Abstract
The focus of this work is the continuous control of the Acrobot under limited-torque condition. By utilizing neural network (NN) and genetic algorithm (GA), a global controller is constructed in order to handle both swing-up and balancing control stages of the Acrobot without the need of different control strategies for the two processes. Based on given control timings, two different evaluation functions are introduced, one being continuous evaluation and the other multi-point based evaluation. In order to improve the system performance, an enhanced GA is proposed which recovers the diversity of population when it tends to be lost by applying an adaptive mutation operator based on a convergence index that reflects the diversity of population in GA. To verify the system performance, numerical simulations are implemented with different timing constraints. Comparisons between the proposed GA with the conventional method as well as between the two evaluation schemes are also provided. Simulation results show that the proposed GA has good performance and the neurocontrol system is able to control the Acrobot effectively by either one of the two evaluation schemes.
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Duong, S.C., Kinjo, H., Uezato, E., Yamamoto, T. (2009). On the Continuous Control of the Acrobot via Computational Intelligence. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_24
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DOI: https://doi.org/10.1007/978-3-642-02568-6_24
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