are assumed to be measured, being both estimators able to recover, not only the velocities, but also the so-called lumped generalised disturbance, which groups both environmental disturbances and nonlinear/unmodelled vessel dynamics. The experimental platform and the open-loop experiments performed to assess and validate the performance of both estimators are described. The experimental results confirm the prospects given by their previously published simulation results, though some room for improvement is detected in the estimation of the state dynamics.

  • Online learning constrained model predictive control based on double prediction
    J. M. Manzano, D. Muñoz de la Peña, J. Calliess, and D. Limon
    Wiley
    A data‐based predictive controller is proposed, offering both robust stability guarantees and online learning capabilities. To merge these two properties in a single controller, a double‐prediction approach is taken. On the one hand, a safe prediction is computed using Lipschitz interpolation on the basis of an offline identification dataset, which guarantees safety of the controlled system. On the other hand, the controller also benefits from the use of a second online learning‐based prediction as measurements incrementally become available over time. Sufficient conditions for robust stability and constraint satisfaction are given. Illustrations of the approach are provided in a simulated case study.

  • Componentwise Hölder Inference for Robust Learning-Based MPC
    Jose Maria Manzano, David Munoz de la Pena, Jan-Peter Calliess, and Daniel Limon
    Institute of Electrical and Electronics Engineers (IEEE)
    This article presents a novel learning method based on componentwise Hölder continuity, which allows one to consider independently the contribution of each input to each output of the function to be learned. The method provides a bounded prediction error, and its learning property is proven. It can be used to obtain a predictor for a nonlinear robust learning-based predictive controller for constrained systems. The resulting controller achieves better closed loop performance and larger domains of attraction than learning methods that only consider nonlinear set membership, as illustrated by a case study.

  • Oracle-based economic predictive control
    José María Manzano, David Muñoz de la Peña, and Daniel Limon
    Elsevier BV

  • Nonlinear model predictive control applied to robust guidance of autonomous surface vehicles
    Jose Maria Manzano, Jose Ramon Salvador, Guillermo Bejarano, and Daniel Limon
    IEEE
    This paper proposes a nonlinear model predictive control-based guidance strategy for autonomous surface vehicles, focused on the path following approach to motion control. The application of this strategy, in addition to overcome the drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for tracking the references provided by the guidance high-level strategy. The robust and stable features of the proposed strategy are discussed, while the effectiveness and the advantages over other nonlinear guidance laws are illustrated through a complete set of simulations.

  • Implementation of Fast Predictive Controllers on FPGA Platforms based on Parallel Lipschitz Interpolation
    J.M. Nadales, J.M. Manzano, A. Barriga, and D. Limon
    IEEE
    The implementation of nonlinear model predictive controllers for systems operating at high frequencies constitutes a significant challenge, mainly because of the complexity and time consumption of the optimization problem involved. An alternative that has been proposed is the employment of data-driven techniques to offline learn the control law, and then to implement it on a target embedded platform. Following this trend, in this paper we propose the implementation of predictive controllers on FPGA platforms making use of a parallel version of the machine learning technique known as Lipschitz interpolation. By doing this, computation time can be enormously accelerated. The results are compared to those obtained when the sequential algorithm runs on standard CPU platforms, and when the system is controlled by solving the optimization problem online, in terms of the error made and computing time. This method is validated in a case study where the nonlinear model predictive controller is employed to control a self-balancing two-wheel robot.

  • Robust learning-based MPC for nonlinear constrained systems
    José María Manzano, Daniel Limon, David Muñoz de la Peña, and Jan-Peter Calliess
    Elsevier BV

  • Online learning robust MPC: An exploration-exploitation approach
    J.M. Manzano, J. Calliess, D. Muñoz de la Peña, and D. Limon
    Elsevier BV

  • Data-based Robust MPC with Componentwise Hölder Kinky Inference
    J.M. Manzano, D. Limon, D. Munoz de la Pena, and J.P. Calliess
    IEEE
    The authors have recently developed predictive controllers based on prediction models derived from experimental data, by means of a class of Hölder interpolation called kinky inference. This paper provides a step forward by proposing a novel estimation method based on componentwise Hölder interpolation. This allows to explicitly consider the contribution of each component on each output, yielding better estimations. Following the procedure used in previous works, this estimation method is used to provide a predictor for a nonlinear robust data-based predictive controller, whose performance and robustness is enhanced by the new setting. The properties of the proposed controller are demonstrated in a case study.

