Reinforcement Learning Based on State Space Model using Growing Neural Gas for a Mobile Robot

2018 ◽  
Author(s):  
Tomoyuki Arai ◽  
Yuichiro Toda ◽  
Iwasa Mutsumi ◽  
Shuai Shao ◽  
Ryuta Tonomura ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
State Space ◽  
State Space Model ◽  
Growing Neural Gas ◽  
Space Model ◽  
Neural Gas

Stabilization of a Four-Wheel Mobile Robot From Kinematic Model to Dynamic Model by Feedback Passivation of Cascades Using Chained Form

2009 ◽  
Author(s):  
Hossein Mohammadi ◽  
Arash Haghpanah ◽  
Mohammad Eghtesad
Keyword(s):  
Mobile Robot ◽  
System Dynamics ◽  
Mobile Robots ◽  
State Space ◽  
Dynamic Model ◽  
Kinematic Model ◽  
State Space Model ◽  
Space Model ◽  
Novel Approach

In this paper, a novel approach for dynamics based stabilization of a four-wheel mobile robot is presented. One of the well-known and well-established approaches for stabilization of mobile robots is converting the kinematic model of the robot to a chained form. In order to extend this method to dynamic based stabilization, kinematic and dynamic subsystems of the mobile robot state-space model can be considered as two subsystems of a cascade and then feedback passivation of cascades can be utilized for stabilization of the whole system dynamics.

Mobile Robot Navigation Using ARTQL Algorithm with Novelty Driven Mechanism

2013 ◽  
Vol 380-384 ◽  
pp. 1117-1120
Author(s):  
Qi Xiang Hu ◽  
Xin Yu Qu
Keyword(s):  
Mobile Robot ◽  
State Space ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
State Space Model ◽  
Mobile Robot Navigation ◽  
Learning Activity ◽  
Q Learning ◽  
Space Model ◽  
Continuous State

Q-learning algorithm is usually used for traditional mobile robot navigation. The traditional Q-learning methods have the problem of dimension disaster, which may be produced by applying Q-learning to intelligent system of continuous state-space. Besides, the learning activity and efficiency are low. In order to solve these problems, a new method called ARTQL is proposed, which combined ART2 network with the traditional Q-learning algorithm. Then, a learning mechanism called novelty driven is proposed to lead the ARTQL algorithm to learn more actively and efficiently. Through the ARTQL with novelty driven mechanism algorithm, Q-learning Agent in view of the duty which needs to complete to learn an appropriate incremental clustering of state-space model, so Agent can carry out decision-making and a two-tiers online learning of state-space model cluster in unknown environment, without any priori knowledge, through interaction with the environment unceasingly alternately to improve the control strategies, increase the learning accuracy, activity and efficiency. Finally through the mobile robot navigation simulation experiments, we show that, using the proposed algorithm, mobile robot can improve its navigation performance continuously by interactive learning with the environment with high autonomous.

scholarly journals The agent, state-space model of the mobile robot

2018 ◽  
Vol 22 (3) ◽  
pp. 41-50 ◽  
Author(s):  
Krzysztof Oprzędkiewicz ◽  
Maciej Ciurej ◽  
Maciej Garbacz
Keyword(s):  
Mobile Robot ◽  
State Space ◽  
State Space Model ◽  
Space Model

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

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