Real‐time NMPC path tracker for autonomous vehicles

Abstract

This work proposes a framework to design, formulate and implement a path tracker for self-driving cars (SDC) based on a nonlinear model-predictive-control (NMPC) approach. The presented methodology is developed to be used by designers in the industrial sector, practitioners, and academics. Therefore, it is straight forward, flexible, and comprehensive. It allows the designer to easily integrate multiple objective terms in the cost function either opposing or correlating. The proposed design of the controller not only targets accurate tracking but also comfortable ride and fast travel time by introducing several sub-objective terms in the main cost function to satisfy these goals. These sub-objective terms are weighted according to their contribution to the optimization problem. The SDC-NMPC framework is developed using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. This makes the SDC-MPC well suited for use in both ADAS and selfdriving cars. Extensive simulation studies featuring complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed SDC-NMPC at different speeds. The presented analysis shows that the proposed controller with its tuning technique outperforms that of the PID-based controller.

Real‐time game‐theoretic model predictive control for differential game of target defense

“…The advantages of MPC comprises its conceptual ability to handle control of multi‐variable coupled dynamical systems, constraints on the states, constraints on control inputs and nonlinearities in the systems model. In addition, MPC has systematic design approach and has a well understood tuning parameters, that is, prediction horizon length and weighting matrices [32–36]. A discrete nonlinear model predictive control (NMPC) can be formulated by solving the following optimal control problem (OCP): $$$$ \underset{\mathbf{u}\in {\mathrm{\mathbb{R}}}^{n_{\mathbf{u}}\times N}}{\min }{J}_N\left({\mathbf{x}}_{\mathbf{0}},\mathbf{u}\right) $$$$ subject to $$$$ \left\{\begin{array}{l}\mathbf{x}(0)={\mathbf{x}}_{\mathbf{0}},\\ {}\mathbf{x}\left(k+1\right)=\mathbf{f}\left(\mathbf{x}(k),\mathbf{u}(k)\right);k\in \left\{0,1,\dots N-1\right\},\\ {}{...$$…”

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…Hierarchical control refers to the use of two or more controllers to control different targets independently, and this control method is relatively simple to implement and can be designed in a coordinated manner based on transverse and longitudinal path tracking control algorithms [72]. For example, in the literature [73], MPC controllers were used to handle perturbations in pavement curvature, PID feedback control to suppress instability and modeling errors; MohammadRokonuzzaman et al proposed an MPC algorithm designed with a neural network-based vehicle learning dynamic model [74]; Yao et al proposed an MPC path with longitudinal speed compensation in the predictive time domain tracking controller and longitudinal speed compensation strategy [75]; Cui et al designed a traceless Kalman filter and proposed a multi-constraint model prediction controller (MMPC) [76]; Wael Farag et al proposed a framework for a path tracker (SDC) for self-driving cars based on the NMPC approach [77]. After simulation verification, all of the above decentralized control methods improve the path tracking accuracy and vehicle driving stability under high-speed conditions.…”

Model predictive control‐based longitudinal control for autonomous electric vehicle with changing mass

“…In addition to mass estimation, a controller needs to be designed for speed tracking. As we know, model predictive control (MPC) has been widely used in autonomous driving [18]. In order to protect the actuators and obtain the desired performance, Liu et al resorted MPC to deal with the input constraints from the wheel torque limits [19].…”