Abstract
An algorithmic parameter tuning methodology for controller design of complex systems is needed. This methodology should offer designers a great degree of flexibility and give insight into the potentials of the controller structure and the consequences of the design decisions that are made. Such a method is proposed here. For an exploratory phase a new pareto-ranked genetic algorithm is proposed to generate an evenly dispersed set of near optimal, global, solutions. By pair-wise preference statements on design alternatives a linear program is set up as a formal means for selecting the solution with best overall designer satisfaction. In a following interactive design phase using nonlinear programming techniques with a priori decisions on allowed quality levels, a best tuning compromise in competing requirements satisfaction is searched for while guaranteeing pareto-optimality. In particular, this two-phase tuning approach allows the designer to balance nominal control performance and multi-model control robustness.
EvoScondi Node, DLR- Institute of Robotics and Mechatronics, Oberpfaffenhofen
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References
Grübel, G.: Perspectives of CACSD: Embedding the Control System Design Process into a Virtual Engineering Environment. Proc. IEEE Int. Symposium on Computer Aided Control System Design, Hapuna-Beach, Hawaii (1999) 297–302
Feng, W., Li, Y.: Performance Indices in Evolutionary CACSD Automation with Application to Batch PID Generation. Proc. IEEE Int. Symposium on Computer Aided Control System Design, Hapuna-Beach, Hawaii (1999) 486–491
Chipperfield, A.J., Dakev, N.V., Fleming, P.J., Whidborne, J.F.: Multiobjective Robust Control Using Evolutionary Algorithms. Proc. IEE Int. Conf. Industrial Technology (1996) 269–273
Joos, H.-D., Schlothane, M., Grübel, G.: Multi-Objective Design of Controllers with Fuzzy Logic. Proc. IEEE/IFAC Joint Symposium on Computer Aided Control System Design, Tucson, AZ (1994) 75–82
Zakian, V., Al-Naib, U.: Design of Dynamical and Control Systems by the Method of Inequalities. Proc. Institute of Electrical Engineers, Vol. 120, No. 11. (1973) 1421–1427
Tan, K.C., Lee, T.H., Khor, E.F.: Control System Design Automation with Robust Tracking Thumbprint Performance Using a Multi-Objective Evolutionary Algorithm. Proc. IEEE Symposium on Computer-Aided Control System Design, Hapuna-Beach, Hawaii (1999) 498–503
Kienitz, K.H.: Controller Design Using Fuzzy Logic — A Case Study. Automatica, Vol. 29, No. 2. (1993) 549–554
Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computing, Vol. 3, No. 1. (1995) 1–16
Pohlheim, H.: Genetic and Evolutionary Algorithm Toolbox for Use with Matlab-Documentation. Technical Report, Technical University Ilmenau, (1996)
d’Ambrosio, J.G., Birmingham, W.P.: Preference-directed Design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 9. (1995) 219–230
Miettinen, K.M.: Nonlinear Multiobjective Optimization, Kluwer Academic Publishers, (1998)
Kreisselmeier, G., Steinhauser, R.: Application of Vector Performance Optimization to Robust Control Loop Design of a Fighter Aircraft. Int. Journal Control, Vol. 37, No. 2. (1983) 251–284.
Finsterwalder, R., Joos, H.-D., Varga, A.: A Graphical User Interface for Flight Control Development. Proc. IEEE Symposium on Computer-Aided Control System Design, Hapuna-Beach, Hawaii (1999) 439–444
Grübel, G., Finsterwalder, R., Gramlich, G., Joos, H.-D., Lewald, S.: ANDECS: A Computation Environment for Control Applications of Optimization. In: Control Applications of Optimization, R. Bulirsch, D. Kraft, eds., Int. Series of Numerical Mathematics, Vol. 115. Birkhäuser Verlag, Basel (1994) 237–254
Grübel, G.: Another View on the Design Challenge Achievements. In: Robust Flight Control — A Design Challenge, Magni, J.F., Bennani, S., Terlouw, J.C., Eds., Lecture Notes in Control and Information Sciences 224, Springer Verlag, Berlin Heidelberg New York (1997) 603–609
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Kolossa, D., Grübel, G. (2000). Evolutionary Computation and Nonlinear Programming in Multi-model-robust Control Design. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_15
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DOI: https://doi.org/10.1007/3-540-45561-2_15
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