Artículos (Ingeniería de Sistemas y Automática)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11342

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Implementation of Soft-Constrained MPC for Tracking Using Its Semi-Banded Problem Structure
(Institute of Electrical and Electronics Engineers, 2024-05) Gracia Villegas, Víctor Manuel; Krupa, Pablo; Limón Marruedo, Daniel; Alamo, Teodoro; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP950: Estimación, Predicción, Optimización y Control
Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issues, either because the system model is not accurate or due to the existence of external disturbances. To mitigate this problem, a solution adopted by the MPC community is the use of soft constraints. In this letter, we consider a not-so-typical methodology to encode soft constraints in a particular MPC formulation known as MPC for Tracking (MPCT), which has several advantages when compared to standard MPC formulations. The motivation behind the proposed encoding is to maintain the semi-banded structure of the ingredients of a recently proposed solver for the considered MPCT formulation, thus providing an efficient and fast solver when compared to alternative approaches from the literature. We show numerical results highlighting the benefits of the formulation and the computational efficiency of the solver.
Artificial-reference tracking MPC with probabilistically validated performance on industrial embedded systems
(Elsevier, 2026-09) Gracia Villegas, Víctor Manuel; Krupa, Pablo; Fele, Filiberto; Alamo, Teodoro; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP950: Estimación, Predicción, Optimización y Control
Industrial embedded systems are typically used to execute simple control algorithms due to their low computational resources. Despite these limitations, the implementation of advanced control techniques such as Model Predictive Control (MPC) has been explored by the control community in recent years, typically considering simple linear formulations or explicit ones to facilitate the online computation of the control input. These simplifications often lack features and properties that are desirable in real-world environments. This article presents an efficient implementation for embedded systems of MPC for tracking with artificial reference, solved via a recently developed structure-exploiting ADMM-based algorithm. This formulation is tailored to a wide range of applications by incorporating essential practical features at a small computational cost, including integration with an offset-free scheme, back-off parameters that enable constraint tightening, and soft constraints that preserve feasibility under disturbances or plant-model mismatch. This is accompanied with a framework for probabilistic performance validation of the closed-loop system over long-term operation. The applicability of the approach is illustrated on a Programmable Logic Controller (PLC), incorporated in a hardware-in-the-loop setup to control a nonlinear continuous stirred-tank reactor. The behavior of the closed-loop system is probabilistically validated with respect to constraint violations and the number of iterations required at each time step by the MPC optimization algorithm.
Stochastic Path Planning with Obstacle Avoidance for UAVs Using Covariance Control
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Garzelli, Alessandro; Benedikter, Boris; Zavoli, Alessandro; Martínez de Dios, José Ramiro; Suárez Fernández-Miranda, Alejandro; Ollero Baturone, Aníbal; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Unmanned aerial vehicles (UAVs) operating in uncertain environments must plan safe and efficient trajectories while avoiding obstacles. This work addresses this challenge by formulating UAV path planning as a stochastic optimal control problem using covariance control. The objective is to generate a closed-loop guidance policy that steers both the mean and covariance of the UAV’s state toward a desired target distribution while ensuring probabilistic collision avoidance with ellipsoidal obstacles. The stochastic problem is convexified and reformulated as a sequence of deterministic optimization problems, enabling efficient computation even from coarse initial guesses. Simulation results demonstrate that the proposed method successfully produces robust trajectories and feedback policies that satisfy chance constraints on obstacle avoidance and reach the target with prescribed statistical characteristics.
Fault Current Limiters for LVRT Enhancement in Wind Turbine Systems: Technologies, Trade-Offs, and Comparative Simulation Insights
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Firouzi, M.; Pourmirasghariyan, M.; Rouzbehi, Kumars; Arahal, Manuel R.; Ingeniería de Sistemas y Automática
This paper presents a comprehensive review and comparative evaluation of Fault Current Limiter (FCL) technologies for enhancing the Low-Voltage Ride-Through (LVRT) capability of Wind Turbine Generation Systems (WTGSs). Among various hardware solutions, FCLs have emerged as a particularly efficient and cost-effective approach to limit high fault currents and ensure grid code compliance. While the application of individual FCL types has been explored in the literature, a comparative analysis encompassing their technologies, inherent trade-offs, and performance insights remains lacking. To address this gap, this work examines the application of key FCL categories, including superconducting FCLs (SFCLs), resonance-type FCLs (RFCLs), and solid-state FCLs (SSFCLs), in both fixed-speed and variable-speed WTGS configurations. The paper synthesizes technological principles, assesses practical trade-offs (e.g., cost, response speed, scalability), and discusses critical insights derived from simulation-based comparisons. The consolidated findings aim to guide the selection, design, and future development of FCL solutions to enhance robust LVRT and provide reliable protection for modern wind power systems.
