Robust optimal predictive control for real-time bus regulation strategy with passenger demand uncertainties in urban rapid transit

2021 ◽  
Vol 127 ◽  
pp. 103086
Author(s):  
Qianqian Ma ◽  
Shukai Li ◽  
Huiming Zhang ◽  
Yin Yuan ◽  
Lixing Yang
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Rapid Transit ◽  
Passenger Demand ◽  
Optimal Predictive Control

Real-time thermal management for two-level active rectifier with finite control set model predictive control

2021 ◽  
Vol 131 ◽  
pp. 107057
Author(s):  
Gokhan Ozkan ◽  
Phuong H. Hoang ◽  
Payam Ramezani Badr ◽  
Chris S. Edrington ◽  
Behnaz Papari
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Thermal Management ◽  
Predictive Control ◽  
Active Rectifier ◽  
Finite Control ◽  
Control Set

scholarly journals Robust and optimal predictive control of the COVID-19 outbreak

2020 ◽  
Author(s):  
Johannes Köhler ◽  
Lukas Schwenkel ◽  
Anne Koch ◽  
Julian Berberich ◽  
Patricia Pauli ◽  
...  
Keyword(s):  
Predictive Control ◽  
Optimal Predictive Control

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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