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Real-Time Adaptive Control of a Flexible Manipulator Using Reinforcement Learning

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2 Author(s)
Pradhan, S.K. ; Dept. of Electr. Eng., Nat. Inst. of Technol., Rourkela, India ; Subudhi, B.

This paper exploits reinforcement learning (RL) for developing real-time adaptive control of tip trajectory and deflection of a two-link flexible manipulator handling variable payloads. This proposed adaptive controller consists of a proportional derivative (PD) tracking loop and an actor-critic-based RL loop that adapts the actor and critic weights in response to payload variations while suppressing the tip deflection and tracking the desired trajectory. The actor-critic-based RL loop uses a recursive least square (RLS)-based temporal difference (TD) learning with eligibility trace and an adaptive memory to estimate the critic weights and a gradient-based estimator for estimating actor weights. Tip trajectory tracking and suppression of tip deflection performances of the proposed RL-based adaptive controller (RLAC) are compared with that of a nonlinear regression-based direct adaptive controller (DAC) and a fuzzy learning-based adaptive controller (FLAC). Simulation and experimental results envisage that the RLAC outperforms both the DAC and FLAC.

Published in:

Automation Science and Engineering, IEEE Transactions on  (Volume:9 ,  Issue: 2 )

Date of Publication:

April 2012

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