GPC mediante descomposición en valores singulares (SVD). Análisis de componentes principales (PCA) y criterios de selección

El control predictivo basado en modelos o Model Predictive Control (MPC), no hace referencia al diseño concreto de un controlador sino más bien a un conjunto de ideas o características para el desarrollo de estrategias de control que, aplicadas en un mayor o menor grado, dan lugar a diferentes tipos de controladores con estructuras similares. El MPC es una de las técnicas de control que más se ha desarrollado en los ámbitos académico e industrial en las últimas décadas debido sobre todo a su simplicidad y eficiencia. Sin embargo, no es fácil relacionar los parámetros de ajuste del controlador y las prestaciones del bucle cerrado. En este sentido, es importante diseñar algoritmos de control predictivo que garanticen la estabilidad nominal del bucle cerrado, con tiempos de cálculo pequeños y con un significado claro de sus parámetros sobre las prestaciones del sistema o sobre el esfuerzo de control. La aportación fundamental de esta tesis está relacionada con la definición de un nuevo tipo de controlador predictivo, el PC-GPC, versión modificada de un GPC estándar. En este controlador se ha sustituido el factor de ponderación de la acción de control por un nuevo parámetro denominado número de componentes principales (NPC). La relación entre el nuevo parámetro (NPC) y algunos indicadores numéricos, como la norma del vector de acciones de control o el número de condición de la matriz dinámica G, hacen que su elección esté basada en criterios menos subjetivos que la ponderación de las acciones de control. Además, se ha analizado este tipo de controlador tanto en el ámbito de procesos SISO como MIMO, así como sus características de robustez y estabilidad. Por otro lado, se ha deducido un método de cálculo de un controlador PC-GPC para garantizar la estabilidad nominal de bucle cerrado, cuando el modelo conocido es exacto.Sanchís Saez, J. (2002). GPC mediante descomposición en valores singulares (SVD). Análisis de componentes principales (PCA) y criterios de selección [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4924Palanci

A graphical user interface for PCA-based MSPC: A benchmark software for multivariate statistical process control in MATLAB

[EN] A Graphical User Interface (GUI) is developed in MATLAB as a tutorial for understanding the PCA-based MSPC strategy. The software allows users to analyze both simulated and external data sets. Simulated data are obtained from a nonlinear model of a binary distillation column implemented in Simulink. The nonlinear model has four manipulated variables, four controlled variables and three input measured disturbances, plus 41 M fractions corresponding to every column stage. The methodology for PCA-based MSPC is implemented in two phases. During Phase I, the user can simulate the distillation column under normal operating conditions at three different operating points. When the simulation is finished, the GUI obtains the corresponding PCA model automatically. In Phase II, the user can simulate several scenarios with different combinations of disturbances and failures and monitor them using Squared Prediction Error (SPE) and T-2 control charts. Contribution plots are used to diagnose the original variables responsible of such abnormal situations. The software also incorporates the possibility to analyze external multivariate process datasets.Research in this study was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant DPI2017-82896-C2-1-R.Villalba-Torán, PM.; Sanchís Saez, J.; Ferrer, A. (2019). A graphical user interface for PCA-based MSPC: A benchmark software for multivariate statistical process control in MATLAB. Chemometrics and Intelligent Laboratory Systems. 185:135-152. https://doi.org/10.1016/j.chemolab.2018.12.004S13515218

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).Reynoso Meza, G.; Sanchís Saez, J.; Blasco, X.; Freire, RZ. (2016). Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning. Expert Systems with Applications. 51:120-133. doi:10.1016/j.eswa.2015.11.028S1201335

