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.
Highlights
Model predictive control (MPC) is an optimal control method that emerged in the 1970s
The above evidence indicates that nonlinear model predictive control (NMPC) is a control method that is superior to linear model predictive control (LMPC) and linear error MPC (LEMPC), but we have found that another MPC method is called NMPC
In the NMPC controller, the discretized mobile robot model is directly used as the prediction model
Summary
Model predictive control (MPC) is an optimal control method that emerged in the 1970s. They designed a path tracking controller for helicopters that are based on MPC The feature of this controller is that the prediction model can work through matrix operations because the model of mobile devices is linearized. The paper that we can find, which was the earliest to adopt the nonlinear error model as the prediction model, is the work of Gu et al [62] This model might come from the path tracking of mobile robots based on feedback control [63]. In 2009, Kanjanawanishkul et al designed a path tracking controller for mobile robots based on the nonlinear error model [64] We believe that it is necessary to compare these control methods
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