Abstract
When designing controllers for complex systems, it is not only necessary to stabilize the system but to improve the robustness in order to get a better response. Some indexes allow to measure this robustness of the system response, such as the gain and phase margins. In this paper a computational tool that implements a Multi-Objective Genetic Algorithm (MOGA) is designed and applied to optimize the robustness of different controllers. So, it is possible to analyse how the variation of the controller parameters influences the robustness of the system. The tool is applied to the optimization of a Linear Quadratic (LQ) and Eigenvalues assignment (EA) controllers for a MIMO autonomous vehicle, a helicopter, with satisfactory results.
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References
Ribeiro, J.M.S., Santos, M.F., Carmo, M.J., Silva, M.F.: Comparison of PID controller tuning methods: analytical/classical techniques versus optimization algorithms. In: 18th International Carpathian Control Conference (ICCC), pp. 533–538. IEEE (2017)
Santos, M.: An applied approach of intelligent control. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(4), 283–296 (2011)
Zhou, K., Doyle, J.C., Glover, K.: Robust and Optimal Control, vol. 40, p. 146. Prentice Hall, New Jersey (1996)
Jamshidi, M., Krohling, R.A., Coelho, L.D.S., Fleming, P.J.: Robust control systems with Genetic Algorithms, vol. 3. CRC Press, Boca Raton (2002)
Alonso, F., Santos, M.: Heuristic optimization of interplanetary trajectories in aerospace missions. Revista Iberoamericana Automática e Informática Industrial 14(1), 1–15 (2017)
Shim, H., Koo, T.J., Hoffmann, F., Sastry, S.: A comprehensive study of control design for an autonomous helicopter. In: Proceedings of the 37th IEEE Conference on Decision and Control, vol. 4, pp. 3653–3658. IEEE, December 1998
Nair, V.V., Jayasree, P.R., Parvathy, G.: Robust control of helicopter with suspended load. In: International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1–6. IEEE (2017)
Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26(6), 369–395 (2004)
Santos, M., Cruz, J.M.: Algoritmos genéticos. Métodos de Procesamiento Avanzado e Inteligencia Artificial en Sistemas Sensores y Biosensores, Reverté 15, 321–333 (2009)
Antequera, N., Santos, M., de la Cruz, J.M.: A helicopter control based on eigenstructure assignment. In: IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2006, pp. 719–724. IEEE, September 2006
Yañez-Badillo, H., Tapia-Olvera, R., Aguilar-Mejía, O., Beltran-Carbajal, F.: On line adaptive neurocontroller for regulating angular position and trajectory of quadrotor system. RIAI 14(2), 141–151 (2017)
Zimenko, K., Polyakov, A., Efimov, D., Kremlev, A.: Feedback sensitivity functions analysis of finite-time stabilizing control system. Int. J. Robust Nonlinear Control 27(15), 2475–2491 (2017)
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Santos, M., Antequera, N. (2019). Genetic Simulation Tool for the Robustness Optimization of Controllers. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_30
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DOI: https://doi.org/10.1007/978-3-319-94120-2_30
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