Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.
We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme.
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Dwarka Sector 3, New Delhi 110078, India. Electronic address: akumar.ju09@gmail.com.
Abhishek
A
Kumar
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Dwarka Sector 3, New Delhi 110078, India. Electronic address: rajneesh496@gmail.com.
Rajneesh
R
Sharma
Fuzzy Lyapunov RL; Fuzzy Q learning; Inverted pendulum; Reinforcement learning; Rotational/Translational Proof-Mass Actuator