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.

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.

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-

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.

... 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.

We present a combination of an output feedback model predictive control scheme and a Gaussian process‐based prediction model that is capable of efficient online learning.

Block diagram of a model predictive control scheme in a feedback loop with a plant. Predictive control methods use finite-horizon model-based predictions to compute an optimal control input.

Blocking factors can be used to ease the computational requirements. This means that constraints and cost is only evaluated at certain time steps, contained in sets Ip and Iu. uk uk+1 − ... ur k+1 , ... ∆uk ∆uk+1 ... yk+1 yk+2 , ey = ... control moves and the last term penalizes tracking error.