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Enhancing Genetic Algorithms using a Dynamic Mutation Value Approach: An Application to the Control of Flexible Robot Systems
Abstract
This paper presents an investigation into a new optimization technique based on genetic algorithm (GA). A dynamically-changed mutation value approach is introduced to increase the diversity in the search space and avoid premature convergence caused by simple genetic algorithm (SGA). The enhanced genetic algorithm (EGA) is used to tune the feedback gains of a PD controller which controls both the position and vibration of a single-link flexible arm. The dynamic model of the system is derived using Hamilton’s principle and modeled using the finite element method (FEM). A multi-objective function is defined and altered to reach a range of specified system responses and therefore it is shown to be able to satisfy different objectives. Adaptive Genetic Algorithm (AGA) and Cloud Model Based Adaptive Genetic Algorithm (CAGA) techniques are used to challenge the proposed technique. Results obtained show that EGA creates significant improvement in the speed of convergence compared to other techniques. Moreover, the obtained solutions are of higher average fitness values. EGA succeeded to consistently reach a global solution for an objective function that needs rigorous search mechanism which encourages for further application to various control problems, complex mathematical functions and real time applications.
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References
S.K. Dwivedy and P. Eberhard, “Dynamic Analysis of Flexible Manipulators: A Literature Review,” Mech. Mach. Theory, vol.41, pp. 749-777, 2006.
M.Benosman and G.Le.Vey, “Control of Flexible Manipulators: A Survey,” Robotica, vol.22, pp. 533-545.
R.H.Cannon Jr., E. Schmitz, “Initial Experiments on the End-Point Control of a Flexible One-Link Robot,” Int. J. Rob. Res., vol.3, no.3, pp. 62-75, 1984.
V. Feliu, K.S.Rattan and H.B.Brown Jr., “Adaptive Control of a Single-Link Flexible Manipulator,” IEEE Control Syst. Magazine, vol.10, no.2, pp. 29-33, 1990.
K.Takahashi and M.Sasaki, “Remarks on Tip Angular Position Control of a Single-Link Flexible Robot Arm Using Modified Lyapunov Function,” in Proc. IEEE Int. Conf. Mechatron. Autom., 2010.
I.A.Mahmood, S.O.R.Moheimani and B.Bhikkaji, “Precise Tip Positioning of a Flexible Manipulator Using Resonant Control,” IEEE/ASME Trans. Mechatron., vol.13, no.2, 2008.
Y.P.Chen and H.T.Hsu, “Regulation and Vibration Control of an FEM-Based Single-Link Flexible Arm Using Sliding-Mode Theory,” J. Vibration Control, vol.7, no.5, pp. 741-752, 2001.
E.Pereira, S.S. Aphale, V.Feliu and S.O.Moheimani, “Integral Resonant Control for Vibration Damping and Precise Tip-Positioning of a Single-Link Flexible Manipulator,” IEEE/ASME Trans. Mechatron., vol.16, no.2, 2011.
M.Baroudi, M.Saad, W.Ghie, A.Kaddouri and H.Ziade, “Vibration Controllability and Observability of a Single-Link Flexible Manipulator,” in Proc. IEEE Conf. Syst. Signals Devices, 2010.
I.H.Akyuz, S.Kizir, Z.Bingul, “Fuzzy Logic Control of Single-Link Flexible Joint Manipulator,” in Proc. IEEE Int. Conf. Ind. Tech., 2011
Z.Su and K.Khorasani, “A Neural-Network-Based Controller for a Single-Link Flexible Manipulator Using the Inverse Dynamics Approach,” IEEE Trans. Ind. Electron., vol.48, no.6, 2001.
T.Mansour, A.Konno and M.Uchiyama, “MPID Control Tuning for a Flexible Manipulator using a Neural Network,” J.Rob. Mechatron., vol.22, no.1, pp.82-90, 2010.
P.Cominos and N.Munro, “PID Controllers: Recent Tuning Methodsand Design to Specification,” in Proc. IEE Control Theory and Applications, vol. 149, no.1, pp. 46-53, 2002.
B.Nagaraj, S.Subha and B.Rampriya, “Tuning Algorithms for PID Controller Using Soft Computing Techniques,” Int. J. Computer Science and Network Security, vol.8, no.4, 2008.
D.Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
S.S.Ge, T.H.Lee and G.Zhu., “Genetic Algorithm Tuning of Lyapunov-Based Controllers: An Application to a Single-Link Flexible Robot System,” IEEE Trans. Ind. Electron., vol.43, 1996.
B.Zain, M.O.Tokhi and S.F.Toha, “PID-Based Control of a Single-Link Flexible Manipulator in Vertical Motion with Genetic Optimisation,” in Proc. IEEE UKSim Computer Modeling and Simulation, 2009.
M.Srinivas and L.M.Patnaik, “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms,” IEEE Trans. on Systems, Man and Cybernetics, Vol.24, No.4, pp. 656-667, April 1994.
L.Lu, W.Cai, Y.C.Soh and L.Xie, “Global Optimization for Overall HVAC systems- Part II,” Energy Conversion and Management, vol.26, pp.1015-1028, 2005.
C.H.Dai, Y.F.Zhu and W.R.Chen, “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms Based on Cloud Model,” in Proc. IEEE Information Theory Workshop, 2006.
J.Yue, T.C.Jie, Z.H.Chun, L.Chuan, C.Yu, WU Jiang and W.D.Lei, “Adaptive Gene Expression Programming Algorithm Based on Cloud Model,” in Proc. IEEE Int. Conf. BioMedical Engineering and Informatics, 2008.
Sarah Deif, Mohammad Tawfik and H. A. Kamal, “Optimal Tuning of a PD Controller for a Single-Link Flexible Robot Arm Using Adaptive Genetic Algorithm,” in Proc. 14th Int. Conf. on Aerospace Sciences and Aviation Technology, Military Technical College, 2011.
M.Tawfik, “Dynamics and Stability of Stepped Gun Barrels with Moving Bullets,” Advances in Acoustics and vibration, 2008
J.H.Holland, Adaptation in natural and articial systems. Ann Arbor, The University of Michigan Press, 1975.
S.N.Sivanadam and S.N.Deepa, Introduction to Genetic Algorithms. Springer, 2008.
K.F.Man, K.S.Tang and S.Kwong, “Genetic Algorithms: Concepts and Applications,” IEEE Trans. Ind. Electron., vol.43, pp. 519-534, 1996.
R.L. Haupt and S.E.Haupt, Practical Genetic Algorithms. Wiley, 2004.
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