The application of continuous action reinforcement learning automata to adaptive PID tuning

2000 ◽  
Author(s):  
M.N. Howell
Keyword(s):  
Reinforcement Learning ◽  
Learning Automata ◽  
Continuous Action ◽  
Pid Tuning ◽  
Reinforcement Learning Automata

scholarly journals Multivariable System Identification Method Based on Continuous Action Reinforcement Learning Automata

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 546 ◽  
Author(s):  
Meiying Jiang ◽  
Qibing Jin
Keyword(s):  
Reinforcement Learning ◽  
Mimo Systems ◽  
Attenuation Factor ◽  
Learning Automata ◽  
Learning Ability ◽  
Current Frequency ◽  
Continuous Action ◽  
Domain Identification ◽  
Identification Method ◽  
Reinforcement Learning Automata

In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation factor. With this method, after continuously interacting with the environment, the optimal attenuation factor can be identified by the continuous action reinforcement learning automata (CARLA), and then the corresponding parameters could be estimated in the end. Moreover, the proposed method could be applied to time-varying systems online due to its online learning ability. The simulation results suggest that the presented approach can meet the requirement of identification accuracy in both square and non-square systems.

APPLICATION OF NOVEL REINFORCEMENT LEARNING AUTOMATA APPROACH IN POWER SYSTEM REGULATION

2009 ◽  
Vol 18 (08) ◽  
pp. 1609-1625 ◽  
Author(s):  
MOHAMMAD KASHKI ◽  
YOUSSEF L. ABDEL-MAGID ◽  
MOHAMMAD A. ABIDO
Keyword(s):  
Reinforcement Learning ◽  
Pid Controller ◽  
Controller Design ◽  
Learning Automata ◽  
Synchronous Generator ◽  
Optimization Method ◽  
Avr System ◽  
Pid Controller Design ◽  
Analytical System ◽  
Reinforcement Learning Automata

In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CDCARLA technique and compares its performance with Particle Swarm Optimization (PSO) as an efficient evolutionary optimization method. The proposed method has been applied to PID controller design. The simulation results show the superior efficiency and robustness of the proposed method.

scholarly journals A Decentralized Economic Load-Balancing Framework among Federated Cloudlets

2021 ◽  
Author(s):  
Sourav Mondal ◽  
Goutam Das
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Automata ◽  
Research Problem ◽  
Low Latency ◽  
Economic Interaction ◽  
Control Message ◽  
Reinforcement Learning Automata

Edge computing servers like cloudlets from different service providers compensate scarce computational and storage resources of mobile devices, are distributed across access networks. However, the dynamically varying computational requirements of associated mobile devices make cloudlets either overloaded or under-loaded. Hence, load balancing among neighboring cloudlets appears to be an essential research problem. Especially, the load balancing problem among federated cloudlets from the same as well as different service providers for low-latency applications needs significant attention. Thus, in this paper, we propose a decentralized load balancing framework among federated cloudlets for low-latency applications that focuses on latency bound rather than latency minimization. In this framework, we employ dynamic processor slicing for handling heterogeneous classes of job requests. We propose a continuous-action reinforcement learning automata-based algorithm that enables cloudlets to independently compute the load balancing strategies in a completely distributed network setting without any exhaustive control message exchange. To capture the economic interaction among federated cloudlets, we model this load balancing problem as an economic and non-cooperative game and by scaffolding the properties of the game formulation, we achieve faster convergence of the reinforcement learning automata. Furthermore, through extensive simulations, we study the impacts of exploration and exploitation on learning accuracy.

Sign in / Sign up

Export Citation Format

Share Document