RECENT SCHOLAR PUBLICATIONS

  • Deployment of a Smart Irrigation Control System with Capacity-Based Moisture Sensors on a Production Farm
    L Orihuela, E Pacheco, J Bareiro, A Tapia, JM Manzano
    Journal of Irrigation and Drainage Engineering 151 (1), 04024039 2025

  • Data-driven spatio-temporal estimation of soil moisture and temperature based on Lipschitz interpolation
    JM Manzano, L Orihuela, E Pacheco, M Pereira
    ISA transactions 156, 535-550 2025

  • Reducing flow heterogeneity in drip irrigation networks using genetic algorithms
    AT Crdoba, JM Manzano
    Irrigation and Drainage 2024

  • Deployment and verification of custom autonomous low-budget iot devices for image feature extraction in wheat
    F Martinez, JB Romaine, JM Manzano, C Ierardi, P Millan
    IEEE Access 2024

  • Aproximacin a la identificacin no paramtrica de sistemas muestreados asncronamente mediante interpolacin de Lipschitz
    L Orihuela, JM Manzano
    Jornadas de Automtica 2024

  • Stochastic Model Predictive Control for Irrigation: addressing solar and rain uncertainties to enhance sustainable productivity
    P Velarde, GB Caceres, JM Manzano
    2024 European Control Conference (ECC), 388-393 2024

  • IFAC Journal of Systems and Control
    B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca
     2024

  • Insulin on Board safety constraint effect in a CHoKI-based MPC for Artificial Pancreas
    B Sonzogni, JM Manzano, F Previdi, A Ferramosca
    IFAC-PapersOnLine 58 (24), 257-262 2024

  • INNOVATING ENGINEERING EDUCATION: A CASE STUDY ON PROJECT-BASED LEARNING IN HOME AUTOMATION
    JM Manzano, JM Barroso
    EDULEARN24 Proceedings, 2647-2652 2024

  • CHoKI-Based MPC for Blood Glucose Regulation in Artificial Pancreas with Probabilistic Constraints
    B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca
    2023 62nd IEEE Conference on Decision and Control (CDC), 1619-1624 2023

  • CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
    B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca
    IFAC-PapersOnLine 56 (2), 9672-9677 2023

  • Diseo ptimo de redes de riego
    A Tapia Crdoba, JM Manzano
    XLIV Jornadas de Automtica, 489-494 2023

  • Aprendizaje basado en proyecto: montaje de un panel domtico
    JM Manzano
    XLIV Jornadas de Automtica, 241-246 2023

  • Input‐to‐state stable predictive control based on continuous projected kinky inference
    JM Manzano, D Muoz de la Pea, D Limon
    International Journal of Robust and Nonlinear Control 2022

  • Experimental validation of robust non-linear state observers for autonomous surface vehicles equipped with position sensors
    TA Morel, G Bejarano, JM Manzano, L Orihuela
    2022 IEEE Conference on Control Technology and Applications (CCTA), 357-362 2022

  • Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles
    G Bejarano, JM Manzano, JR Salvador, D Limon
    Ocean Engineering 258, 111764 2022

  • Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors
    JM Manzano, JR Salvador, JB Romaine, L Alvarado-Barrios
    Renewable Energy 194, 647-658 2022

  • Modelling and Identification of an Autonomous Surface Vehicle: Technical Report
    TA Morel, JM Manzano, G Bejarano, L Orihuela
     2022

  • Efficient FPGA parallelization of Lipschitz interpolation for real-time decision-making
    JM Nadales, JM Manzano, A Barriga, D Limn
    IEEE Transactions on Control Systems Technology 30 (5), 2163-2175 2022

  • Irrigation control by mimicry
    P Milln, JM Manzano, J Bareiro, GB Cceres, JR Salvador, ...
     2022

