Review and Comparison of Path Tracking Based on Model Predictive Control

Abstract

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

Complex Track Maneuvering using Real-Time MPC Control for Autonomous Driving

“…The motivation of this paper is to facilitate for the SDC path tracker to achieve the previous kind-of-opposing objectives by proposing a sophisticated control methodology that takes into account all these objectives and provide a way to find a delicate balance among them based on the designer preferences. The proposed methodology is based on the Model Predictive Control (MPC) technique due to its flexibility and practicality [10]. The MPC technique is an optimal control method that has the advantage of tailoring its optimization strategy in a way that offers multiple options to reach the desired performance for strongly nonlinear systems with constraints [10], which are difficult to handle using traditional linear control approaches [11].…”

“…The proposed methodology is based on the Model Predictive Control (MPC) technique due to its flexibility and practicality [10]. The MPC technique is an optimal control method that has the advantage of tailoring its optimization strategy in a way that offers multiple options to reach the desired performance for strongly nonlinear systems with constraints [10], which are difficult to handle using traditional linear control approaches [11]. http://dx.doi.org/10.12785/ijcds/090511 http://journal.uob.edu.bh Numerous thought-provoking MPC design methodologies exist in the literature, in [12] Ikeda et al proposed a new design of optimal control that takes only specified discrete values and applies finite-horizon sum-ofabsolute-values optimization.…”

Anti‐sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control

“…The most important one is the real-time performance of the controller. The real-time performance of non-linear MPC (NMPC) is inferior [8][9][10], moreover, in [7], the optimisation function has an added dimension due to the increase of the target state. So the method in [7] causes the real-time performance of the controller to deteriorate further.…”

“…So we also considered other solutions. Considering the advantages of linear MPC (LMPC) in real-time performance [10], we designed a multilayer MPC (MMPC) controller combining NMPC and LMPC [11]. In this controller, the lower LMPC is used to control the robot to track the reference path, while the upper NMPC is used to adjust the longitudinal velocity.…”

“…The design process of the NMPC controller is described in detail in [7,9,10]. It must be pointed out that in this Letter, the prediction model of the NMPC controller is the kinematics model in [7], not the dynamic model.…”