Cascaded Finite State Control for a Five-Phase Induction Machine
(MDPI (Multidisciplinary Digital Publishing Institute), 2026) Arahal, Manuel R.; Garrido Satué, Manuel; Pérez Vega-Leal, Alfredo; Ingeniería de Sistemas y Automática; Ingeniería Electrónica
The area of Finite State Model Predictive Control (FSMPC) has seen a rapid development in recent years. In particular, its application to multiphase induction machine drives has received attention due to the specific advantages of such systems. The FSMPC method has been shown to be flexible thanks to the combination of a model and a cost function. However, the selection of the weighting coefficients in the cost function remains an obstacle for practitioners. Existing tuning methods for weighting coefficients require a large dataset obtained by extensive experimentation. In this paper, a cascaded structure providing a means for cost function self tuning is proposed. Tests are conducted with a five-phase induction machine connected to a mechanical load. The results show that, for each speed and load, the cascaded structure yields weighting coefficients with improved results.
A hierarchical control system for optimizing crop production in a vertical farming container and its Internet of Things architecture
(Elsevier, 2026-03) García-Mañas, Francisco; Ruipérez-Algarra, Elia; Muñoz, Manuel; Rodríguez, Francisco; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This work presents a hierarchical control system to maximize profit for vertical farming in a shipping container. In its upper optimization layer, a model predictive control (MPC) approach was implemented to calculate the optimal crop cycle duration, the optimal duration of the artificial lighting periods, and the optimal air temperature setpoints, as variables with high impact on crop growth and directly linked to resource consumption and costs. In addition, to address commercial contract aspects, a minimum weight constraint to be met by the crop at the end of the cycle was incorporated. The MPC strategy uses a temperature model and a crop growth model, both based on first principles, which were calibrated and validated using climate data and destructive samples of lettuce crops from a real container located in southeastern Spain. For practical deployment, the hierarchical control system was conceptually implemented in a cloud platform under the Internet of Things (IoT) paradigm, which provides interoperability with sensors and actuators, remote supervision, and scalability to multiple containers. The responses provided by the hierarchical system are analyzed after three simulations involving the achievement of the optimal setpoints in a lower control layer, a sudden change in the minimum crop weight restriction, and a case in which the optimal setpoints are not reached during the crop cycle. In each simulation, the performance of the MPC strategy is compared against a baseline controller that operates with manually fixed setpoints. The proposed control system achieves production targets five days earlier, reducing energy consumption by 6.5% and increasing profitability by 2%. It would allow for an additional crop growth cycle each year, increasing container productivity (and therefore annual profits). Moreover, it is able to comply with variability in product demand, while the baseline controller would violate the maximum cycle duration constraint and might not yield any benefits. Regarding performance with uncertainty, the proposed control system can generate adequate setpoints to continue optimizing production, which shows that the system automates decision-making to assist farmers in achieving long-term objectives.
Experimental iterative learning control of a quadrotor in flight: A derivation of the state-dependent Riccati equation method
(Cambridge University Press, 2025-12) Nekoo, Saeed Rafee; Ollero Baturone, Aníbal; Ingeniería de Sistemas y Automática; European Commission (EC)
Learning has recently played a vital role in control engineering, producing numerous applications and facilitating easier control over systems; however, it has presented serious challenges in flight learning for unmanned platforms. Iterative learning control (ILC) is a practical method for cases needing repetition in control loops. This work focuses on the ILC of a quadrotor flight. An unstable flight might lead to a crash in the system and stop the iterations; hence, a base controller, the state-dependent Riccati equation (SDRE), is selected to stabilize the drone in the first loop. The ILC acts on top of the SDRE to increase the precision and force the system to learn to track trajectories better. The combination of ILC and SDRE was tested for stationary (fixed-base) systems without the risk of crashes; nonetheless, its implementation on a flying (mobile) system is reported for the first time. The gradient descent method shapes the training criteria for error reduction in the ILC. The proposed design is implemented on simulation and a real flight of a quadrotor in a series of tests, showing the effectiveness of the proposed input law. The nonlinear and optimal structure of the base controller and the complex iterative learning programming were challenges of this work, which were successfully addressed and demonstrated experimentally.
