Design of Active Disturbance Rejection Controller for Trajectory-Following of Autonomous Ground Electric Vehicles

Abstract

In this paper, the concept of symmetry is utilized in the promising trajectory-following control design of autonomous ground electric vehicles—that is, the construction and the solution of active disturbance rejection controllers are symmetrical. This paper presents an active disturbance rejection controller (ADRC) for improving the trajectory-following performance of autonomous ground electric vehicles (AGEV) with an advanced active front steering system. Since AGEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environment, the main control objective is to deal with the AGEV trajectory control challenges of system uncertainties, system nonlinearities, and external disturbance. First, the vehicle dynamics trajectory-following model and its state space representation system are established. Then, the augmented control-oriented vehicle-trajectory-following system with dynamic error is developed. The resulting active disturbance rejection controller of the vehicle-trajectory-following system is finally designed using the trajectory performance index and active disturbance compensation, and the stability of the active disturbance rejection controller is also analyzed and derived via Lyapunov stability theory. The effectiveness of the proposed controller is validated through double lane change and serpentine maneuvers under the co-simulation platform of MATLAB/Simulink-Carsim®. Simulation results show that the designed controller provides enhanced vehicle-trajectory-following performance compared to the linear quadratic regulator controller (LQR) and model predictive controller (MPC). It will provide a certain guidance for the controller engineering design of the AGEV trajectory-following system.

Practical Prescribed Tracking Control of n-DOF Robotic Manipulator with Uncertainties via Friction Compensation Approach

“…The linear extended state observer (LESO) [9] can overcome these limitations. When applied to a single input single output (SISO) system, LESO effectively estimates the lumped uncertainty and unmeasured velocity state using a quite simple structure with only one parameter [10,11]. Nevertheless, LESO still has the following two limitations:…”

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

“…Through the hierarchical solution of the original algorithm, the method of expanding the node of the original algorithm and applying the map simplification strategy shortens the solution time, and enhances the obstacle avoidance ability and real-time performance of the unmanned vehicle. To solve the problem of poor dynamic performance of traditional artificial potential field algorithm, Jin [19] et al introduced rotation factor to reduce calculation burden, and adopted gradient descent method to guide vehicle motion. To solve the local optimization problem of traditional particle swarm optimization algorithm, Tao [20] et al…”