scholarly journals Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 918 ◽  
Author(s):  
Narissara Chatrattanawet ◽  
Soorathep Kheawhom ◽  
Yong-Song Chen ◽  
Amornchai Arpornwichanop
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Solid Oxide ◽  
State Feedback Control ◽  
Model Parameters ◽  
Oxide Fuel ◽  
Worst Case ◽  
Robust Model Predictive Control ◽  
Internal Reforming ◽  
Robust Model

An off-line robust linear model predictive control (MPC) using an ellipsoidal invariant set is synthesized based on an uncertain polytopic approach and then implemented to control the temperature and fuel in a direct internal reforming solid oxide fuel cell (SOFC). The state feedback control is derived by minimizing an upper bound on the worst-case performance cost. The simulation results indicate that the synthesized robust MPC algorithm can control and guarantee the stability of the SOFC; although there are uncertainties in some model parameters, it can keep both the temperature and fuel at their setpoints.

Collision prediction and avoidance for satellite ultra-close relative motion with zonotope-based reachable sets

2018 ◽  
Vol 233 (11) ◽  
pp. 3920-3937
Author(s):  
Zhanpeng Xu ◽  
Xiaoqian Chen ◽  
Yiyong Huang ◽  
Yuzhu Bai ◽  
Qifeng Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Reachable Sets ◽  
Close Relative ◽  
Worst Case ◽  
Reference Orbit ◽  
Robust Model Predictive Control ◽  
Collision Prediction ◽  
Robust Model

Collision prediction and avoidance are critical for satellite proximity operations, and the key is the treatment of satellites' motion uncertainties and shapes, especially for ultra-close autonomous systems. In this paper, the zonotope-based reachable sets are utilized to propagate the uncertainties. For satellites with slender structures (such as solar panels), their shapes are simplified as cuboids which is a special class of zonotopes, instead of the classical sphere approach. The domains in position subspace influenced by the uncertainties and shapes are determined, and the relative distance is estimated to assess the safety of satellites. Moreover, with the approximation of the domains, the worst-case uncertainties for path constraints are determined, and a robust model predictive control method is proposed to deal with the line of sight and obstacle avoidance constraints. With zonotope representations of satellites, the proposed robust model predictive control is capable of handling the shapes of the satellite and obstacle simultaneously. Numerical simulations demonstrate the effectiveness of the proposed methods with an elliptic reference orbit. 1

scholarly journals Robust MPC of an unstable chemical reactor using the nominal system optimization

2014 ◽  
Vol 7 (2) ◽  
pp. 87-93 ◽  
Author(s):  
Monika Bakošová ◽  
Juraj Oravec
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Chemical Reactor ◽  
System Optimization ◽  
Open Loop ◽  
Continuous Stirred Tank Reactor ◽  
Worst Case ◽  
Nominal System ◽  
Robust Model Predictive Control ◽  
Robust Model

Abstract The continuous stirred-tank reactor with uncertain parameters was stabilized in the open-loop unstable steady state using the robust model predictive control. The gain matrices of the robust state-feedback controller were designed using the nominal system optimization and the quadratic parameter-dependent Lyapunov functions. The controller was verified by simulations using the non-linear model of the reactor and compared with the robust model predictive controller designed using the worst-case system optimization. The values of the quadratic cost function and the consumption of coolant were observed. Both robust model predictive controllers stabilized the reactor despite constrained control inputs and states. The robust model predictive control based on the nominal system optimization improved control responses and decreased the consumption of coolant.

Robust model predictive control of an anaesthesia workstation ventilation unit

2020 ◽  
Vol 68 (11) ◽  
pp. 941-952
Author(s):  
Georg Männel ◽  
Marlin Siebert ◽  
Christian Brendle ◽  
Philipp Rostalski
Keyword(s):  
Mechanical Ventilation ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Ventilatory Support ◽  
Physiological Model ◽  
Central Component ◽  
Model Parameters ◽  
Control Approach ◽  
Robust Model Predictive Control ◽  
Robust Model

AbstractRespiratory support is a key element of modern medical care, ranging from oxygen therapy to full ventilatory support. A central component of mechanical ventilation is the control of the resulting pneumatic quantities such as pressure and flow. In this article the use of robust model predictive control for pressure-controlled mechanical ventilation is proposed, with the goal of increasing the safety of the patient by considering physiological safety constraints. The uncertainty in the estimation of physiological model parameters as well as model uncertainties are considered as disturbances to the system, which are taken into account through the proposed robust model predictive control framework. The practical applicability of this control approach is illustrated in an implementation on a research demonstrator of the ventilation unit from an anaesthesia workstation.

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