SimpleBox4Planet: environmental fate modelling of PFASs and their alternatives via the Enalos Cloud Platform
(Royal Society of Chemistry, 2026) Mintis, Dimitris G.; Papavasiliou, Constantinos; Varsou, Dimitra Danai; Tsoumanis, Andreas; Melagraki, Georgia; Seif, Johannes P.; Majó, Marc; Real Torres, Alejandro del; Serchi, Tommaso; Afantitis, Antreas; Ingeniería de Sistemas y Automática; European Union (UE); European Union's HORIZON
This work presents the development of SimpleBox4Planet, a user-friendly web application implementation of SimpleBox, and demonstrates its use in facilitating the assessment of the environmental fate of per- and polyfluoroalkyl substances (PFASs) as well as other chemicals of interest, with the aim of supporting research into safer chemical alternatives with lower environmental impact. The SimpleBox4Planet web application is freely accessible on the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/proplanet/simplebox4planet/ and https://www.enaloscloud.novamechanics.com/chiasma/simplebox4planet/). The SimpleBox4Planet web application integrates the SimpleBox (version 4.04) multimedia mass balance model (based on a ‘Mackay type’ model), accommodating both steady-state (level III) and quasi-dynamic (level IV) computations of mass flows and chemical concentrations across three environmental scales: regional, continental and global, while also considering the chemical distributions at each scale across environmental compartments, including air, soil, water and sediment, thus streamlining the workflow and enhancing visualisation of the model outcomes. The complexities related to modelling SimpleBox through MS Excel spreadsheets are eliminated through the design of the user-friendly graphical user interface (GUI) provided by SimpleBox4Planet. This interface enables users to input the physicochemical properties of any chemical of interest (based on its CAS number) from the CompTox Chemicals Dashboard either directly or dynamically through application programming interfaces (APIs), to define emission rates, and to configure landscape settings. Both expert and non-expert users can efficiently perform complex multimedia fate modelling, significantly broadening the tool's applicability in regulatory, academic, and industrial contexts. Furthermore, the platform facilitates integration with other tools and models, including Life Cycle Impact Assessment (LCIA) frameworks, and can be used as an input to risk assessment, to support the evaluation of both ecotoxicological and human health impacts.
Adaptive Virtual Inertia for AC Grids Connected to MT-HVDC Grid by Considering DC Voltage Stability
(Wiley, 2025-12) Astereki, Amir Arsalan; Monadi, Mehdi; Seifossadat, Seyed Ghodratolah; Saffarian, Alireza; Rouzbehi, Kumars; Ingeniería de Sistemas y Automática
The growing integration of power electronics converters (PECs) and multi-terminal high voltage DC (MT-HVDC) grids within the power system decreases the system's inertia. Conversely, maintaining the voltage level of the MT-HVDC grid is crucial for preserving the overall system's stability. One of the primary challenges in generating virtual inertia for AC grids connected to MT-HVDC grids is the further decline in DC voltage caused by the additional power absorption needed for virtual inertia provision. This indicates that the implementation of virtual inertia negatively impacts DC voltage levels. In order to elucidate this issue, the present study develops a small-signal model of the Cigre-DCS3, incorporating a virtual synchronous generator (VSG). This model aims to analyse the effects of VSG parameters on the stability characteristics of the system under consideration. This analysis reveals a conflicting interaction between the DC voltage droop control loop and the virtual inertia time constant in the VSGs, as the presence of virtual inertia tends to adversely affect the DC-side voltage stability. In response to this challenge, this paper introduces an innovative approach that integrates DC voltage stability considerations into the virtual inertia control loop. This integration aims to improve the dynamic response of VSGs while enhancing overall system reliability. The proposed method incorporates the rate of change of frequency, variations in frequency, and deviations in DC voltage to provide adaptive virtual inertia (AVI). Additionally, the stability of the presented controller is validated through Lyapunov stability analysis. Lastly, the simulation results illustrate the efficiency of the proposed approach in enhancing overall system performance.