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. Ocean Engineering, 151, 105-114. doi:10.1016/j.oceaneng.2018.01.032Ramasamy, S., Sabatini, R., Gardi, A., & Liu, J. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid. Aerospace Science and Technology, 55, 344-358. doi:10.1016/j.ast.2016.05.020Zhu, L., Cheng, X., & Yuan, F.-G. (2016). A 3D collision avoidance strategy for UAV with physical constraints. Measurement, 77, 40-49. doi:10.1016/j.measurement.2015.09.006Chee, K. Y., & Zhong, Z. W. (2013). Control, navigation and collision avoidance for an unmanned aerial vehicle. Sensors and Actuators A: Physical, 190, 66-76. doi:10.1016/j.sna.2012.11.017Courbon, J., Mezouar, Y., Guénard, N., & Martinet, P. (2010). Vision-based navigation of unmanned aerial vehicles. Control Engineering Practice, 18(7), 789-799. doi:10.1016/j.conengprac.2010.03.004Aguilar, W., & Morales, S. (2016). 3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms. Electronics, 5(4), 70. doi:10.3390/electronics5040070Yan, F., Liu, Y.-S., & Xiao, J.-Z. (2013). Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method. International Journal of Automation and Computing, 10(6), 525-533. doi:10.1007/s11633-013-0750-9Yeh, H.-Y., Thomas, S., Eppstein, D., & Amato, N. M. (2012). UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2012.6385875Liang, Y., & Xu, L. (2009). Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC ’09. doi:10.1145/1543834.1543875Liu, J., Yang, J., Liu, H., Tian, X., & Gao, M. (2016). An improved ant colony algorithm for robot path planning. Soft Computing, 21(19), 5829-5839. doi:10.1007/s00500-016-2161-7Cao, H., Sun, S., Zhang, K., & Tang, Z. (2016). Visualized trajectory planning of flexible redundant robotic arm using a novel hybrid algorithm. Optik, 127(20), 9974-9983. doi:10.1016/j.ijleo.2016.07.078Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics, 7(1), 24-37. doi:10.1108/ijicc-02-2014-0005Pandey, A., & Parhi, D. R. (2017). Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Defence Technology, 13(1), 47-58. doi:10.1016/j.dt.2017.01.001Samaniego, F., Sanchis, J., García-Nieto, S., & Simarro, R. (2019). Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs. Electronics, 8(3), 306. doi:10.3390/electronics8030306Dubins, L. E. (1957). On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents. American Journal of Mathematics, 79(3), 497. doi:10.2307/2372560Fleury, S., Soueres, P., Laumond, J.-P., & Chatila, R. (1995). Primitives for smoothing mobile robot trajectories. IEEE Transactions on Robotics and Automation, 11(3), 441-448. doi:10.1109/70.388788Vanegas, G., Samaniego, F., Girbes, V., Armesto, L., & Garcia-Nieto, S. (2018). Smooth 3D path planning for non-holonomic UAVs. 2018 7th International Conference on Systems and Control (ICSC). doi:10.1109/icosc.2018.8587835Brezak, M., & Petrovic, I. (2014). Real-time Approximation of Clothoids With Bounded Error for Path Planning Applications. IEEE Transactions on Robotics, 30(2), 507-515. doi:10.1109/tro.2013.2283928Barsky, B. A., & DeRose, T. D. (1989). Geometric continuity of parametric curves: three equivalent characterizations. IEEE Computer Graphics and Applications, 9(6), 60-69. doi:10.1109/38.41470Kim, H., Kim, D., Shin, J.-U., Kim, H., & Myung, H. (2014). Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Engineering, 84, 37-44. doi:10.1016/j.oceaneng.2014.03.034Isaacs, J., & Hespanha, J. (2013). Dubins Traveling Salesman Problem with Neighborhoods: A Graph-Based Approach. Algorithms, 6(1), 84-99. doi:10.3390/a6010084Masehian, E., & Kakahaji, H. (2014). NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments. Robotica, 32(7), 1101-1123. doi:10.1017/s0263574713001276Fraichard, T., & Scheuer, A. (2004). From Reeds and Shepp’s to Continuous-Curvature Paths. IEEE Transactions on Robotics, 20(6), 1025-1035. doi:10.1109/tro.2004.833789Pepy, R., Lambert, A., & Mounier, H. (s. f.). Path Planning using a Dynamic Vehicle Model. 2006 2nd International Conference on Information & Communication Technologies. doi:10.1109/ictta.2006.1684472Girbés, V., Vanegas, G., & Armesto, L. (2019). Clothoid-Based Three-Dimensional Curve for Attitude Planning. Journal of Guidance, Control, and Dynamics, 42(8), 1886-1898. doi:10.2514/1.g003551De Lorenzis, L., Wriggers, P., & Hughes, T. J. R. (2014). Isogeometric contact: a review. GAMM-Mitteilungen, 37(1), 85-123. doi:10.1002/gamm.201410005Pigounakis, K. G., Sapidis, N. S., & Kaklis, P. D. (1996). Fairing Spatial B-Spline Curves. Journal of Ship Research, 40(04), 351-367. doi:10.5957/jsr.1996.40.4.351Pérez, L. H., Aguilar, M. C. M., Sánchez, N. M., & Montesinos, A. F. (2018). Path Planning Based on Parametric Curves. Advanced Path Planning for Mobile Entities. doi:10.5772/intechopen.72574Huh, U.-Y., & Chang, S.-R. (2014). A G2 Continuous Path-smoothing Algorithm Using Modified Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(2), 25. doi:10.5772/57340Chang, S.-R., & Huh, U.-Y. (2014). A Collision-Free G2 Continuous Path-Smoothing Algorithm Using Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(12), 194. doi:10.5772/59463Yaochu Jin, & Sendhoff, B. (2008). Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(3), 397-415. doi:10.1109/tsmcc.2008.919172Velasco-Carrau, J., García-Nieto, S., Salcedo, J. V., & Bishop, R. H. (2016). Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification. Journal of Guidance, Control, and Dynamics, 39(2), 372-389. doi:10.2514/1.g001294Honig, E., Schucking, E. L., & Vishveshwara, C. V. (1974). Motion of charged particles in homogeneous electromagnetic fields. Journal of Mathematical Physics, 15(6), 774-781. doi:10.1063/1.1666728Iyer, B. R., & Vishveshwara, C. V. (1988). The Frenet-Serret formalism and black holes in higher dimensions. Classical and Quantum Gravity, 5(7), 961-970. doi:10.1088/0264-9381/5/7/005Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm

