scholarly journals Robust constrained MPC stabilization of a CSTR

2012 ◽  
Vol 5 (2) ◽  
pp. 153-158 ◽  
Author(s):  
Juraj Oravec ◽  
Monika Bakošová
Keyword(s):  
Predictive Control ◽  
Exothermic Reaction ◽  
Robust Stabilization ◽  
Optimization Methods ◽  
Linear Matrix ◽  
Continuous Stirred Tank Reactor ◽  
Control Approach ◽  
Model Based ◽  
Constrained Model ◽  
Input Variables

Abstract The paper addresses a case study of robust stabilization of a continuous stirred tank reactor using robust model-based predictive control with constrained input variables. One exothermic reaction runs in the reaction mixture and the reactor is modelled in the form of an uncertain polytopic system. The control approach is based on solution of a set of linear matrix inequalities. This formulation enables to use convex optimization methods to design a gain matrix of a state feedback controller in each control step. The task of stabilization is solved in assumed control conditions with respect to symmetric constraints on control inputs. The control performance achieved by robust constrained model-based predictive control is studied via simulations. Obtained results confirm that the robust constrained model-based predictive control ensures the stability demands and the quality requirements represented by chosen quadratic cost function.

Robust stabilization of a chemical reactor

2009 ◽  
Vol 63 (5) ◽  
Author(s):  
Monika Bakošová ◽  
Dalibor Puna ◽  
Petr Dostál ◽  
Jana Závacká
Keyword(s):  
Output Feedback ◽  
Controller Design ◽  
Exothermic Reaction ◽  
Robust Stabilization ◽  
Output Feedback Control ◽  
Chemical Reactor ◽  
Linear Matrix ◽  
Static Output Feedback ◽  
Continuous Stirred Tank Reactor ◽  
Static Output

AbstractRobust static output feedback control was applied to a continuous stirred tank reactor with parametric uncertainty and multiple steady states in which exothermic reaction takes place. The problem of robust controller design was converted to a solution of linear matrix inequalities and a computationally simple non-iterative algorithm is presented. The possibility of using robust static output feedback for stabilization of reactors with uncertainty and comparison of robust P and PI controllers with an optimal controller is demonstrated by simulation results.

scholarly journals Integration of Model-Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Haitao Xu ◽  
Jing Chen ◽  
Jie Xu
Keyword(s):  
Predictive Control ◽  
Traffic Congestion ◽  
Road Network ◽  
Signalized Intersection ◽  
Signal Control ◽  
Control Approach ◽  
Model Based ◽  
Real Time Traffic ◽  
Overall Performance ◽  
Improved Model

An improved model-based predictive control approach integrating model-based signal control and queue spillover control is proposed in this paper, which includes three modules: model-based signal control, queue spillover identification, and spillover control to deal with the problem of traffic congestion for urban oversaturated signalized intersection. The main steps are as follows. First of all, according to the real-time traffic flow data, the green time splits for all intersections will be solved online by the model-based signal control controller whose optimization model is based on model-predictive control (MPC) strategy. Second, the queue spillover identification module will be used to detect the potential queue spillover. If potential queue spillover is detected, the spillover control module will be activated to prevent vehicles from the upstream link of the link with possible spillover from entering the downstream link to avoid traffic congestion. The experiment is performed on a simulated road network. The results verify that the proposed scheme can significantly decrease the delay which reflects the overall performance of the studied intersection.

scholarly journals Robust Constrained Model Predictive Control for T-S Fuzzy Uncertain System with Data Loss and Data Quantization

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Hongchun Qu ◽  
Yu Li ◽  
Wei Liu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Infinite Horizon ◽  
Uncertain System ◽  
Linear Matrix ◽  
Data Loss ◽  
Worst Case ◽  
Constrained Model ◽  
Constrained Model Predictive Control

This paper addresses the robust constrained model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy uncertain quantized system with random data loss. To deal with the quantization error and the data loss over the networks, the sector bound approach and the Bernoulli process are introduced, respectively. The fuzzy controller and new conditions for stability, which are written as the form of linear matrix inequality (LMI), are presented based on nonparallel distributed compensation (non-PDC) control law and an extended nonquadratic Lyapunov function, respectively. In addition, slack and collection matrices are provided for reducing the conservativeness. Based on the obtained stability results, a model predictive controller which explicitly considers the input and state constraints is synthesized by minimizing an upper bound of the worst-case infinite horizon quadratic cost function. The developed MPC algorithm can guarantee the recursive feasibility of the optimization problem and the stability of closed-loop system simultaneously. Finally, the simulation example is given to illustrate the effectiveness of the proposed technique.

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