A Particle Swarm Optimized Fuzzy Neural Network Control for Acrobot

  • Dong-bin Zhao
  • Jian-qiang Yi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


This paper addresses the problem of controlling an acrobot, an under-actuated robotic systems, using fuzzy neural network approach. A five-layer Takagi-Sugeno fuzzy neural network control (TSFNNC) is proposed to swing up the acrobot from the low stable equilibrium to approach and balance around its top unstable equilibrium position. By analyzing the system dynamics, total energy and potential energy of the system are introduced in the second layer, with the system states as the inputs to the first layer. Fuzzy membership functions and rules are depicted in the third and fourth layers respectively. The fifth layer works as the final output. A modified particle swarm optimizer (PSO) is adopted to train the consequents in the fourth layer. Simulation results indicate that the integrated TSFNNC approach can control the acrobot system from upswing to balance process effectively. This approach provides an easy and feasible solution for similar control problems.


Particle Swarm Optimization Particle Swarm Fuzzy Neural Network Energy Error Fourth Layer 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-bin Zhao
    • 1
  • Jian-qiang Yi
    • 1
  1. 1.Laboratory of Complex Systems and Intelligence Science, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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