Optimal Model Predictive Control for Path Tracking of Autonomous Vehicle

Li, Lei; Jia, Zhurong; Cheng, Ting; Jia, Xinchun

doi:10.1109/icmtma.2011.481

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…YOLO receives input photos that are mapped into SxS-sized grid cells. The bounding box must be predicted for each grid cell with a confidence score and class probability [20]. Five parameters x, y, w, h, confidence, and class probability-as well as Class probability prediction are included for each predicted box.…”

Section: Figure 6 Cross Probability Mapmentioning

confidence: 99%

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sudianto

Muslim

Rusli

2023

KINETIK

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Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot’s movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of the wheel while the Artificial Neural Network (ANN) approach to use in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as a control for high-level part of the robot. The results of this study can prove that CNN can help detect existing objects with a value of 45% for detecting some objects. The obtained result from the MPC method, which has been combined with an ANN as an obstacle detector, is that the smaller the horizon value, the shorter the time needed to reach the desired coordinates with the result being 45 seconds.

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Section: Figure 6 Cross Probability Mapmentioning

confidence: 99%

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sudianto

Muslim

Rusli

2023

KINETIK

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“…The dimension value of solution space is the same as the value of N p . At sampling instant k, the position and velocity of particle i can be expressed as equation (8), where i=1,2,3,ÁÁÁ,N. DU i (k) is the control sequence represented by particle i. v i (k) is the velocity vector of particle i.…”

Section: Pso-faiw Algorithmmentioning

confidence: 99%

“…7 An MPC controller used for tracking was established by searching the optimal linear model parameters dynamically to deal with model mismatch issues, which acquired better tracking performance than the traditional approximate linear model predictive controller. 8 One tracking controller combining MPC algorithm and fuzzy PID algorithm was proposed, which considered both vehicle dynamics' constraints and vehicle stability evaluation indexes. 9 Great efforts have been paid into improving the real-time performance of tracking algorithms based on MPC.…”

Section: Introductionmentioning

confidence: 99%

Design of autonomous vehicle trajectory tracking controller based on Neural Network Predictive Control

Geng

Duan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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When the linear Model Predictive Control (MPC) algorithm is used for trajectory tracking control of autonomous vehicles in high-speed turning conditions, the tracking accuracy is usually decreased due to the low fidelity of the predictive model. To deal with this problem, a trajectory tracking controller based on Neural Network Predictive Control (NNPC) is designed in this paper. The data collected from driving simulation experiments is used for the Back Propagation Neural Network (BPNN) training process to obtain the vehicle state predictive models. To reduce the iteration times of the rolling optimization module of NNPC, Particle Swarm Optimization (PSO) with Fitness Allocating Inertia Weights (PSO-FAIW) algorithm is proposed, which can allocate inertia weights in light of the distance between each particle’s fitness and the best fitness of the swarm. Finally, the performance of the controller designed in this paper is verified based on MATLAB /Simulink. The simulation results demonstrate that the controller proposed in this paper can give consideration to tracking accuracy and real-time performance.

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“…But this method is sensitive to noise. The methods based on the template need to build templates according to a prior knowledge of existing objects [19] [20]. Such kind of methods can track targets in complex environments.…”

Section: B Trackingmentioning

confidence: 99%

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

Tang²,

Yang

et al. 2014

AMM

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This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

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Optimal Model Predictive Control for Path Tracking of Autonomous Vehicle

Cited by 15 publications

References 10 publications

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Design of autonomous vehicle trajectory tracking controller based on Neural Network Predictive Control

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

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