Genetic design of decentralized controllers for 5dof robotic manipulator

Decentralized PID control is a widespread solution for trajectory tracking control of robotic manipulators in industrial contexts, because it provides a good trade-off between ease of implementation and performance. However, in many cases the final tuning of the independent controllers relies on exhaustive trial-and-error procedures, which are time-consuming, and may lead to globally sub-optimal configurations. In this paper, we describe an automatic and simultaneous tuning procedure for the linear controllers of a 5dof robotic manipulator based on Genetic Algorithms. The preliminary results described in this paper, obtained on a detailed model of an industrial manipulator developed within the SimMechanics Matlab environment, show the effectiveness of the evolutionary design procedure. The decentralized controllers can hold satisfactory performances over a wide range of operating conditions, including unknown load disturbances.

[1]  Guanrong Chen,et al.  A fuzzy adaptive variable structure controller with applications to robot manipulators , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  Wander G. da Silva,et al.  Application of genetic algorithms to the online tuning of electric drive speed controllers , 2000, IEEE Trans. Ind. Electron..

[5]  Jay A. Farrell,et al.  Tracking control of a manipulator under uncertainty by FUZZY P+ID controller , 2001, Fuzzy Sets Syst..

[6]  Jon Rigelsford,et al.  Modelling and Control of Robot Manipulators , 2000 .

[7]  Shaocheng Tong,et al.  Fuzzy adaptive control of multivariable nonlinear systems1 , 2000, Fuzzy Sets Syst..

[8]  David Naso,et al.  On-line genetic optimization of unstructured controllers for electric drives , 2002, Industrial Electronics, 2002. ISIE 2002. Proceedings of the 2002 IEEE International Symposium on.

[9]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[10]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[11]  Kazuo Furuta,et al.  Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution , 2002, IEEE Trans. Fuzzy Syst..

[12]  A. Golea,et al.  Fuzzy adaptive control of multivariable nonlinear systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[13]  Antonio Visioli,et al.  On the trajectory tracking control of industrial SCARA robot manipulators , 2002, IEEE Trans. Ind. Electron..

[14]  Naresh K. Sinha,et al.  Intelligent control of robotic manipulators: experimental study using neural networks , 2000 .