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

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.

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

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2447
Author(s):  
Yi Chung ◽  
Yee-Pien Yang
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Driving ◽  
Steering System ◽  
Graphic User Interface ◽  
Test Field ◽  
Hardware In The Loop ◽  
Dynamic Path Planning ◽  
Dynamic Path

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.

Nonlinear output path following control using a two-loop robust model predictive control approach

2021 ◽  
pp. 014233122097142
Author(s):  
Mohammad Ghassem Farajzadeh-Devin ◽  
Seyed Kamal Hosseini Sani
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Robot Manipulator ◽  
Path Following ◽  
Uncertain System ◽  
Loop Model ◽  
Control Approach ◽  
Output Tracking ◽  
Path Following Control ◽  
Nonlinear Uncertain System

In this paper, output tracking of a geometric path for a nonlinear uncertain system with input and state constraints is considered. We propose an enhanced two-loop model predictive control approach for output tracking of a nonlinear uncertain system. Additionally, we propose an optimal version of output path following control problem to improve the controller synthesis. Satisfaction of the dynamical constraints of a system such as velocity, acceleration and jerk limitations is added to the problem introducing a new augmented system. The recursive feasibility of the proposed method is demonstrated, and its robust stability is guaranteed such that relaxation on the terminal constraint and penalty are achieved. To validate the theoretical benefits of the proposed controller, it is simulated on a SCARA robot manipulator and the results are compared with a two-loop model predictive controller successfully.

Discrete and Analytic Model Predictive Control for Path Following of Underactuated Ships

2009 ◽  
Author(s):  
Xiaofei Wang ◽  
Zaojian Zou ◽  
Tieshan Li ◽  
Weilin Luo
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Feedback Linearization ◽  
Control Method ◽  
State Space Model ◽  
Path Following ◽  
Analytic Model ◽  
Space Model ◽  
Path Following Control

The control problem of underactuated surface ships and underwater vehicles has attracted more and more attentions during the last years. Path following control aims at forcing the vehicles to converge and follow a desired path. Path following control of underactuated surface ships or underwater vehicles is an important issue to study nonlinear systems control, and it is also important in the practical implementation such as the guidance and control of marine vehicles. This paper proposes two nonlinear model predictive control algorithms to force an underactuated ship to follow a predefined path. One algorithm is based on state space model, the other is based on analytic model predictive control. In the first algorithm, the state space GPC (Generalized Predictive Control) method is used to design the path-following controller of underactuated ships. The nonlinear path following system of underactuated ships is discretized and re-arranged into state space model. Then states are augmented to get the new state space model with control increment as input. Thus the problem is becoming a typical state space GPC problem. Some characters of GPC such as cost function, receding optimization, prediction horizon and control horizon occur in the design procedure of path-following controller. The control law is derived in the form of control increment. In the second algorithm, an analytic model predictive control algorithm is used to study the path following problem of underactuated ships. In this path-following algorithm, the output-redefinition combined heading angle and cross-track error is introduced. As a result, the original single-input multiple-output (SIMO) system is transformed into an equivalent single-input single-output (SISO) system. For the transformed system, we use the analytic model predictive control method to get path-following control law in the analytical form. The analytic model predictive controller can be regarded as special feedback linearization method optimized by predictive control method. It provides a systematic method to compute control parameters rather than by try-and-error method which is often used in the exact feedback linearization control. Relative to GPC, the analytic model predictive control method provides an analytic optimal solution and decreases the computational burden, and the stability of closed-loop system is guaranteed. The path-following system of underactuated ships is guaranteed to follow and stabilize onto the desired path. Numerical simulations demonstrate the validity of the proposed control laws.

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