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Two-Level Intelligent Control of Multiple Robotic Arms

Mollaie Emamzadeh, Mohammad | 2017

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 50429 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Sadati, Naser
  7. Abstract:
  8. This thesis presents a fuzzy-based interaction prediction approach (F-IPA) for two-level optimal control of large-scale systems. The design procedure uses a decomposition/coordination framework of hierarchical structures. At the first level, the system is decomposed into subsystems for which subproblems are formed. At the second level, a fuzzy coordinator is used to predict the coordination parameters needed to coordinate the solutions of the first level subproblems. The fuzzy coordinator uses a critic vector to evaluate its performance and learn its parameters by minimizing an energy function. The proposed control scheme is implemented on a two-degrees-of-freedom (2DOF) model of robot manipulator with parallel links, considered as a large-scale system. Simulation results are obtained and compared with the gradient-based interaction prediction approach (G-IPA) and a centralized optimization. Moreover, inequality constraints on inputs and states are considered in the proposed two-level optimal control strategy. The gradient-type method of coordination (G-IPA) and fuzzy-based interaction prediction approach (F-IPA) are extended to deal with these constraints. For simulation, implementation of extend approaches on a multiple-arm robotic system has been presented, where each arm is treated as a subsystem. The simulation results are also compared with centralized optimization
  9. Keywords:
  10. Large Scale System ; Intelligent Coordination ; Fuzzy Prediction ; Reinforcement Learning ; Multiple Arm Robot System

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