Multi-Chiller Plant Under Demand Uncertainties: Predictive Versus Planned Approaches
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02) Garrido Satué, Manuel; Pérez Vega-Leal, Alfredo; Martínez Heredia, Juana María; Arahal, Manuel R.; Ingeniería de Sistemas y Automática; Ingeniería Electrónica
Recently, different techniques have been proposed for the scheduling and loading problems in cooling plants with chillers in a parallel configuration. Two broad groups can be considered: the online control-based group and the offline optimization-based group. The first group is exemplified by Model Predictive Control, where the selection of control moves provides a solution to both scheduling and loading. The second group includes Optimal Chiller Loading and Optimal Chiller Sequencing algorithms. They usually derive operating plans with some lead time in a batch-like fashion for long horizons. Both groups use forecasts of important factors such as the cooling demand and ambient conditions; hence, they have to deal with inaccuracies in the forecasts. In this paper, a comparison among these two groups is made considering demand uncertainties. The severity of the uncertainty is shown to play a role in the results as well as the controller tuning in the case of the predictive approach. The results are favorable to OCS with respect to overall consumption (up to 15%) but uses more on/off changes in the chiller’s operation (double in some cases).
Predictive receding-horizon multi-robot task allocation applied to the mapping of direct normal irradiance in a thermosolar power plant
(Elsevier, 2023-10) García Martín, Javier; Hanif, Muhammad; Hatanaka, Takeshi; Maestre Torreblanca, José María; Camacho, Eduardo F.; Ingeniería de Sistemas y Automática; European Union (UE); Ministerio de Ciencia e Innovación (MICIN). España
This article considers a robotic sensor network that measures the loss of irradiance in a thermosolar power plant due to moving clouds. To this end, a receding-horizon predictive algorithm is proposed for multi-robot task allocation. Despite the high nonlinearity of the problem, the experiments carried out varying the horizon size show that the proposed method has a good performance with small horizons and tasks moving in similar directions, outperforming a previously published approach based on genetic algorithms. Finally, realistic simulations performed on a solar plant implemented in Robot Operating System/Gazebo prove the feasibility of the proposed method and its potential to provide significant performance gains with a much lower investment than an equivalent fixed sensor network.
Optimum operating temperature of parabolic trough solar fields
(Elsevier, 2017-12) Navas Herrera, Sergio Jesús; Ollero, Pedro; Rodríguez Rubio, Francisco; Ingeniería de Sistemas y Automática; Ingeniería Química y Ambiental; Ministerio de Ciencia e Innovación (MICIN). España
This paper shows the relationship between the incident solar radiation and the optimum outlet temperature of a solar field to produce the highest amount of electrical power. Various simulations were made for different values of incident solar radiation, calculating for each one the optimum temperature which produces the maximum electrical power and demonstrating that to operate the field at the highest allowable temperature is not the optimal operating point from certain values of solar radiation; a situation which takes special relevance during cloudy days. These simulations were carried out using two connected models, one for the solar field and another one for the power cycle.
Optimal Control Applied to Distributed Solar Collector Fields with Partial Radiation
(Elsevier, 2018-01) Navas Herrera, Sergio Jesús; Rodríguez Rubio, Francisco; Ollero, Pedro; Lemos, João M.; Ingeniería de Sistemas y Automática; Ingeniería Química y Ambiental; Ministerio de Ciencia e Innovación (MICIN). España
This paper describes and assesses two strategies to control distributed solar collector fields, especially during days with partial radiation due to the passage of clouds. The main objective of these control strategies is to maximize the electrical power generated during different situations in which different parts of the solar field receive different degrees of solar radiation. Simulations were carried out using two connected models, one for the solar field (taking into account all of its loops), that includes the passage of clouds, and another one for the power cycle. The solar field simulated is a pilot plant, in which it is assumed that all the loops have the same characteristics; and the nominal power range of the Rankine cycle is 800–2330 kW. Finally, the improvement in electrical power achieved by both strategies is compared with a typical control strategy that tries to keep constant the outlet oil temperature of the field. This improvement varies between 4% for clear days and 5.7% for cloudy days.
