A Particle Swarm Optimized Fuzzy Neural Network Control for Acrobot
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.
KeywordsParticle Swarm Optimization Particle Swarm Fuzzy Neural Network Energy Error Fourth Layer
Unable to display preview. Download preview PDF.
- 2.Smith, M.H., Zhang, T., Gruver, W.A.: Dynamic Fuzzy Control and System Stability for the Acrobot. In: IEEE International Conference on Fuzzy Systems, pp. 286–291 (1998)Google Scholar
- 4.Xu, X., He, H.G.: Residual-gradient-based Neural Reinforcement Learning for the Optimal Control of an Acrobot. In: IEEE International Symposium on Intelligent Control, pp. 758–763 (2002)Google Scholar
- 5.Zhao, D.B., Yi, J.Q.: GA-based Control to Swing up an Acrobot with Limited Torque. In: Proceedings of International Conference on Intelligent Computing, pp. 3358–3367 (2005)Google Scholar
- 8.Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. IEEE World Congress on Computational Intelligence, 69–73 (1998)Google Scholar