Research on Intelligent Vehicle Trajectory Planning and Control Based on an Improved Terminal Sliding Mode

Abstract

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness.

Recent advances in Rapidly-exploring random tree: A review

“…The Rapidly-exploring Random Tree (RRT) algorithm commonly referred to in the field is actually heuristically biasing RRT (HBRRT) [ 3 ], target-biased RRT (TBRRT) [ [4] , [5] , [6] , [7] ], goal-biased RRT (GBRRT) [ [8] , [9] , [10] , [11] ], goal-oriented RRT (GORRT) [ [12] , [13] , [14] ] or goal-directed RRT algorithm (GDRRT) [ [15] , [16] , [17] , [18] ], the idea of this algorithm is to take the initial position as the root node and then add leaf nodes by random sampling. When the leaf nodes of the random tree arrive at the target position, the path from the initial position to the goal position is planned.…”

An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

“…(2) Path planning strategies based on graph searching algorithms are known as "graph search-based strategies", which are improved over the motion planning approaches of the robots and have strong scenario adaptability and flexibility. The A-Star (A*) [13], rapidly exploring random tree (RRT) [14] are typical graph search-based strategies that use a random searching process to produce flexible, adaptable, and variable planning outcomes. However, it is difficult to guarantee that the suggested courses will be easy to navigate and adhere to the continuity requirements.…”

“…. , p cdk cd (14) where p cdk cd represents the kth node, k cd is the number of nodes included. If the length of P c in the s-axle direction is l P c .…”

Lateral Control Calibration and Testing in a Co-Simulation Framework for Automated Vehicles

“…Because of the complex interconnections between the vehicle's lateral and longitudinal dynamics, designing a controller needs to be considered carefully [3] and it continues to remain a challenge. There are various control techniques that have been used for trajectory tracking in automated vehicles, such as: PID [4,5], linear quadratic regulator (LQR) [6][7][8][9], the sliding mode control (SMC) [10,11], robust control [12], model predictive control (MPC) [8,[13][14][15][16] and reinforcement learning [17,18]. However, most of them are used to control longitudinal and lateral dynamics separately.…”