Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.
ISA Trans
; 67: 151-159, 2017 Mar.
Article
in En
| MEDLINE
| ID: mdl-28159314
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
ISA Trans
Year:
2017
Document type:
Article