Intelligent Control Strategies for the Acrobot Using Neurocontroller Optimized by Genetic Algorithm
Sam Chau Duong, Eiho Uezato, Hiroshi Kinjo, Tetsuhiko Yamamoto · SICE Journal of Control Measurement and System Integration · 2009
This paper focuses on two control methods which are constructed based on neurocontroller (NC) and genetic algorithm (GA) for the Acrobot. A switching controller is first introduced where an NC optimized by GA is used for the swing-up stage and a linear quadratic regulator (LQR) is applied for stabilization. Next, we show that it is possible to handle both control stages of the Acrobot by using only the NC, called global NC, with a different evaluation function for GA. This controller seems to be the first smooth control strategy for the Acrobot. In order to analyze the characteristics and verify the effectiveness of the proposed control methods, numerical simulations are implemented using different timing constraints. A comparison with a classical controller is also provided. Simulation results show that the proposed controllers work effectively.