Path Tracking for Autonomous Vehicles Based on Nonlinear Model: Predictive Control Method

2019 ◽  
Author(s):  
Shaosong Li ◽  
Zheng Li ◽  
Bangcheng Zhang ◽  
Shunhang Zheng ◽  
Xiaohui Lu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Path Tracking ◽  
Nonlinear Model Predictive Control

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control

2016 ◽  
Vol 33 (3) ◽  
pp. 405-415 ◽  
Author(s):  
J. Keighobadi ◽  
J. Faraji ◽  
S. Rafatnia
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chaos Control ◽  
Control Method ◽  
Nonlinear Model Predictive Control ◽  
Stable Range ◽  
Operational Conditions ◽  
Simulation Results ◽  
Micro Cantilever

AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

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