INTEGRATION OF MOTION PLANNING AND MODEL-PREDICTIVE-CONTROL-BASED CONTROL SYSTEM FOR AUTONOMOUS ELECTRIC VEHICLES

This paper introduces the development of an autonomous driving system in autonomous electric vehicles, which consists of a simplified motion-planning program and a Model-Predictive-Control-Based (MPC-based) control system. The motion-planning system is based on polynomial parameterization, which computes a path toward the expected longitudinal and lateral positions within required time interval in real scenarios. Then the MPC-based control system cooperates the front steering and individual wheel torques to track the planned trajectories, while fulfilling the physical constraints of actuators. The proposed system is evaluated through simulation, using a seven-degrees-offreedom vehicle model with a ‘magic formula’ tire model. The simulations and validation through CarSim show that the proposed planner algorithm and controller are feasible and can achieve requirements of autonomous driving in normal scenarios.

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Cascaded Model Predictive Control for Shared Autonomous Electric Vehicles Systems with V2G Capabilities

2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm.2019.8909735 ◽

2019 ◽

Author(s):

Riccardo Iacobucci ◽

Raffaele Bruno

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Electric Vehicles ◽

Cascaded Model ◽

Autonomous Electric Vehicles

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Model Predictive Control of a Shared Autonomous Electric Vehicles System with Charge Scheduling and Electricity Price Response

2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) ◽

10.1109/icite.2018.8492676 ◽

2018 ◽

Author(s):

Riccardo Iacobucci ◽

Benjamin McLellan ◽

Tetsuo Tezuka

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Electric Vehicles ◽

Electricity Price ◽

Price Response ◽

Autonomous Electric Vehicles

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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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