Control predictivo con restricciones e independiente de modelo para el p´endulo de Furuta

[Resumen] Este trabajo presenta el control de seguimiento de un péndulo Furuta basado en el diseño de dos lazos paralelos con restricciones, rechazo activo de perturbaciones y predicción de salidas. Para cada subsistema, se asume que la planta gobernada tiene una dinámica de primer orden más integrador (FOPI). Así, se calcula una ley de control predictivo aplicando una estrategia de horizonte deslizante en dicha planta (FOPI). La diferencia entre la dinámica real y el modelo de predicción asumido se compensa mediante el mecanismo de rechazo de perturbaciones heredado del control activo de rechazo de perturbaciones (ADRC) e incorporado en los lazos. Para el diseño del control no se realiza ninguna identificación de modelos ni linealización matemática. Además, la estrategia permite incorporar las restricciones reales del sistema. Este trabajo valida con resultados prometedores la arquitectura denominada Modified Active Disturbance Rejection Predictive Control (MADRPC) mediante la estabilización de un sistema mecánico subactuado considerando las variables restringidas y en ausencia de un modelo nominal, en contraste con el enfoque estándar en el Control Predictivo de Modelos (MPC) en espacio de estados.[Abstract] This paper presents the tracking control of a Furuta pendulum based on the design of two parallel-constrained loops with active disturbance rejection and outputs predictions. For each subsystem, it is assumed that the governed plant resembles firstorder plus integrator (FOPI) dynamics. So, a predictive control law is computed by applying a receding horizon strategy in such FOPI plant. The mismatch between the actual dynamics and the assumed prediction model is compensated through the disturbance rejection mechanism inherited from the Active Disturbance Rejection Control (ADRC) incorporated in the loops. No modelling identification or mathematical linearisation is performed for the control design. Moreover, the strategy allows the incorporation of the actual system constraints. This work validates with promising results the architecture named Modified Active Disturbance Rejection Predictive Control (MADRPC) by stabilising a sub-actuated mechanical system considering the constrained variables and in the absence of a nominal model, in contrast to the standard approach in the state-space Model Predictive Control (MPC).Colombia. Ministerio de Ciencia, Tecnología e Innovación; Scholarship 885Generalitat Valenciana; CIAICO/2021/064Ministerio de Ciencia e Innovación; PID2020-120087GBC21Ministerio de Ciencia e Innovación; PID2020-119468RA-I0

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

[EN] Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness.This work was partially funded by project RTI2018-096904-B-I00 from the Spanish Ministry of Economy and by project AICO/2019/055 from Generalitat Valenciana.Ortiz, A.; Garcia-Nieto, S.; Simarro Fernández, R. (2021). Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking. Electronics. 10(3):1-31. https://doi.org/10.3390/electronics1003033113110

Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

[EN] Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovacion y Universidades for providing funding through the project RTI2018-096904-B-I00 and the local administration Generalitat Valenciana through projects GV/2017/029 and AICO/2019/055. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work.Samaniego, F.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2020). Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs. Electronics. 9(1):1-23. https://doi.org/10.3390/electronics9010051S12391Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 127-140. doi:10.1016/j.trf.2015.04.014Münzer, S., Zimmer, H. D., Schwalm, M., Baus, J., & Aslan, I. (2006). Computer-assisted navigation and the acquisition of route and survey knowledge. Journal of Environmental Psychology, 26(4), 300-308. doi:10.1016/j.jenvp.2006.08.001Morales, Y., Kallakuri, N., Shinozawa, K., Miyashita, T., & Hagita, N. (2013). Human-comfortable navigation for an autonomous robotic wheelchair. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2013.6696743Krotkov, E., & Hebert, M. (s. f.). Mapping and positioning for a prototype lunar rover. Proceedings of 1995 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1995.525697Rodriguez-Seda, E. J. (2014). Decentralized trajectory tracking with collision avoidance control for teams of unmanned vehicles with constant speed. 2014 American Control Conference. doi:10.1109/acc.2014.6859184Xiaoping Ren, & Zixing Cai. (2010). Kinematics model of unmanned driving vehicle. 2010 8th World Congress on Intelligent Control and Automation. doi:10.1109/wcica.2010.5554512Jun, J.-Y., Saut, J.-P., & Benamar, F. (2016). Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains. Robotics and Autonomous Systems, 75, 325-339. doi:10.1016/j.robot.2015.09.014Li, Y., Ding, L., & Liu, G. (2016). Attitude-based dynamic and kinematic models for wheels of mobile robot on deformable slope. Robotics and Autonomous Systems, 75, 161-175. doi:10.1016/j.robot.2015.10.006Mekonnen, G., Kumar, S., & Pathak, P. M. (2016). Wireless hybrid visual servoing of omnidirectional wheeled mobile robots. Robotics and Autonomous Systems, 75, 450-462. doi:10.1016/j.robot.2015.08.008Xu, J., Wang, M., & Qiao, L. (2015). Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles. Ocean Engineering, 105, 54-63. doi:10.1016/j.oceaneng.2015.06.022Gafurov, S. A., & Klochkov, E. V. (2015). Autonomous Unmanned Underwater Vehicles Development Tendencies. Procedia Engineering, 106, 141-148. doi:10.1016/j.proeng.2015.06.017Qi, X., & Cai, Z. (2018). Three-dimensional formation control based on nonlinear small gain method for multiple underactuated underwater vehicles. 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Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop

