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

Control of the interaction of a gantry robot end effector with the environment by the adaptive behaviour of its joint drive actuators

The thesis examines a way in which the performance of the robot electric actuators can be precisely and accurately force controlled where there is a need for maintaining a stable specified contact force with an external environment. It describes the advantages of the proposed research, which eliminates the need for any external sensors and solely depends on the precise torque control of electric motors. The aim of the research is thus the development of a software based control system and then a proposal for possible inclusion of this control philosophy in existing range of automated manufacturing techniques.The primary aim of the research is to introduce force controlled behaviour in the electric actuators when the robot interacts with the environment, by measuring and controlling the contact forces between them. A software control system is developed and implemented on a robot gantry manipulator to follow two dimensional contours without the explicit geometrical knowledge of those contours. The torque signatures from the electric actuators are monitored and maintained within a desired force band. The secondary aim is the optimal design of the software controller structure. Experiments are performed and the mathematical model is validated against conventional Proportional Integral Derivative (PID) control. Fuzzy control is introduced in the software architecture to incorporate a sophisticated control. Investigation is carried out with the combination of PID and Fuzzy logic which depend on the geometrical complexity of the external environment to achieve the expected results

Tuning of Pareto-optimal robust controllers for multivariable systems. Application on helicopter of two-degress-of-freedom

