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Bipedal-double-pendulum walking robot control using recurrent hybrid neural network

Bipedal-double-pendulum walking robot control using recurrent hybrid neural network

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This chapter presents neural control scheme ofa planar-like double-pendulum-bipedal robot. For simplicity, only a five-link planar system is considered. The system effectively acts as two dynamically interacting planar robot arms. The scheme employs a single neural controller for the whole biped. Recurrent networks have feedback connections and thus an inherent memory for dynamics which makes them suitable for dynamic system modeling. A feature of the networks adopted is their hybrid hidden layer which includes both linear and nonlinear neurons. The standard proportional derivative (PD) controller is also employed for comparison. The results presented show the superior ability of the proposed neural control scheme at adapting to changes in the dynamics parameters of the bipedal robot.

Chapter Contents:

  • Abstract
  • 11.1 Introduction
  • 11.2 Bipedal-like double-pendulum robot
  • 11.2.1 Walking
  • 11.2.2 Impact
  • 11.3 Proposed neural network
  • 11.4 Control system
  • 11.4.1 Neural network controller
  • 11.5 Results
  • 11.6 Conclusion
  • Appendix A
  • References

Inspec keywords: recurrent neural nets; neurocontrollers; PD control; legged locomotion; robotic assembly; nonlinear control systems; pendulums; manipulator dynamics; gait analysis

Other keywords: PD controller; neural control scheme; planar-like double-pendulum-bipedal robot; nonlinear neurons; dynamic system modeling; feedback connections; dynamics parameters; linear neurons; hybrid hidden layer; dynamically interacting planar robot arms; bipedal-double-pendulum walking robot control; five-link planar system; recurrent hybrid neural network; standard proportional derivative controller

Subjects: Control applications in assembling; Nonlinear control systems; Neurocontrol; Robotics; Mobile robots; Manipulators; Assembling

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