Advantages of using relevant nearly optimal solutions in multi-objective tuning of robust controllers

[EN] This paper presents a new approach for the multiobjective optimal design of robust controllers in systems with stochastic parametric uncertainty. Traditionally, uncertainty is incorporated into the optimization process. However, this can generate two problems: (1) low performance in the nominal scenario; and (2) high computational cost. For the first point, it is possible to ensure that the controllers produce an acceptable performance for the nominal scenario in exchange for being lightly robust. For the second point, the methodology proposed in this work reduces the computational cost significantly. This approach addresses uncertainty by analyzing the robustness of optimal and nearly optimal controllers in the nominal scenario. The methodology guarantees obtaining controllers that are similar/neighboring to lightly robust controllers. Two examples of controller design are shown: one for a linear model and another for a nonlinear model. Both examples demonstrate the usefulness of the proposed new approach.This work was supported in part by the grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033/ and by ''ERDF A way of making Europe''; by the Universitat Politecnica de Valencia through the grant SP20200109 (PAID-10-20); by grant PRE2019-087579 funded by MCIN/AEI/10.13039/501100011033 and by ''ESF Investing in your future''; and by the Generalitat Valenciana regional government through project CIAICO/2021/064. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Veyna-Robles, U. (2023). Advantages of using relevant nearly optimal solutions in multi-objective tuning of robust controllers. ISA Transactions. 139:143-155. https://doi.org/10.1016/j.isatra.2023.05.00314315513

Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell

[EN] Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process. This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy. To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework.This work was supported in part by grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe"; by grant SP20200109 (PAID-10-20) funded by Universitat Politecnica de Valencia; and by grant PRE2019-087579 funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future"; and by the Generalitat Valenciana regional government through project CIAICO/2021/064. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Veyna-Robles, U.; Blasco, X.; Herrero Durá, JM.; Pajares-Ferrando, A. (2023). Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell. Engineering Applications of Artificial Intelligence. 119:1-18. https://doi.org/10.1016/j.engappai.2022.10575811811

Quadcopters Testing Platform for Educational Environments

[EN] This work focuses on the design and construction of an experimental test bench of three degrees of freedom with application in educational environments. It is constituted by a gyroscopic structure that allows the movements of a quadcopter to analyze the control systems. In this context, the main features of the mechanical and electronic design of this prototype are described. At the same time, the main characteristics with respect to existing platforms are highlighted in aspects such as: system autonomy, cost, safety level, operation ranges, experimental flexibility, among others. The possible controller design approaches for quadcopter stabilization can extend to many basic and advanced techniques. In this work, to show the operation and didactic use of the platform, the development of the controller for tilt angle stabilization under two different approaches are presented. The first approach is through PID control, oriented for undergraduate students with basic level in control theory. The second approach is by means of State Feedback, oriented to students with more advanced level in this field. The result of this work is an open test bench, enabled for the experimentation of control algorithms using Matlab-Simulink.This work was supported partially by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00.Veyna-Robles, U.; Garcia-Nieto, S.; Simarro Fernández, R.; Salcedo-Romero-De-Ávila, J. (2021). Quadcopters Testing Platform for Educational Environments. Sensors. 21(12):1-27. https://doi.org/10.3390/s21124134S127211