scholarly journals An Efficient Model Based Control Algorithm for the Determination of an Optimal Control Policy for a Constrained Stochastic Linear System

2018 ◽  
Vol 51 (18) ◽  
pp. 590-595 ◽  
Author(s):  
J. Prakash ◽  
David Zamar ◽  
Bhushan Gopaluni ◽  
Ezra Kwok
Keyword(s):  
Optimal Control ◽  
Linear System ◽  
Control Algorithm ◽  
Control Policy ◽  
Model Based Control ◽  
Model Based ◽  
Stochastic Linear System

scholarly journals The Development and Implementation of Model-Based Control Algorithm of Urea-SCR Dosing System for Improving De-NOx Performance and Reducing NH3-Slip

2011 ◽  
Author(s):  
Soo-Jin Jeong ◽  
Woo-Seung Kim ◽  
Jung-Kwon Park ◽  
Ho-Kil Lee ◽  
Se-Doo Oh
Keyword(s):  
Control Algorithm ◽  
Catalytic Reduction ◽  
Fluid Model ◽  
Nox Reduction ◽  
Reaction Model ◽  
Urea Solution ◽  
Model Based Control ◽  
Model Based ◽  
Nh3 Emission ◽  
Scr System

The selective catalytic reduction (SCR) system is a highly-effective aftertreatment device for NOx reduction of diesel engines. Generally, the ammonia (NH3) was generated from reaction mechanism of SCR in the SCR system using the liquid urea as the reluctant. Therefore, the precise urea dosing control is a very important key for NOx and NH3 slip reduction in the SCR system. This paper investigated NOx and NH3 emission characteristics of urea-SCR dosing system based on model-based control algorithm in order to reduce NOx. In the map-based control algorithm, target amount of urea solution was determined by mass flow rate of exhaust gas obtained from engine rpm, torque and O2 for feed-back control NOx concentration should be measured by NOx sensor. Moreover, this algorithm cannot estimate NH3 absorbed on the catalyst Hence, the urea injection can be too rich or too lean. In this study, the model-based control algorithm was developed and evaluated based on the analytic model for SCR system. The channel thermo-fluid model coupled with finely tuned chemical reaction model was applied to this control algorithm. The vehicle test was carried out by using map-based and model-based control algorithms in the NEDC mode in order to evaluate the performance of the model based control algorithm.

scholarly journals OPTIMAL CONTROL OF STOCHASTIC LINEAR SYSTEM WITH DELAYED RELAY OUTPUT

2002 ◽  
Vol 35 (1) ◽  
pp. 407-411
Author(s):  
P.-A. Bliman ◽  
A.B. Piunovskiy ◽  
M. Sorine
Keyword(s):  
Optimal Control ◽  
Linear System ◽  
Stochastic Linear System

scholarly journals Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles

2021 ◽  
Author(s):  
Xinglong Zhang ◽  
Yaoqian Peng ◽  
Biao Luo ◽  
Wei Pan ◽  
Xin Xu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Control Policy ◽  
Intelligent Vehicles ◽  
Time Varying ◽  
Control Constraints ◽  
Model Based ◽  
Safety Constraints ◽  
And Control ◽  
State And Control Constraints

<div>Recently, barrier function-based safe reinforcement learning (RL) with the actor-critic structure for continuous control tasks has received increasing attention. It is still challenging to learn a near-optimal control policy with safety and convergence guarantees. Also, few works have addressed the safe RL algorithm design under time-varying safety constraints. This paper proposes a model-based safe RL algorithm for optimal control of nonlinear systems with time-varying state and control constraints. In the proposed approach, we construct a novel barrier-based control policy structure that can guarantee control safety. A multi-step policy evaluation mechanism is proposed to predict the policy's safety risk under time-varying safety constraints and guide the policy to update safely. Theoretical results on stability and robustness are proven. Also, the convergence of the actor-critic learning algorithm is analyzed. The performance of the proposed algorithm outperforms several state-of-the-art RL algorithms in the simulated Safety Gym environment. Furthermore, the approach is applied to the integrated path following and collision avoidance problem for two real-world intelligent vehicles. A differential-drive vehicle and an Ackermann-drive one are used to verify the offline deployment performance and the online learning performance, respectively. Our approach shows an impressive sim-to-real transfer capability and a satisfactory online control performance in the experiment.</div>

scholarly journals Visualization of thermal washout due to spatiotemporally heterogenous perfusion in the application of a model-based control algorithm for MR-HIFU mediated hyperthermia

