Affiliations: Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
Correspondence:
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Corresponding author. Abhishek Kumar, Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Dwarka, New Delhi-78, India. E-mail: akumar.ju09@gmail.com.
Abstract: Markov game based controllers are robust but lack guarantee on the stability of the designed controller. In this work, we attempt to address this shortcoming by proposing a lyapunov fuzzy Markov game controller for safe and stable tracking control of two link robotic manipulators. Lyapunov theory has been used to generate fuzzy linguistic rules for implementing a reinforcement learning (RL) based Markov game controller. We employ fuzzy inference system as a generic function approximator to deal with the “curse of dimensionality” issue. Proposed RL based Markov game controller is self-learning, adaptive and optimal. We implement the proposed control paradigm on: a) Two link robot manipulator and b) SCARA manipulator for the cases: i) controller handles disturbances and parameter variations, and ii) disturbances and no parameter variations. We give comparative evaluation of our approach against: a) fuzzy Q learning controller, and b) fuzzy Markov game controller. Simulation results illustrate stable and superior tracking performance and advantage in terms of lower control torque requirements.
Keywords: Reinforcement learning, fuzzy Q learning, fuzzy Markov game control, lyapunov fuzzy control, two link robotic manipulator, SCARA
