A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Abstract

<jats:p>This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.</jats:p>

Preview Control with Dynamic Constraints for Autonomous Vehicles

“…The aforementioned methods of this category can usually achieve favorable tracking behaviors in normal cases, but they may fail to acquire satisfactory results under highly dynamic conditions mainly due to the absence of future road information and vehicle dynamic constraints. (3) The model predictive control (MPC) scheme employs a vehicle dynamic model to forecast the future evolution of the system and generate online open-loop optimal control input in a receding horizon under the consideration of constraints [ 15 , 16 ]. Due to the requirement of optimization at each sampling time, numerical computation is inevitable, which may trigger heavy burden on a vehicular computer.…”

“…An improved LTV-MPC method involving estimations of steering angle and state variables within the prediction horizon was further put forward in [ 20 ]. More recently, He et al [ 16 ] designed a two-layer controller for path tracking, in which the upper layer was an LTV-MPC with parameters optimized via particle swarm optimization, and the lower layer consisted of a self-adaptive PID controller. Simulation results showed the robustness of this hierarchical controller under varying velocities and road adhesion.…”

Research on autonomous vehicle path tracking control considering roll stability

“…During recent years, more and more self-driving path tracking problem was solved by MPC. [14][15][16] The principle of MPC applied to vehicle path tracking is to track the target path by establishing a vehicle dynamic model and considering various vehicle dynamic parameters, with the front wheel angle or vehicle speed as control variable. Control variables are rolling optimized and feedback correction continuously.…”

A Sensor Fusion Based Nonholonomic Wheeled Mobile Robot for Tracking Control

“…In [ 19 ], Rayguru proposed a robust-observer based sliding mode controller to achieve the motion control task in the presence of incomplete state measurements and sensor inaccuracies. A two-layer lateral path tracking controller are presented in [ 20 ], the upper-layer controller is implemented with a linear time-varying model predictive control (LTV-MPC) algorithm, the lower-layer controller is implemented by a radial basis function neural network proportion-integral-derivative (RBFNN-PID) algorithm. The advantage of the two-layer controller is that controller can track the reference paths accurately while ensuring the stability of the vehicle, however, this type of the tracking controller suffers from high computation.…”