scholarly journals Model Predictive Control for Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2593
Author(s):  
Trieu Minh Vu ◽  
Reza Moezzi ◽  
Jindrich Cyrus ◽  
Jaroslav Hlava
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control ◽  
System Stability ◽  
Vehicle Speed ◽  
Model Predictive Controller ◽  
Predictive Controller

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

scholarly journals Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

2019 ◽  
Vol 9 (7) ◽  
pp. 1372 ◽  
Author(s):  
Guoxing Bai ◽  
Li Liu ◽  
Yu Meng ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Longitudinal Velocity ◽  
Path Tracking ◽  
Nonlinear Model Predictive Control ◽  
Maximum Displacement ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Mining Vehicles

Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

scholarly journals Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion

2019 ◽  
Vol 52 (8) ◽  
pp. 182-187 ◽  
Author(s):  
M. Rick ◽  
J. Clemens ◽  
L. Sommer ◽  
A. Folkers ◽  
K. Schill ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Sensor Fusion ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control

Nonlinear Parameters and State Estimation for Adaptive Nonlinear Model Predictive Control Design

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Hichem Salhi ◽  
Faouzi Bouani
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Nonlinear Model ◽  
State Space Model ◽  
Nonlinear Model Predictive Control ◽  
Control Law ◽  
Unknown Parameters ◽  
Prediction Horizon ◽  
Predictive Controller

This paper deals with an adaptive nonlinear model predictive control (NMPC) based estimator in cases of mismatch modeling, presence of perturbations and/or parameter variations. Thus, we propose an adaptive nonlinear predictive controller based on the second-order divided difference filter (DDF) for multivariable systems. The controller uses a nonlinear state-space model for parameters and state estimation and for the control law synthesis. Two nonlinear optimization layers are included in the proposed algorithm. The first optimization problem is based on the output error (OE) model with a tuning factor, and it is dedicated to minimize the error between the model and the system at each sample time by estimating unknown parameters when assuming that all system states are available. The second optimization layer is used by the centralized nonlinear predictive controller to generate the control law which minimizes the error between future setpoints and future outputs along the prediction horizon. The proposed algorithm leads to a good tracking performance with an offset-free output and an effectiveness in perturbation attenuation. Practical results on a real setup show the reliability of the proposed approach.

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