Model-Free Predictive Control and Its Applications

Abstract

Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.

Application of Feedforward Cascade Compound Control Based on Improved Predictive Functional Control in Heat Exchanger Outlet Temperature System

“…By substituting the data into Equations (10) and (12), the transfer function controlling channel 4 is obtained, as shown in Equation (17). The transfer function represents the control effect of the flow rate of hot fluid on the exit temperature of hot fluid.…”

“…Since 1970, model predictive control (MPC) has been proposed and developed, and it has a good control effect on complex optimization problems [17]. However, when dealing with nonlinear, uncertain, and time-varying control systems, MPC control alone is still relatively laborious.…”

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

“…Many researchers have used the Model Predictive Control (MPC) method [25][26][27], which provides a more robust control for robot trajectory. The basic concept of this control approach is to predict the future behavior of a system up to a determined finite prediction horizon time by minimizing the finite horizon cost function defined under the future states with some determined constraints.…”