Rule Based Reinforcement Learning Algorithm for Weight Update in FuNe I Adaptive Feedback Controller
نویسندگان
چکیده
FuNe I Adaptive Feedback Controller (FuNe I AFC) has been successfully implemented as a regulator controller. The design of FuNe I AFC is independent of plant dynamics and it is online adaptive. The adaptive feature of this controller is the result of a Weight Matrix updated by rule based reinforcement learning. The Weight Matrix updates the connection weights between rule nodes and the output neuron at each of the simulation step. With the rule based reinforcement learning algorithm, various position response characteristics can be obtained to achieve desired specifications. Authors are currently developing online adaptive Weight Matrix learning algorithms.
منابع مشابه
Reinforcement Learning Based PID Control of Wind Energy Conversion Systems
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملReinforcement learning based feedback control of tumor growth by limiting maximum chemo-drug dose using fuzzy logic
In this paper, a model-free reinforcement learning-based controller is designed to extract a treatment protocol because the design of a model-based controller is complex due to the highly nonlinear dynamics of cancer. The Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. In the Q-learning algorithm, each entry of the Q-table is updated using data...
متن کاملDesign of a Model Reference Adaptive Controller Using Modified MIT Rule for a Second Order System
Sometimes conventional feedback controllers may not perform well online because of the variation in process dynamics due to nonlinear actuators, changes in environmental conditions and variation in the character of the disturbances. To overcome the above problem, this paper deals with the designing of a controller for a second order system with Model Reference Adaptive Control (MRAC) scheme usi...
متن کاملAn Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملMini/Micro-Grid Adaptive Voltage and Frequency Stability Enhancement Using Q-learning Mechanism
This paper develops an adaptive control method for controlling frequency and voltage of an islanded mini/micro grid (M/µG) using reinforcement learning method. Reinforcement learning (RL) is one of the branches of the machine learning, which is the main solution method of Markov decision process (MDPs). Among the several solution methods of RL, the Q-learning method is used for solving RL in th...
متن کامل