scholarly journals Aircraft Control System Using Model Predictive Controller

2015 ◽  
Vol 15 (2) ◽  
pp. 259
Author(s):  
Labane Chrif ◽  
Zemalache Meguenni Kadda
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Receding Horizon ◽  
Model Predictive Controller ◽  
Control Approach ◽  
Dynamic Matrix ◽  
Basic Model ◽  
Terrain Following ◽  
Predictive Controller ◽  
Linear State

This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industrial model predictive control technology has been presented first followed by a some concepts like the receding horizon, moves etc. which form the basis of the MPC. It follows the Optimization problem which ultimately leads to the description of the Dynamic Matrix Control (DMC).The MPC presented in this report is based on DMC. After this the application summary and the limitations of the existing technology has been discussed and the next generation MPC, with an emphasis on potential business and research opportunities has been reviewed. Finally in the last part we generate Matlab code to implement basic model predictive controller and introduce noise into the model.

scholarly journals Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Mapopa Chipofya ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
State Space ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Linear Quadratic Regulator ◽  
Laguerre Functions ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Linear State ◽  
Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

scholarly journals Model Predictive Control of NCS with Data Quantization and Bounded Arbitrary Time Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jimin Yu ◽  
Yanan Xie ◽  
Xiaoming Tang
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Infinite Horizon ◽  
Stability Result ◽  
Loop Model ◽  
Unified Framework ◽  
Model Predictive Controller ◽  
Predictive Controller

The model predictive control for constrained discrete time linear system under network environment is considered. The bounded time delay and data quantization are assumed to coexist in the data transmission link from the sensor to the controller. A novel NCS model is specially established for the model predictive control method, which casts the time delay and data quantization into a unified framework. A stability result of the obtained closed-loop model is presented by applying the Lyapunov method, which plays a key role in synthesizing the model predictive controller. The model predictive controller, which parameterizes the infinite horizon control moves into a single state feedback law, is provided which explicitly considers the satisfaction of input and state constraints. Two numerical examples are given to illustrate the effectiveness of the derived method.

scholarly journals Robust Output Model Predictive Control of an Unstable Rijke Tube

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Fabian Jarmolowitz ◽  
Christopher Groß-Weege ◽  
Thomas Lammersen ◽  
Dirk Abel
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
State Feedback ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Rijke Tube ◽  
Guaranteed Stability ◽  
Predictive Controller ◽  
Linear State ◽  
Gain Scheduled

This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC). As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.

Reactive planar non-prehensile manipulation with hybrid model predictive control

2020 ◽  
Vol 39 (7) ◽  
pp. 755-773
Author(s):  
Francois R Hogan ◽  
Alberto Rodriguez
Keyword(s):  
Integer Programming ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Controller Design ◽  
Hybrid Control ◽  
Machine Learning Techniques ◽  
Control Approach ◽  
Dynamic Constraints ◽  
Hybrid Dynamics ◽  
Predictive Controller

This article presents an offline solution and online approximation to the hybrid control problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are key characteristics of this task that complicate the design of feedback controllers. We show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of contact. We introduce the Model Predictive Controller with Learned Mode Scheduling (MPC-LMS), which leverages integer programming and machine learning techniques to effectively deal with the combinatorial complexity associated with determining sequences of contact modes. We validate the controller design through a numerical simulation study and with experiments on a planar manipulation setup using an industrial ABB IRB 120 robotic arm. Results show that the proposed algorithm achieves closed-loop tracking of a nominal trajectory by reasoning in real-time across multiple contact modalities.

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 Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

2021 ◽  
Vol 13 (10) ◽  
pp. 5630
Author(s):  
Sadiqa Jafari ◽  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Urban Traffic ◽  
Capital City ◽  
Traffic Network ◽  
Model Predictive Controller ◽  
Traffic Volumes ◽  
Predictive Controller ◽  
Control Traffic

The use of a Model Predictive Controller (MPC) in an urban traffic network allows for controlling the infrastructure of a traffic network and errors in its operations. In this research, a novel, stable predictive controller for urban traffic is proposed and state-space dynamics are used to estimate the number of vehicles at an isolated intersection and the length of its queue. This is a novel control strategy based on the type of traffic light and on the duration of the green-light phase and aims to achieve an optimal balance at intersections. This balance should be adaptable to the unchanging behavior of time and to the randomness of traffic situations. The proposed method reduces traffic volumes and the number of crashes involving cars by controlling traffic on an urban road using model predictive control. A single intersection in Tehran, the capital city of Iran, was considered in our study to control traffic signal timing, and model predictive control was used to reduce traffic. A model of traffic systems was extracted at the intersection, and the state-space parameters of the intersection were designed using the model predictive controller to control traffic signals based on the length of the vehicle queue and on the number of inbound and outbound vehicles, which were used as inputs. This process demonstrates that this method is able to reduce traffic volumes at each leg of an intersection and to optimize flow in a road network compared to the fixed-time method.

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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