2021 ◽  
Vol 38 (1) ◽  
pp. 1174-1187
Author(s):  
Lukas Christian Sebeke ◽  
Pia Rademann ◽  
Alexandra Claudia Maul ◽  
Sin Yuin Yeo ◽  
Juan Daniel Castillo Gómez ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Model Based Control ◽  
Model Based

scholarly journals Large Aberration Correction by Magnetic Fluid Deformable Mirror with Model-Based Wavefront Sensorless Control Algorithm

2019 ◽  
Vol 20 (15) ◽  
pp. 3697
Author(s):  
Xiang Wei ◽  
Yuanyuan Wang ◽  
Zhan Cao ◽  
Dziki Mbemba ◽  
Azhar Iqbal ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Magnetic Fluid ◽  
Control Algorithm ◽  
Imaging System ◽  
Colloidal Suspension ◽  
Deformable Mirror ◽  
Model Based Control ◽  
Model Based ◽  
Proposed Model ◽  
Ferromagnetic Particles

Magnetic fluid is a stable colloidal suspension of nano-sized, single-domain ferri/ferromagnetic particles dispersed in a liquid carrier. The liquid can be magnetized by the ferromagnetic particles aligned with the external magnetic field, which can be used as a wavefront corrector to correct the large aberrations up to more than 100 µm in adaptive optics (AO) systems. Since the measuring range of the wavefront sensor is normally small, the application of the magnetic fluid deformable mirror (MFDM) is limited with the WFS based AO system. In this paper, based on the MFDM model and the relationship between the second moment (SM) of the aberration gradients and the far-field intensity distribution, a model-based wavefront sensorless (WFSless) control algorithm is proposed for the MFDM. The correction performance of MFDM using the model-based control algorithm is evaluated in a WFSless AO system setup with a prototype MFDM, where a laser beam with unknown aberrations is supposed to produce a focused spot on the CCD. Experimental results show that the MFDM can be used to effectively compensate for unknown aberrations in the imaging system with the proposed model-based control algorithm.

scholarly journals Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles

2021 ◽  
Author(s):  
Xinglong Zhang ◽  
Yaoqian Peng ◽  
Biao Luo ◽  
Wei Pan ◽  
Xin Xu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Control Policy ◽  
Intelligent Vehicles ◽  
Time Varying ◽  
Control Constraints ◽  
Model Based ◽  
Safety Constraints ◽  
And Control ◽  
State And Control Constraints

<div>Recently, barrier function-based safe reinforcement learning (RL) with the actor-critic structure for continuous control tasks has received increasing attention. It is still challenging to learn a near-optimal control policy with safety and convergence guarantees. Also, few works have addressed the safe RL algorithm design under time-varying safety constraints. This paper proposes a model-based safe RL algorithm for optimal control of nonlinear systems with time-varying state and control constraints. In the proposed approach, we construct a novel barrier-based control policy structure that can guarantee control safety. A multi-step policy evaluation mechanism is proposed to predict the policy's safety risk under time-varying safety constraints and guide the policy to update safely. Theoretical results on stability and robustness are proven. Also, the convergence of the actor-critic learning algorithm is analyzed. The performance of the proposed algorithm outperforms several state-of-the-art RL algorithms in the simulated Safety Gym environment. Furthermore, the approach is applied to the integrated path following and collision avoidance problem for two real-world intelligent vehicles. A differential-drive vehicle and an Ackermann-drive one are used to verify the offline deployment performance and the online learning performance, respectively. Our approach shows an impressive sim-to-real transfer capability and a satisfactory online control performance in the experiment.</div>

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