Robust invariant sets for software rejuvenation via resampled dynamics
(Elsevier, 2026-04) Luque Martínez, Irene; Chanfreut, Paula; Maestre Torreblanca, José María; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This article introduces a method that enlarges the safe operational space of a system within the context of software rejuvenation (SWR), an active cyber-defense method. The presented approach computes a polyhedral inner safe set obtained through the definition of a SWR cycle that is employed to derive simpler resampled dynamics for which a maximal robust invariant set can be computed. Also, constraint satisfaction in the original system is guaranteed by a convenient mapping onto the resampled system. As a result, the state trajectory in the original system is confined within a tube with time-varying section. This technique is applied to a discrete-time linear system and compared with other approaches, showing that the proposed inner safe set offers broader operational margins and therefore enables the system to perform its mission over an enlarged domain.
Perch Like a Bird: Bio-Inspired Optimal Maneuvers and Nonlinear Control for Flapping-Wing Unmanned Aerial Vehicles
(Institute of Electrical and Electronics Engineers, 2026-02) Ruiz Páez, Cristina; Acosta Rodríguez, José Ángel; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP995: Multi-Robot and Control Systems
This research endeavors to design the perching maneuver and control in ornithopter robots. By analyzing the dynamic interplay between the robot’s flight dynamics, feedback loops, and the environmental constraints, we aim to advance our understanding of the perching maneuver, drawing parallels to biological systems. Inspired by the elegant control strategies observed in avian flight, we develop an optimal maneuver and a corresponding controller to achieve stable perching. The maneuver consists of a deceleration and a rapid pitch-up (vertical turn), which arises from analytically solving the optimization problem of minimal velocity at perch, subject to kinematic and dynamic constraints. The controller for the flapping frequency and tail symmetric deflection is nonlinear and adaptive, ensuring robustly stable perching. Indeed, such adaptive behavior in a sense incorporates homeostatic principles of cybernetics into the control system, enhancing the robot’s ability to adapt to unexpected disturbances and maintain a stable posture during the perching maneuver. The resulting autonomous perching maneuvers—closed-loop descent and turn—have been verified and validated, demonstrating excellent agreement with real bird perching trajectories reported in the literature. These findings lay the theoretical groundwork for the development of future prototypes that better imitate the skillful perching maneuvers of birds.
Safe Optimal Vessel Planning on Natural Inland Waterways
(Institute of Electrical and Electronics Engineers (IEEE), 2023-09) Moreno Nadales, Juan; Muñoz de la Peña Sequedo, David; Limón Marruedo, Daniel; Alamo, Teodoro; Ingeniería de Sistemas y Automática; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Agencia Estatal de Investigación. España
Despite inland ports play an essential role in today’s global logistics chain, many problems still remain unsolved. One of the main challenges that inland waterways face is the trip planning of cargo vessels. A correct trip planning does not only improve the efficiency and prestige of the port, but is also crucial to ensure the safety of operations. This issue is particularly critical in the case of natural waterways whose depth and width are conditioned by natural phenomena that, in principle, cannot be controlled. This is the case of the Guadalquivir river, a waterway that connects the Atlantic Ocean and the inland Port of Seville in the south of Spain. In this context, this work proposes a two-step solution to optimize the time needed for vessels to complete their journey through the waterway while considering the constraint imposed by the time-varying depth and encountering situations. The outcome is the set of times when vessels are required to cross a series of boundaries delimiting the different sections of the waterway. The advantages of the proposed approach are studied in contrast to a first-arrived first-served scheduling solution in terms of optimality and feasibility.
Modulation Analysis of Monovector and Multivector Predictive Control of Five-Phase Drives
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-01) Garrido Satué, Manuel; Martínez Heredia, Juana María; Mora Jiménez, José Luis; Ingeniería Electrónica; Ingeniería de Sistemas y Automática
The Finite State Model Predictive Control (FSMPC) of variable speed drives is the subject of many works in the recent literature. Many variants of FSMPC exist, each aiming at an aspect such as the complexity of the cost function, switching frequency, current quality, etc. In the case of multiphase drives, two popular variants are the monovector and multivector techniques. Despite past efforts to compare different techniques, the field must still reach a consensus regarding the relative merits of each one. This paper presents a new method to compare two families of FSMPC. The method is based on a reduced set of figures of merit using the current modulation index as the variable. The comparison is made for the equal usage of the power converter in terms of commutations. The results point to better values for the figures of merit for the monovector that, in addition, portrays more flexibility and better DC link usage.
