Automatic Model Predictive Control code-generation for real-time implementation in a high-performance microcontroller

2019 ◽  
pp. 25-40
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Code Generation ◽  
High Performance ◽  
Control Code

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.

scholarly journals Model predictive control for autonomous ground vehicles: a review

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
High Performance ◽  
Control Method ◽  
Theoretical Background ◽  
Future Research ◽  
Centralized Control ◽  
Ground Vehicles ◽  
Uncertain Environments ◽  
Autonomous Ground Vehicles

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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