[EN] In general, the starting point for the complex task of designing a robust and efficient control system is the use of nominal models that allow to establish a first set of parameters for the selected control scheme. Once the initial stage of design is achieved, control engineers face the difficult task of Fine-Tuning for a more realistic environment, where the environment conditions are as similar as possible to the real system. For this reason, in the last decades the use of Hardware-in-The-Loop (HiL) systems has been introduced. This simulation technique guarantees realistic simulation environments to test the designs but without danger of damaging the equipment. Also, in this iterative process of Fine-Tuning, it is usual to use different (generally conflicting/opposed) criteria that take into account the sensitivities that always appear in every project, such as economic, security, robustness, performance, for example. In this framework, the use of multi-objective techniques are especially useful since they allow to study the different design alternatives based on the multiple existing criteria. Unfortunately, the combination of multi-objective techniques and verification schemes based on Hardware-In-The-Loop presents a high incompatibility. Since obtaining the optimal set of solutions requires a high computational cost that is greatly increased when using Hardware- In-the-Loop. For this reason, it is often necessary to use less realistic but more computationally efficient verification schemes such as Model in the Loop (MiL), Software in the Loop (SiL) and Processor in the Loop (PiL). In this paper, a combined methodology is presented, where multi-objective optimisation and multi-criteria decision making steps are sequentially performed to achieve a final control solution. The authors claim that while going towards the optimisation sequence over MiL -> SiL -> PiL -> HiL platforms, the complexity of the problem is unveiled to the designer, allowing to state meaningful design objectives. In addition, safety in the step between simulation and reality is significantly increased.The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for providing funding through the project DPI2015-71443-R and the grant BES-2012-056210. This work has been partially supported by the National Council of Scientific and Technological Development of Brazil (CNPq) through the BJT/304804/2014-2 and PQ-2/304066/2016-8 grants.Reynoso Meza, G.; Velasco-Carrau, J.; Garcia-Nieto, S.; Blasco, X. (2017). Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop. Engineering Applications of Artificial Intelligence. 64:52-66. https://doi.org/10.1016/j.engappai.2017.05.005S52666

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

[EN] A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3D trajectory. The flight environment will be considered as a 3D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for providing funding through the project DPI2015-71443-R and the local administration Generalitat Valenciana through the project GV/2017/029. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work.Samaniego-Riera, FE.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2019). Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs. Electronics. 8(3):1-21. https://doi.org/10.3390/electronics8030306S12183Valavanis, K. P., & Vachtsevanos, G. J. (Eds.). (2015). 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Tuning Rules for Active Disturbance Rejection Controllers via Multiobjective Optimization - A Guide for Parameters Computation Based on Robustness

[EN] A set of tuning rules for Linear Active Disturbance Rejection Controller (LADRC) with three different levels of compromise between disturbance rejection and robustness is presented. The tuning rules are the result of a Multiobjective Optimization Design (MOOD) procedure followed by curve fitting and are intended as a tool for designers who seek to implement LADRC by considering the load disturbance response of processes whose behavior is approximated by a general first-order system with delay. The validation of the proposed tuning rules is done through illustrative examples and the control of a nonlinear thermal process. Compared to classical PID (Proportional-Integral-Derivative) and other LADRC tuning methods, the derived functions offer an improvement in either disturbance rejection, robustness or both design objectives.This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00.Martínez, BV.; Sanchís Saez, J.; Garcia-Nieto, S.; Martínez Iranzo, MA. (2021). Tuning Rules for Active Disturbance Rejection Controllers via Multiobjective Optimization - A Guide for Parameters Computation Based on Robustness. Mathematics. 9(5):1-34. https://doi.org/10.3390/math90505171349

