Real-time Optimal Planning and Model Predictive Control of a Multi-rotor with a Suspended Load

2019 ◽  
Author(s):  
Clark Youngdong Son ◽  
Dohyun Jang ◽  
Hoseong Seo ◽  
Taewan Kim ◽  
HyeonBeom Lee ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Suspended Load ◽  
Optimal Planning ◽  
Time Optimal

Real-Time Optimal Speed Coordination and Scheduling for High-Speed Trains Based on Model Predictive Control

2012 ◽  
Vol 433-440 ◽  
pp. 6043-6048 ◽  
Author(s):  
Yong Hua Zhou ◽  
Yang Peng Wang ◽  
Pin Wu ◽  
Peng Wang
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Mobile Communications ◽  
High Speed ◽  
Real Time Processing ◽  
Optimal Speed ◽  
Simulation Speed ◽  
High Speed Trains ◽  
Time Optimal

In the high-speed train control system, the command information such as allowable running distance, time and speed can be sent by the global system for mobile communications for railways (GSM-R). This paper will propose the framework of real-time train scheduling and control based on model predictive control for the optimal speed set-points of high-speed trains. The rolling optimization process combines the genetic algorithm with the simulation of train operation to evaluate the performance of speed set-points, which can be easily implemented in the parallel computing environment for real-time processing. The conflict resolution at the crossing stations is modeled by and embedded in the combination of various speed set-points which are formed from virtual to simulation speed. The final actual speed of train is engendered based on the movement authority and running time through the system of automatic train protection (ATP). The simulation results demonstrate the favorable performance of proposed method.

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.

Sign in / Sign up

Export Citation Format

Share Document