Experimental study on control performance comparison between model predictive control and proportion-integral-derivative control for radiant ceiling cooling integrated with underfloor ventilation system

2018 ◽  
Vol 143 ◽  
pp. 130-136 ◽  
Author(s):  
Dongliang Zhang ◽  
Xiaoqing Huang ◽  
Dongyan Gao ◽  
Xiaobo Cui ◽  
Ning Cai
Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Weide Guan ◽  
Shoudao Huang ◽  
Derong Luo ◽  
Fei Rong

In recent years, modular multilevel converters (MMCs) have developed rapidly, and are widely used in medium and high voltage applications. Model predictive control (MPC) has attracted wide attention recently, and its advantages include straightforward implementation, fast dynamic response, simple system design, and easy handling of multiple objectives. The main technical challenge of the conventional MPC for MMC is the reduction of computational complexity of the cost function without the reduction of control performance of the system. Some modified MPC scan decrease the computational complexity by evaluating the number of on-state sub-modules (SMs) rather than the number of switching states. However, the computational complexity is still too high for an MMC with a huge number of SMs. A reverse MPC (R-MPC) strategy for MMC was proposed in this paper to further reduce the computational burden by calculating the number of inserted SMs directly, based on the reverse prediction of arm voltages. Thus, the computational burden was independent of the number of SMs in the arm. The control performance of the proposed R-MPC strategy was validated by Matlab/Simulink software and a down-scaled experimental prototype.


Energy ◽  
2019 ◽  
Vol 176 ◽  
pp. 23-33 ◽  
Author(s):  
Dongliang Zhang ◽  
Ning Cai ◽  
Xiaobo Cui ◽  
Xueying Xia ◽  
Jianzhong Shi ◽  
...  

2020 ◽  
Vol 31 (9) ◽  
pp. 1157-1170 ◽  
Author(s):  
Van Ngoc Mai ◽  
Dal-Seong Yoon ◽  
Seung-Bok Choi ◽  
Gi-Woo Kim

This article presents vibration control of a semi-active quarter-car suspension system equipped with a magneto-rheological damper that provides the physical constraint of a damping force. In this study, model predictive control was designed to handle the constraints of control input (i.e. the limited damping force). The explicit solution of model predictive control was computed using multi-parametric programming to reduce the computational time for real-time implementation and then adopted in the semi-active suspension system. The control performance of model predictive control was compared with that of a clipped linear-quadratic optimal controller, where the damping force was bound using a standard saturation function. Two types of road conditions (bump and random excitation) were applied to the suspension system, and the vibration control performance was evaluated through both simulations and experiments.


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