A Comparative Study of Stochastic Model Predictive Controllers

[EN] A comparative study of two state-of-the-art stochastic model predictive controllers for linear systems with parametric and additive uncertainties is presented. On the one hand, Stochastic Model Predictive Control (SMPC) is based on analytical methods and solves an optimal control problem (OCP) similar to a classic Model Predictive Control (MPC) with constraints. SMPC defines probabilistic constraints on the states, which are transformed into equivalent deterministic ones. On the other hand, Scenario-based Model Predictive Control (SCMPC) solves an OCP for a specified number of random realizations of uncertainties, also called scenarios. In this paper, Classic MPC, SMPC and SCMPC are compared through two numerical examples. Thanks to several Monte-Carlo simulations, performances of classic MPC, SMPC and SCMPC are compared using several criteria, such as number of successful runs, number of times the constraints are violated, integral absolute error and computational cost. Moreover, a Stochastic Model Predictive Control Toolbox was developed by the authors, available on MATLAB Central, in which it is possible to simulate a SMPC or a SCMPC to control multivariable linear systems with additive disturbances. This software was used to carry out part of the simulations of the numerical examples in this article and it can be used for results reproduction.Gonzalez, E.; Sanchís Saez, J.; Garcia-Nieto, S.; Salcedo-Romero-De-Ávila, J. (2020). A Comparative Study of Stochastic Model Predictive Controllers. Electronics. 9(12):1-22. https://doi.org/10.3390/electronics9122078S12291

Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems

[EN] This paper presents a novel control strategy that provides active disturbance rejection predictive control on constrained systems with no nominal identified model. The proposed loop relaxes the modelling requirement to a fixed discrete-time state¿space realisation of a first-order plus integrator plant despite the nature of the controlled process. A third-order discrete Extended State Observer (ESO) estimates the model mismatch and assumed plant states. Moreover, the constraints handling is tackled by incorporating the compensation term related to the total perturbation in the definition of the optimisation problem constraints. The proposal merges in a new way state¿space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) into an architecture suitable for the servo-regulatory operation of linear and non-linear systems, as shown through validation examples.This work has been supported by MCIN/AEI/10.13039/501100011033 [Project PID2020-120087GB-C21] , MCIN/AEI/10.13039/501100011033 [Project PID2020-119468O-I00] , the Generalitat Valenciana regional government, Spain [Project CIAICO/2021/064] , and the Ministry of Science, Technology and Innovation of Colombia [scholarship programme 885] .Martínez-Carvajal, BV.; Sanchís Saez, J.; Garcia-Nieto, S.; Martínez Iranzo, MA. (2023). Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems. ISA Transactions. 142:148-163. https://doi.org/10.1016/j.isatra.2023.08.01114816314

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

[EN] The design of racing drones brings quite a thrilling challenge from a flight dynamics point of view. This work aims to offer a single-based simulation platform combining its geometric design, trajectory control, and guidance of racing drones. Also, it is reckoned from a pilot¿s view in a classic FPV competition. Hence, it is an active platform for studying racing drones¿ design founded on dynamics, with fifteen different drone models. It is one of the few existing platforms that combine all aspects of racing drones in a single simulation environment. Also, it is open access via Matlab Central - File Exchange.This work was partially supported by proyect PID2020-119468RA-I00 funded by MCIN/AEI/10.13039/501100011033.Castiblanco Quintero, J.; Garcia-Nieto, S.; Simarro Fernández, R.; Salcedo-Romero-De-Ávila, J. (2022). Co-simulation platform for geometric design, trajectory control and guidance of racing drones. International Journal of Micro Air Vehicles. 14:1-20. https://doi.org/10.1177/175682932211437851201

BIBO stabilisation of continuous time takagi sugeno systems under persistent perturbations and input saturation

[EN] This paper presents a novel approach to the design of fuzzy state feedback controllers for continuous-time non-linear systems with input saturation under persistent perturbations. It is assumed that all the states of the Takagi¿Sugeno (TS) fuzzy model representing a non-linear system are measurable. Such controllers achieve bounded input bounded output (BIBO) stabilisation in closed loop based on the computation of inescapable ellipsoids. These ellipsoids are computed with linear matrix inequalities (LMIs) that guarantee stabilisation with input saturation and persistent perturbations. In particular, two kinds of inescapable ellipsoids are computed when solving a multiobjective optimization problem: the maximum volume inescapable ellipsoids contained inside the validity domain of the TS fuzzy model and the smallest inescapable ellipsoids which guarantee a minimum *-norm (upper bound of the 1-norm) of the perturbed system. For every initial point contained in the maximum volume ellipsoid, the closed loop will enter the minimum *-norm ellipsoid after a finite time, and it will remain inside afterwards. Consequently, the designed controllers have a large domain of validity and ensure a small value for the 1-norm of closed loop.The authors wish to thank the Editor-in-Chief and the anonymous reviewers for their valuable comments and suggestions. This work has been funded by Ministerio de Economia y Competitividad (Spain) through the research project DPI2015-71443-R and by Generalitat Valenciana (Valencia, Spain) through the research project GV/2017/029.Salcedo-Romero-De-Ávila, J.; Martínez Iranzo, MA.; Garcia-Nieto, S.; Hilario Caballero, A. (2018). BIBO stabilisation of continuous time takagi sugeno systems under persistent perturbations and input saturation. International Journal of Applied Mathematics and Computer Science (Online). 28(3):457-472. https://doi.org/10.2478/amcs-2018-0035S45747228