Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control

This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility.

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Research on Model Predictive Control Method for Vehicle Lateral Stability Based on Hardware-in-the-Loop Test

Mathematical Problems in Engineering ◽

10.1155/2020/4712327 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jian Wang ◽

Shifu Liu ◽

Jian Wu ◽

Jun Yang ◽

Aijuan Li

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Model Uncertainty ◽

Control Method ◽

Autonomous Driving ◽

Steering System ◽

Control Model ◽

Hardware In The Loop ◽

Active Steering ◽

Active Steering System

With the rapid development of the vehicle chassis control and autonomous driving technology, it is more and more urgent to realize the active steering technology of autonomous driving stability control. Under emergency conditions, the adhesion constraints, the model uncertainty, and the strong nonlinearity of vehicle bring great challenges to active steering control. In this paper, a model predictive control method for an active steering system based on a nonlinear vehicle model is proposed to solve the problems of adhesion constraint, model uncertainty, and external disturbance in the active steering system. Based on the real-time measurement of vehicle state, a new optimization method is proposed in this paper, which has good performance in dealing with the uncertainty and nonlinearity of the model. The control method transforms the constraint problem into quadratic programming and nonlinear programming. In order to ensure the control accuracy when the vehicle enters the nonlinear area, the control model is built with the combination of the nonlinear tire model and the 2DOF model. The control model is built based on Simulink, and the effectiveness of the controller is the verified joint simulation of Simulink and CarSim. The hardware-in-the-loop (HIL) test bench based on LabVIEW RT is built and tested in order to verify the feasibility and real effect of the controller. Simulation and HIL test results demonstrate that, compared with PID controller, the model predictive controller can accomplish the driving task well and improve the vehicle handling stability.

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Dynamic path planning and path following control for autonomous vehicle based on the piecewise affine tire model

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

10.1177/0954407020941729 ◽

2020 ◽

pp. 095440702094172

Author(s):

Wuwei Chen ◽

Mingyue Yan ◽

Qidong Wang ◽

Kai Xu

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Predictive Control ◽

Path Following ◽

Tire Model ◽

Sideslip Angle ◽

Piecewise Affine ◽

Dynamic Path Planning ◽

Path Following Control ◽

Dynamic Path

This paper proposes a novel dynamic path planning and path following control method for collision avoidance, which works based on an improved piecewise affine tire model. The main contribution of this work is the design of a dynamic path planning method based on model predictive control, where it replans a maneuverable path to avoid moving obstacle in real time. A hierarchical control framework contains a high-level path replanning model predictive control and a low-level path following model predictive control. A collision avoidance cost function in the high hierarchies was designed to calculate the relative dynamic distance, which copes with the sudden obstacle. Moreover, the replanning path is the optimized output according to reference trajectory, obstacle, and handling stability. The control objective of the low hierarchies is to accurately track the replanning path, especially for the increased nonlinearity of large tire sideslip angle. For this reason, an improved piecewise affine tire model is designed and used for model predictive control to improve the path following performance and reduce calculated burden. The main improvement of the piecewise affine tire model is that the varied lateral stiffness coefficients adapt to the change of the tire sideslip angle in different tire regions. Based on the CarSim and Simulink platform, the dynamic path planning and path following simulations are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method.

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Model predictive control for an AUV with dynamic path planning

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) ◽

10.1109/sice.2015.7285374 ◽

2015 ◽

Cited By ~ 5

Author(s):

Chao Shen ◽

Yang Shi ◽

Brad Buckham

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Predictive Control ◽

Dynamic Path Planning ◽

Dynamic Path

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Dynamic Path Planning for Autonomous Driving on Branch Streets With Crossing Pedestrian Avoidance Guidance

IEEE Access ◽

10.1109/access.2019.2938232 ◽

2019 ◽

Vol 7 ◽

pp. 144720-144731

Author(s):

Wenjing Wu ◽

Hongfei Jia ◽

Qingyu Luo ◽

Zhanzhong Wang

Keyword(s):

Path Planning ◽

Autonomous Driving ◽

Dynamic Path Planning ◽

Dynamic Path

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Path Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae1121_04 ◽

2021 ◽

Vol 11 (11) ◽

pp. 31-38

Author(s):

Kiwon Yeom ◽

Keyword(s):

Neural Network ◽

Path Planning ◽

Model Predictive Control ◽

Mobile Robot ◽

Predictive Control ◽

Deep Neural Network ◽

Optimal Solution ◽

Autonomous Driving ◽

Physical Constraints ◽

Predictive Controller

A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.

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