[EN] In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder¿Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.This work was supported by the Vanier Canada Graduate Scholarship, the Michael Smith Foreign Study Supplements Program from the Natural Sciences and Engineering Research Council of Canada and by the Ministerio de Economia y Competitividad (Spain), project DPI2015-71443-R. It was also supported by the Bourse Mobilite Etudiante from Ministere de l'Education du Quebec, the CEMF Claudette MacKay-Lassonde Graduate Engineering Ambassador Award and the SWAAC Bourseau merite pour etudiantes de cycles superieurs.Blondin, MJ.; Sanchís Saez, J.; Sicard, P.; Herrero Durá, JM. (2018). New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm. Applied Soft Computing. 62:216-229. https://doi.org/10.1016/j.asoc.2017.10.007S2162296

Controller tuning by means of multi-objective optimization algorithms: a global tuning framework

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821

A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization

[EN] New challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented - a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem. (c) 2008 Elsevier Inc. All rights reserved.Partially supported by MEC (Spanish Government) and FEDER funds: Projects DPI2005-07835, DPI2004-8383-C03-02 and GVA-026.Blasco, X.; Herrero Durá, JM.; Sanchís Saez, J.; Martínez Iranzo, MA. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences. 178(20):3908-3928. https://doi.org/10.1016/j.ins.2008.06.010S390839281782

WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy

[EN] Several approaches have been presented to identify Wiener-Hammerstein models, most of them starting from a linear dynamic model whose poles and zeros are distributed around the static non- linearity. To achieve good precision in the estimation, the Best Linear Approximation (BLA) has usually been used to represent the linear dynamics, while static non-linearity has been arbitrarily parameterised without considering model complexity. In this paper, identification of Wiener, Hammerstein or Wiener-Hammerstein models is stated as a multiobjective optimisation problem (MOP), with a trade-off between accuracy and model complexity. Precision is quantified with the Mean-Absolute-Error (MAE) between the real and estimated output, while complexity is based on the number of poles, zeros and points of the static non- linearity. To solve the MOP, WH-MOEA, a new multiobjective evolutionary algorithm (MOEA) is proposed. From a linear structure, WH-MOEA will generate a set of optimal models considering a static non-linearity with a variable number of points. Using WH-MOEA, a procedure is also proposed to analyse various linear structures with different numbers of poles and zeros (known as design concepts). A comparison of the Pareto fronts of each design concept allows a more in-depth analysis to select the most appropriate model according to the user¿s needs. Finally, a complex numerical example and a real thermal process based on a Peltier cell are identified, showing the procedure¿s goodness. The results show that it can be useful to consider the simultaneously precision and complexity of a block-oriented model (Wiener, Hammerstein or Wiener- Hammerstein) in a non-linear process identification.This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, Spain, under Grant RTI2018-096904-B-I00-AR, and in part by the Salesian Polytechnic University of Ecuador through a Ph.D. scholarships granted to J. Zambrano.Zambrano, J.; Sanchís Saez, J.; Herrero Durá, JM.; Martínez Iranzo, MA. (2020). WH-MOEA: A Multi-Objective Evolutionary Algorithm for Wiener-Hammerstein System Identification. A Novel Approach for Trade-Off Analysis Between Complexity and Accuracy. IEEE Access. 8:228655-228674. https://doi.org/10.1109/ACCESS.2020.3046352228655228674

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