Nonlinear Model Predictive Tracking Control with C/GMRES Method for Heavy-Duty AGVs

2021 ◽  
pp. 1-1
Author(s):  
Xingjie Liu ◽  
Guolei Wang ◽  
Ken Chen
Keyword(s):  
Nonlinear Model ◽  
Tracking Control ◽  
Gmres Method ◽  
Heavy Duty

Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing

2016 ◽  
Vol 28 (5) ◽  
pp. 695-701 ◽  
Author(s):  
Tomohiro Henmi ◽  
Keyword(s):  
Nonlinear Model ◽  
Tracking Control ◽  
Parameter Tuning ◽  
Reference Trajectory ◽  
Control Input ◽  
Control Parameters ◽  
Model Predictive Controller ◽  
Tuning Method ◽  
Predictive Controller ◽  
System Output

[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.

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