scholarly journals A self‐adaptive SAC‐PID control approach based on reinforcement learning for mobile robots

2021 ◽  
Author(s):  
Xinyi Yu ◽  
Yuehai Fan ◽  
Siyu Xu ◽  
Linlin Ou
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Pid Control ◽  
Control Approach ◽  
Self Adaptive

An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots

2020 ◽  
Vol 102 ◽  
pp. 280-294 ◽  
Author(s):  
Ignacio Carlucho ◽  
Mariano De Paula ◽  
Gerardo G. Acosta
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Pid Control ◽  
Learning Approach

scholarly journals Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 487
Author(s):  
Fumitake Fujii ◽  
Akinori Kaneishi ◽  
Takafumi Nii ◽  
Ryu’ichiro Maenishi ◽  
Soma Tanaka
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Pid Control ◽  
Multiple Input Multiple Output ◽  
Industrial Process ◽  
Pi Controller ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Self Tuning ◽  
Two Degree Of Freedom

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller.

The Application of Adaptive Fuzzy PID in the Liquid Level Cascade Control System

2013 ◽  
Vol 846-847 ◽  
pp. 321-324 ◽  
Author(s):  
Le Peng Song ◽  
Hua Bin Wang
Keyword(s):  
Control System ◽  
Pid Control ◽  
Cascade Control ◽  
Liquid Level ◽  
Water Tank ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Adaptive Fuzzy ◽  
Cascade Control System ◽  
Self Adaptive

As liquid level cascade system has the character the issue of non-linearity ,time variability and the overshoot,tradition PID control can not meet the requirement of precise molding system. So devise a self-_ adaptive fuzzy PID control .A self-_ adaptive fuzzy PID control combined PID to control calculate way and faintness to control the advantage of method, this text permits water tank to carry on mathematics model to design the double permit a water tank liquid misty PID string class control system. Matlab/Simulink and fuzzy logic toolbox are simulated to the single loop PID control system,the cascade control system and the cascade control system based on fuzzy self-tuning PID were simulated with Simulink. The analysis and simulation results indicate that the character issue of non-linearity ,time variability and the overshoot of the liquid level cascade control system based on a self-_ adaptive fuzzy PID controller are superior to previous of two methods.

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

2013 ◽  
Vol 14 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Xin Ma ◽  
Ya Xu ◽  
Guo-qiang Sun ◽  
Li-xia Deng ◽  
Yi-bin Li
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Autonomous Mobile Robots

Synchronous Control Characteristics Analysis of Shield Propulsion Hydraulic System Based on Tracking Differentiator and Self-Adaptive Nonlinear PID

2021 ◽  
Author(s):  
Sen Li ◽  
XiaoHua Cao
Keyword(s):  
Pid Control ◽  
Hydraulic System ◽  
Control Method ◽  
Hydraulic Systems ◽  
Fuzzy Pid Control ◽  
Synchronous Control ◽  
Tracking Differentiator ◽  
Characteristics Analysis ◽  
Servo Tracking ◽  
Self Adaptive

Aiming at the low precision problem of multi-cylinder cooperative propulsion control in different regions of shield propulsion hydraulic systems under conditions of large load changes, this paper proposes a tracking differentiator and self-adaptive nonlinear PID (TD-NPID) control method to improve the synchronous control characteristics of shield propulsion hydraulic systems. First, the working principles of shield propulsion hydraulic systems were analyzed, and a mathematical model and TD-NPID controller were developed. Then, a simulation model was developed in AMESim-MATLAB environment, and the synchronous dynamic performances of fuzzy PID control, conventional PID control, and TD-NPID control were compared and analyzed. The results demonstrated that the shield propulsion hydraulic system with TD-NPID control had better servo tracking ability and steady-state performance than the systems with fuzzy or conventional PID control, which verified the feasibility of the application of TD-NPID control for the synchronous control of shield propulsion hydraulic systems.

scholarly journals Reinforcement learning: a control approach for reducing component damage in mobile machines

2020 ◽  
Author(s):  
Lars Brinkschulte ◽  
◽  
Marina Graf ◽  
Marcus Geimer ◽  
◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Approach

scholarly journals Correction to: Quadrotor Modeling and a PID Control Approach

2020 ◽  
pp. C1-C1 ◽  
Author(s):  
César A. Cárdenas R. ◽  
Víctor Hugo Grisales ◽  
Carlos Andrés Collazos Morales ◽  
H. D. Cerón-Muñoz ◽  
Paola Ariza-Colpas ◽  
...  
Keyword(s):  
Pid Control ◽  
Control Approach
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