Implementation of adaptive model predictive controller and model predictive control for temperature regulation and concentration tracking of CSTR

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.

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Model Predictive Control for Autonomous Driving Vehicles

Electronics ◽

10.3390/electronics10212593 ◽

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.

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Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

Applied Sciences ◽

10.3390/app9071372 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1372 ◽

Cited By ~ 8

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.

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

Sustainability ◽

10.3390/su13105630 ◽

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.

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

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407021992165 ◽

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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Constrained model predictive control of a vehicle suspension using Laguerre polynomials

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219889078 ◽

2019 ◽

Vol 234 (6) ◽

pp. 1253-1268

Author(s):

CU Dogruer

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Time Complexity ◽

Laguerre Polynomials ◽

Active Suspension ◽

Model Predictive Controller ◽

Prediction Horizon ◽

Predictive Controller ◽

Constrained Model ◽

Constrained Model Predictive Control

In this paper, a fast constrained model predictive control algorithm was designed for the active suspension of a half-car model to increase the controller bandwidth so that high frequency displacement disturbance coming from the road can be rejected. To this end, a quasi-LTI model of a semi-active suspension model was controlled by a model predictive controller with orthogonal Laguerre polynomials. With the use of Laguerre polynomials, it has been shown that the optimization parameter set could be made minimal, and thereby it has been shown that on-line optimization takes less time. With numerical simulations, it has been shown that the time complexity of a model predictive control having Laguerre polynomials is linear in the length of prediction horizon, whereas time complexity of a regular model predictive control is quadratic in the length of prediction horizon. Since it has been shown that time complexity of the constrained model predictive controller with orthogonal Laguerre polynomial is reduced, it is possible to extend the prediction horizon to large values. Further, constraints on the input signal and the state vector were also discussed within this context.

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Stochastic Model-Predictive Control with Uncertainty Estimation for Autonomous Driving at Uncontrolled Intersections

Applied Sciences ◽

10.3390/app11209397 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9397

Author(s):

Yonghwan Jeong

Keyword(s):

Model Predictive Control ◽

Stochastic Model ◽

Predictive Control ◽

Uncertainty Estimation ◽

Model Predictive Controller ◽

Prediction Uncertainty ◽

Target State ◽

State Prediction ◽

Predictive Controller ◽

Stochastic Model Predictive Control

This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covariance estimator has been utilized to estimate the prediction uncertainty for each approaching vehicle. The desired driving mode has been determined based on the uncontrolled intersection theory. The estimated prediction uncertainty has been used to define the probability distribution of the stochastic model-predictive controller to cope with time-varying uncertainty characteristics of the perception algorithm. The constrained stochastic model-predictive controller based on safety indexes has determined the desired longitudinal acceleration. The proposed robust intersection-passing algorithm has been evaluated via computer simulation based on Monte Carlo simulation with a sensor model. The simulation results showed that the proposed algorithm guarantees the minimum safety constraints and improves the ride comfort at uncontrolled intersections by estimating the uncertainty of sensors and prediction.

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Aircraft Control System Using Model Predictive Controller

TELKOMNIKA Indonesian Journal of Electrical Engineering ◽

10.11591/tijee.v15i2.1538 ◽

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.

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