Cascaded Model Predictive Controller Performance for the Selection of Robust Working Fluids in Absorption Refrigeration Cycles

2022 ◽  
pp. 118038
Author(s):  
Alexios-Spyridon Kyriakides ◽  
Thomas Prousalis ◽  
Athanasios I. Papadopoulos ◽  
Ibrahim Hassan ◽  
Panos Seferlis
Keyword(s):  
Absorption Refrigeration ◽  
Model Predictive Controller ◽  
Working Fluids ◽  
Controller Performance ◽  
Cascaded Model ◽  
Predictive Controller ◽  
Selection Of

Design and Performance Analysis of a Cascaded Model Predictive Controller and Command Governor for Fuel-Efficient Control of Heavy-Duty Trucks

2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Ben Groelke ◽  
John Borek ◽  
Christian Earnhardt ◽  
Chris Vermillion
Keyword(s):  
Control Strategy ◽  
Heavy Duty ◽  
Model Predictive Controller ◽  
Fuel Savings ◽  
Heavy Duty Truck ◽  
Simulink Model ◽  
Car Following Model ◽  
Cascaded Model ◽  
Predictive Controller ◽  
And Performance

Abstract This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

scholarly journals A Perception-Aware Flatness-Based Model Predictive Controller for Fast Vision-Based Multirotor Flight

2020 ◽  
Vol 53 (2) ◽  
pp. 9412-9419
Author(s):  
Melissa Greeff ◽  
Timothy D. Barfoot ◽  
Angela P. Schoellig
Keyword(s):  
Model Predictive Controller ◽  
Predictive Controller
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