Towards optimal control of air handling units using deep reinforcement learning and recurrent neural network

2020 ◽  
Vol 168 ◽  
pp. 106535 ◽  
Author(s):  
Zhengbo Zou ◽  
Xinran Yu ◽  
Semiha Ergan
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Reinforcement Learning ◽  
Recurrent Neural Network ◽  
Air Handling Units

scholarly journals Reinforcement learning of recurrent neural network for temporal coding

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3379-3386 ◽  
Author(s):  
Daichi Kimura ◽  
Yoshinori Hayakawa
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Recurrent Neural Network ◽  
Temporal Coding

scholarly journals A novel mobile robot navigation method based on deep reinforcement learning

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092167
Author(s):  
Hao Quan ◽  
Yansheng Li ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Mobile Robots ◽  
Recurrent Neural Network ◽  
Three Dimensional ◽  
Mobile Robot Navigation ◽  
Simulation Environment ◽  
Network Modules ◽  
Good Improvement ◽  
Dimensional Simulation

At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

2020 ◽  
Vol 42 (15) ◽  
pp. 2833-2856
Author(s):  
Ahmed Elkenawy ◽  
Ahmad M El-Nagar ◽  
Mohammad El-Bardini ◽  
Nabila M El-Rabaie
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Lyapunov Stability Theory ◽  
Adaptive Dynamic Programming ◽  
External Disturbances ◽  
System States

This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

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