An online stochastic MPC-based fault-tolerant optimization for microgrids
(Elsevier, 2023-01) Zafra Cabeza, Ascensión; Márquez Quintero, J.J.; Bordons Alba, Carlos; Ridao Carlini, Miguel Ángel; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
Due to the current energy dependence of society, the availability and correct functioning of microgrids are strategic issues to be dealt with. Currently, energy management systems are very focused on achieving systems that present a remarkable optimization of demand as well as a high degree of fault tolerance. This paper presents a novel Control Reconfiguration framework to manage faults based on Model Predictive Control (MPC). The proposal comprises fault detection, isolation and reconfiguration. The Fault Detection and Isolation method identifies the faults based on parity equations, structured residuals and stochastic thresholds, while the reconfiguration is focused on adapting the control law to the faulty scenario. Therefore, two different model predictive controllers are involved: MPC-1 drives the microgrid to the correct values and MPC-2 carries out the control reconfiguration, optimizing a multi-criteria objective function where outputs are the values of criteria. The new decision variables of the fault reconfiguration are the selection of mitigation actions to be performed to reduce the effects of faults. A novel formulation of this problem is provided. Experiments have been carried out on a real microgrid located in the laboratory to show the benefits of the method.
A fault detection and reconfiguration approach for MPC-based energy management in an experimental microgrid
(Elsevier, 2021-02) Márquez Quintero, J.J.; Zafra Cabeza, Ascensión; Bordons Alba, Carlos; Ridao Carlini, Miguel Ángel; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
Microgrids are getting a growing role in the evolution of the traditional electricity system towards a more distributed grid. Nowadays, efforts are being put into the development of applications that ensure the availability and the correct functioning of microgrids. Microgrids Energy Management Systems (EMS) must be able to manage faults and therefore, drive the system to a safe scenario. In this context, fault diagnosis, isolation and reconfiguration are main subjects to be dealt within microgrids. This paper presents a Model Predictive Control approach applied to energy management in microgrids from the point of view of fault mitigation. In order to detect faults online, the real behaviour and the model are compared in each sampling period through generated residuals. The thresholds used for the detection of faults are determined by the qualitative statistical decision theory. When true inconsistencies are detected, the information about faults occurrences is sent to a new reconfiguration block to recover the system executing mitigation actions. Experiments on a real laboratory-scaled microgrid have been carried out to show the benefits of the method. This work shows how the proposed scheme can be used as a tool which integrates a fault isolation and reconfiguration module taking into account disturbances, noise and modelling errors from a stochastic point of view.
Control Aware of Limitations of Manipulators with Claw for Aerial Robots Imitating Bird’s Skeleton
(Institute of Electrical and Electronics Engineers, 2021-10) Feliu Talegón, Daniel; Acosta Rodríguez, José Ángel; Ollero Baturone, Aníbal; Ingeniería de Sistemas y Automática; European Union (UE); TEP995: Multi-Robot and Control Systems; TEP151: Robótica, Visión y Control
Winged animals such as birds, flying mammals or insects have lightweight limbs which allow them to perform different tasks. Although in robotics there are some examples of winged robots (called ornithopters), it has not been yet studied how to add them some manipulation-like capabilities, similarly to the anatomy of animals limbs. Adding those capabilities to ornithopters will outperform multirotor platforms giving the possibility to perch in unaccessible places, grasp objects and perform some kind of manipulation while being in proximity to humans. The special manipulator imitates the anatomy of the birds, having a kinematic chain with actuated joints except the first passive one that resembles the claw of a bird with a grasping force. This work analyzes in depth these ornithopter-like manipulators and proposes a nonlinear controller aware of the limitation in the grasping force of the claw, modeled as static friction. The solution is based on a methodology to control constrained-nonlinear systems via diffeomorphisms providing an explicit controller with low torques demand to meet aerial requirements. It is verified on a realistic simulator with 5DOF links—claw, low/upp-er leg, body, neck and beak–, and experimentally validated in a simpler 3DOF prototype.

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