Abstract
The evolution of controllers using genetic programming is described for the continuous, limited torque minimum time swing-up and inverted balance problems of the acrobot. The best swing-up controller found is able to swing the acrobot up to a position very close to the inverted ‘handstand’ position in a very short time, which is comparable to the results which have been achieved by other methods using similar parameters for the dynamic system. The balance controller is successful at keeping the acrobot in the unstable, inverted position when starting from the inverted position.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boone, G.: Minimum-time control of the acrobot. In: Robotics and Automation, 1997. Proceedings. 1997 IEEE International Conference on, vol. 4, pp. 3281–3287 (1997)
Coulom, R.: High-accuracy value-function approximation with neural networks. In: EuropeanSymposium on Artificial Neural Networks (2004)
Doucette, J., Heywood, M.I.: Revisiting the acrobot ’height’ task: An example of efficient evolutionary policy search under an episodic goal seeking task. In: Evolutionary Computation(CEC), 2011 IEEE Congress on, pp. 468 –475 (2011)
Dracopoulos, D.C.: Genetic evolution of controllers for challenging control problems. Journalof Computational Methods in Science and Engineering 11(4), 227–242 (2011)
Duong, S., Kinjo, H., Uezato, E., Yamamoto, T.: On the continuous control of the acrobotvia computational intelligence. In: B.C. Chien, T.P. Hong, S.M. Chen, M. Ali (eds.) Next-Generation Applied Intelligence, Lecture Notes in Computer Science, vol. 5579, pp. 231–241.Springer Berlin / Heidelberg (2009)
Franklin, G.F., Powell, J.D., Emami-Naeini, A.: Feedback Control of Dynamic Systems, 4edn. Prentice Hall, New Jersey (2002)
Fukushima, R., Uezato, E.: Swing-up control of a 3-dof acrobot using an evolutionary approach.Artificial Life and Robotics 14, 160–163 (2009)
Jung, T., Polani, D., Stone, P.: Empowerment for continuous agent-environment systems.Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems 19, 16–39 (2011)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA (1992)
Lai, X.Z., She, J.H., Yang, S.X., Wu, M.: Comprehensive unified control strategy for underactuated two-link manipulators. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39(2), 389–398 (2009)
RLC: Reinforcement learning competition. http://www.rl-competition.org (2009)
Spong, M.W.: Swing up control of the acrobot. In: Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on, vol. 3, pp. 2356–2361 (1994)
Spong, M.W.: The swing up control problem for the acrobot. Control Systems, IEEE 15(1)49–55 (1995)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, Massachusetts (1998)
Wiklendt, L., Chalup, S., Middleton, R.: A small spiking neural network with lqr control applied to the acrobot. Neural Computing & Applications 18, 369–375 (2009)
Willson, S., Mullhaupt, P., Bonvin, D.: Quotient method for controlling the acrobot. In: Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on, pp. 1770–1775 (2009)
Xu, X., Hu, D., Lu, X.: Kernel-based least squares policy iteration for reinforcement learning. Neural Networks, IEEE Transactions on 18(4), 973–992 (2007)
Yoshimoto, J., Nishimura, M., Tokita, Y., Ishii, S.: Acrobot control by learning the switching of multiple controllers. Artificial Life and Robotics 9, 67–71 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London
About this paper
Cite this paper
Dracopoulos, D.C., Nichols, B.D. (2012). Swing Up and Balance Control of the Acrobot Solved by Genetic Programming. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_19
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4739-8_19
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4738-1
Online ISBN: 978-1-4471-4739-8
eBook Packages: Computer ScienceComputer Science (R0)