Computational feasibility and performance of nonlinear model predictive control schemes

2001 ◽  
Author(s):  
R. Findeisen ◽  
M. Diehl ◽  
Z. Nagy ◽  
F. Allgower ◽  
H.G. Bock ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Control Schemes ◽  
And Performance

scholarly journals Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021 ◽  
Author(s):  
Jingjie Xie ◽  
Xiaowei Zhao ◽  
Hongyang Dong
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Nominal Model ◽  
Performance Requirements ◽  
Control Scheme ◽  
System Uncertainties ◽  
And Performance ◽  
Safety And Stability

AbstractA learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

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