[ES] La sintonización de controladores Pareto-óptimo robustos ha sido empleada para mejorar el rendimiento de un helicóptero de dos grados de libertad con un algoritmo de control lineal. El procedimiento de sintonización del controlador está basado en la minimización simultánea de las integrales de la suma del cuadrado del error y de la acción de control. Como resultado de dicha minimización y dado que los objetivos entran en conflicto, se obtiene un conjunto de soluciones que describen un frente de Pareto. Posteriormente, un proceso de análisis en los mismos es llevado a cabo para seleccionar los controladores a implementar en el sistema físico. Los resultados experimentales con los controladores seleccionados muestran que el procedimiento de ajuste es eficaz y práctico.[EN] The tuning of Pareto-optimal robust controllers was applied to improve the performance of a helicopter with two-degrees-of-freedom with a linear control algorithm. The tuning procedure is based on the simultaneous minimization of the integral of square sum of errors and the integral of square sum of control action. A 2D Pareto front is built with these integrals. Afterwards, a decision-making process is carried out to select the most preferable controller. Experimental results on the physical platform validate the tuning procedure as practical and reliable.Dirección General de Educación Tecnológica Superior (DGEST), Consejo Nacional de Ciencia y Tecnología (CONACyT), l Ministerio de Economía y Competitividad de España y Consejo Nacional de Desarrollo Científico y Tecnológico de BrasilCarrillo Ahumada, J.; Reynoso Meza, G.; Sanchís Saez, J.; García Nieto, S.; García Alvarado, M. (2015). Sintonización de controladores Pareto-óptimo robustos para sistemas multivariables. Aplicación en un helicóptero de 2 grados de libertad. Revista Iberoamericana de Automática e Informática industrial. 12(2):177-188. https://doi.org/10.1016/j.riai.2015.03.002OJS177188122Carrillo-Ahumada, J., Rodríguez-Jimenes, G. C., & García-Alvarado, M. A. (2011). Tuning optimal-robust linear MIMO controllers of chemical reactors by using Pareto optimality. Chemical Engineering Journal, 174(1), 357-367. doi:10.1016/j.cej.2011.09.007Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. doi:10.1109/mci.2006.1597059Corne, D.W., Knowles, J.D., 2007. Techniques for highly multiobjective optimisation: some nondominated points are better than others. En: Proceedings of the 9th annual conference on Genetic and evolutionary computation. GECCO ‘07. ACM, New York, NY, USA, pp. 773-780.CSS, 2012. Unmanned aerial vehicle. special issue. IEEE Control Systems magazine 32 (5).Unmanned Aerial Vehicles and Control: Lockheed Martin Advanced Technology Laboratories. (2012). IEEE Control Systems, 32(5), 32-34. doi:10.1109/mcs.2012.2205474Gabriel, C., 2008. Modelling, simulation and control of a twin rotor mimo-system. Master thesis, Polytechnic University of Valencia, Spain.García-Sanz, M., & Elso, J. (2007). Ampliación del benchmark de diseño de controladores para el cabeceo de un helicóptero. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(1), 107-110. doi:10.1016/s1697-7912(07)70196-6García-Sanz, M., & Elso, J. (2007). Resultados Del Benchmark de Diseño De Controladores Para el Cabeceo de un Helicóptero. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(4), 117-120. doi:10.1016/s1697-7912(07)70251-0Garcia-Alvarado, M. A., & Ruiz-López, I. I. (2010). A design method for robust and quadratic optimal MIMO linear controllers. Chemical Engineering Science, 65(11), 3431-3438. doi:10.1016/j.ces.2010.02.033Hernández, L. H., Pestana, J., Palomeque, D. C., Campoy, P., & Sanchez-Lopez, J. L. (2013). Identificación y control en cascada mediante inversión de no linealidades del cuatrirrotor para el Concurso de Ingeniería de Control CEA IFAC 2012. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(3), 356-367. doi:10.1016/j.riai.2013.05.008Huba, M. (2013). Performance measures, performance limits and optimal PI control for the IPDT plant. Journal of Process Control, 23(4), 500-515. doi:10.1016/j.jprocont.2013.01.002Ishibuchi, H., Tsukamoto, N., Nojima, Y., 2008. Evolutionary many-objective optimization: A short review. En: Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on.Juang, J.-G., Lin, R.-W., & Liu, W.-K. (2008). Comparison of classical control and intelligent control for a MIMO system. Applied Mathematics and Computation, 205(2), 778-791. doi:10.1016/j.amc.2008.05.061Juang, J.-G., Liu, W.-K., & Lin, R.-W. (2011). A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation. ISA Transactions, 50(4), 609-619. doi:10.1016/j.isatra.2011.06.006Lotov, A., Miettinen, K., 2008. Visualizing the Pareto frontier. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (Eds.), Multiobjective Optimization. Vol. 5252 of Lecture Notes in Computer Science. Springer Berlin /Heidel-berg, pp. 213-243.Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369-395. doi:10.1007/s00158-003-0368-6Rahideh, A., Shaheed, M., april 2009. Robust model predictive control of a twin rotor mimo system. En: Mechatronics, 2009. ICM 2009. IEEE International Conference on. pp. 1-6.Rahideh, A., & Shaheed, M. H. (2012). Constrained output feedback model predictive control for nonlinear systems. Control Engineering Practice, 20(4), 431-443. doi:10.1016/j.conengprac.2011.12.003Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J.M., August 2014a. A stabilizing PID controller sampling procedure for stochastic optimizers. En: Memories of the 19th World Congress IFAC 2014. pp. 8158-8163.Reynoso-Meza, G., Blasco, X., Sanchis, J., & Martínez, M. (2014). Controller tuning using evolutionary multi-objective optimisation: Current trends and applications. Control Engineering Practice, 28, 58-73. doi:10.1016/j.conengprac.2014.03.003Reynoso-Meza, G., Sánchez, H.S., Blasco, X., Vilanova, R., August 2014c. Reliability based multiobjective optimization design procedure for PI controller tuning. En: Memories of the 19th World Congress IFAC 2014. pp. 10263-10268.Ruiz-López, I. I., Rodríguez-Jimenes, G. C., & García-Alvarado, M. A. (2006). Robust MIMO PID controllers tuning based on complex/real ratio of the characteristic matrix eigenvalues. Chemical Engineering Science, 61(13), 4332-4340. doi:10.1016/j.ces.2006.02.015Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. doi:10.1016/s0959-1524(02)00062-8Sánchez, H.S., Vilanova, R., 2013a. Multiobjective tuning of PI controller using the NNC method: Simplified problem definition and guidelines for decision making. En: Proceedings of the 18th. IEEE Conference on emerging technologies & factory automation (ETFA).Sánchez, H.S., Vilanova, R., 2013b. Nash-based criteria for selection of pareto optimal controller. En: Proceedings of the 17th. International Conference on System Theory, Control anmd Computing.Tao, C. W., Taur, J. S., & Chen, Y. C. (2010). Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system. Fuzzy Sets and Systems, 161(15), 2081-2103. doi:10.1016/j.fss.2009.12.007Tao, C.-W., Taur, J.-S., Chang, Y.-H., & Chang, C.-W. (2010). A Novel Fuzzy-Sliding and Fuzzy-Integral-Sliding Controller for the Twin-Rotor Multi-Input–Multi-Output System. IEEE Transactions on Fuzzy Systems, 18(5), 893-905. doi:10.1109/tfuzz.2010.2051447Toha, S., Tokhi, M., nov. 2009. Dynamic nonlinear inverse-model based control of a twin rotor system using adaptive neuro-fuzzy inference system. En: Computer Modeling and Simulation, 2009. EMS ‘09. Third UKSim European Symposium on. pp. 107-111.Vargas, F. J., Salgado, M. E., & Silva, E. I. (2011). Optimal ripple-free deadbeat control using an integral of time squared error (ITSE) index. Automatica, 47(9), 2134-2137. doi:10.1016/j.automatica.2011.06.006Velasco, J., García-Nieto, S., Reynoso-Meza, G., Sanchis, J., 2013. Implementación de un sistema hardware-in-the-loop para la simulación en tiempo real de pilotos automáticos para uavs. En: de Automática, C.E. (Ed.), Memorias de las XXXIV Jornadas de Automática.Vilanova, R., Alfaro, V. M., & Arrieta, O. (2012). Simple robust autotuning rules for 2-DoF PI controllers. ISA Transactions, 51(1), 30-41. doi:10.1016/j.isatra.2011.09.001Wen, P., & Lu, T.-W. (2008). Decoupling control of a twin rotor MIMO system using robust deadbeat control technique. IET Control Theory & Applications, 2(11), 999-1007. doi:10.1049/iet-cta:20070335Witczak, M., Puig, V., de Oca, S., oct. 2010. A fault-tolerant control scheme for non-linear discrete-time systems: Application to the twin-rotor system. En: Control and Fault-Tolerant Systems (SysTol), 2010 Conference on. pp. 861-866