Model predictive control (MPC) is an optimal control technique in which the calculated control actions minimize a cost function for a constrained dynamical system over a finite, receding, horizon. At each time step, an MPC controller receives or estimates the current state of the plant.

Model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant. At each control interval an MPC algorithm attempts to optimize future plant

The results show that the GPC can achieve longer prediction horizons than other controllers based on model predictive control. Furthermore, in contrast to the PI controller, the GPC...

A model predictive controller uses, at each sampling instant, the plant’s current input and output measurements, the plant’s current state, and the plant’s model to • calculate, over a finite horizon, a future control sequence that optimizes a given per-

Figure 20.1 Block diagram for model predictive control. A block diagram of a model predictive control sys-tem is shown in Fig. 20.1. A process model is used to predict the current values of the output variables. The residuals, the differences between the actual and pre-dicted outputs, serve as the feedback signal to a Predic-tion block.

Block Diagram of the Successive Linearization Model Predictive Controller. Tutorial objectives. This tutorial covers basic implementation of the SLMPC controller. This consists of: Linearization of model; Discretization of linearized model; Solving SL …

... predictive control exerts the control by minimizing a cost function that explains the required system behavior and compares the estimated outputs with a reference value.

Block diagram of model predictive control system. The automatic driving robot can replace the driver in the traditional test and ADAS test; Model Predictive Control (MPC) is used...

The block diagram in Fig. 1 represents a generic feed-back loop, with the plant P(z) and the compensator, or controller, C(z). The variable z describes the complex argument of the plant and controller models in the discrete frequency domain. While an estimator is usually required for state feedback methods, it is assumed in the following

Block diagram of a model predictive controller in a feedback loop with a plant. In large-scale dynamical systems and networks of cooperating systems, it is often impossible or undesirable to control the overall system with one centralized controller.