A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.

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Trajectory Tracking Control of Intelligent Driving Vehicle Based on Model Predictive Control

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327274 ◽

2020 ◽

Author(s):

Lin Qi ◽

Xiaohong Jiao ◽

Zhong Wang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Trajectory Tracking Control

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An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽

10.3390/machines9050105 ◽

2021 ◽

Vol 9 (5) ◽

pp. 105

Author(s):

Zhenzhong Chu ◽

Da Wang ◽

Fei Meng

Keyword(s):

Model Predictive Control ◽

Prediction Model ◽

Real Time ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Working Environment ◽

Gray Wolf ◽

Trajectory Tracking Control ◽

System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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