MOST CITED SCHOLAR PUBLICATIONS

  • Robust learning-based MPC for nonlinear constrained systems
    JM Manzano, D Limon, DM de la Pea, JP Calliess
    Automatica 117, 108948 2020
    Citations: 86

  • Stability of Gaussian process learning based output feedback model predictive control
    M Maiworm, D Limon, JM Manzano, R Findeisen
    IFAC-PapersOnLine 51 (20), 455-461 2018
    Citations: 59

  • Output Feedback MPC based on Smoothed Projected Kinky Inference
    JM Manzano, D Limon, D Muoz de la Pea, JP Calliess
    IET Control Theory & Applications 13 (6), 795-805 2019
    Citations: 44

  • Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles
    G Bejarano, JM Manzano, JR Salvador, D Limon
    Ocean Engineering 258, 111764 2022
    Citations: 35

  • Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors
    JM Manzano, JR Salvador, JB Romaine, L Alvarado-Barrios
    Renewable Energy 194, 647-658 2022
    Citations: 21

  • Robust data-based model predictive control for nonlinear constrained systems
    JM Manzano, D Limon, DM de la Pe, J Calliess
    IFAC-PapersOnLine 51 (20), 505-510 2018
    Citations: 18

  • Componentwise Hlder inference for robust learning-based MPC
    JM Manzano, D Muoz de la Pena, JP Calliess, D Limon
    IEEE Transactions on Automatic Control 2021
    Citations: 17

  • Localised kinky inference
    A Blaas, JM Manzano, D Limon, J Calliess
    2019 18th European Control Conference (ECC), 985-992 2019
    Citations: 15

  • Robust design through probabilistic maximization
    T Alamo, JM Manzano, EF Camacho
    Uncertainty in Complex Networked Systems: In Honor of Roberto Tempo, 247-274 2018
    Citations: 13

  • EEG—Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction
    M Pereira, JB Romaine, JR Salvador, Manzano, J Mara
    Brain Sciences 11 (4), 516 2021
    Citations: 12

  • CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
    B Sonzogni, JM Manzano, M Polver, F Previdi, A Ferramosca
    IFAC-PapersOnLine 56 (2), 9672-9677 2023
    Citations: 9

  • Efficient FPGA parallelization of Lipschitz interpolation for real-time decision-making
    JM Nadales, JM Manzano, A Barriga, D Limn
    IEEE Transactions on Control Systems Technology 30 (5), 2163-2175 2022
    Citations: 9

  • Online learning constrained model predictive control based on double prediction
    JM Manzano, D Muoz de la Pea, J Calliess, D Limon
    International Journal of Robust and Nonlinear Control 2020
    Citations: 8

  • Online learning robust MPC: an exploration-exploitation approach
    JM Manzano, J Calliess, D Munoz de la Pena, D Limon
    IFAC-PapersOnLine 53 (2), 5292-5297 2020
    Citations: 8

  • Modelling and Identification of an Autonomous Surface Vehicle: Technical Report
    TA Morel, JM Manzano, G Bejarano, L Orihuela
     2022
    Citations: 7

  • Oracle-based economic predictive control
    JM Manzano, DM de la Pea, D Limon
    Computers & Chemical Engineering, 107434 2021
    Citations: 6

  • Input‐to‐state stable predictive control based on continuous projected kinky inference
    JM Manzano, D Muoz de la Pea, D Limon
    International Journal of Robust and Nonlinear Control 2022
    Citations: 4

  • Data-based Robust MPC with Componentwise Hlder Kinky Inference
    JM Manzano, D Limon, DM de la Pea, JP Calliess
    2019 IEEE 58th Conference on Decision and Control (CDC), 6449-6454 2019
    Citations: 4

  • Control predictivo basado en datos
    JM Manzano, D Limn, T lamo Cantarero, JP Callies
    Actas de las XXXVIII Jornadas de Automtica 2017
    Citations: 4

  • Experimental validation of robust non-linear state observers for autonomous surface vehicles equipped with position sensors
    TA Morel, G Bejarano, JM Manzano, L Orihuela
    2022 IEEE Conference on Control Technology and Applications (CCTA), 357-362 2022
    Citations: 3