scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

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.

Event-triggered nonlinear model predictive control for trajectory tracking of unmanned vehicles

2021 ◽  
pp. 095440702199216
Author(s):  
Kai Zou ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Xiaoqiang Sun
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Unmanned Vehicles ◽  
Tracking Accuracy ◽  
Model Predictive Controller ◽  
Time Performance ◽  
Predictive Controller ◽  
Event Triggered

In order to increase the real-time performance of lateral trajectory tracking of unmanned vehicles, this paper designs an event-triggered nonlinear model predictive controller, which can save computation resource to a large extent while the tracking accuracy is still guaranteed. Firstly, a simplified vehicle is established using a two-degree-of-freedom dynamics model. Then, according to the theory of model predictive control, a nonlinear model predictive controller (NMPC) is designed. Since traditional NMPCs often have poor real-time control performance, this paper introduces an event-triggered mechanism, which allows the remaining elements of the control variables in the control horizon to be applied to the system once a specific condition is satisfied. Finally, the proposed controller is established by Matlab/Simulink, and the different trigger conditions are compared and verified in a double lane change maneuvers Then a system for evaluation is designed to quantify the performance of the controller in different trigger conditions. For further verification of the proposed controller, a Hard-in-the-loop simulation system based on Xpack package is established to conduct an HIL experiment. The results show that compared with traditional nonlinear model predictive control, our method offers greatly improved real-time performance while the tracking accuracy is guaranteed.

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