How does model predictive controller work?

How does model predictive controller work?

Learn how model predictive control (MPC) works. MPC uses a model of the plant to make predictions about future plant outputs. It solves an optimization problem at each time step to find the optimal control action that drives the predicted plant output to the desired reference as close as possible.

What is adaptive model predictive control?

Adaptive MPC controllers adjust their prediction model at run time to compensate for nonlinear or time-varying plant characteristics. Such a linear time-varying model is useful when controlling periodic systems or nonlinear systems that are linearized around a time-varying nominal trajectory.

What is predictive control algorithm?

Predictive control is a control algorithm based on a predictive model of the process. The model is used to predict the future output based on historical information about the process, as well as anticipated future input. It calculates future control action based on a penalty or a performance function.

Where is model predictive control used?

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s.

What is the difference between PID and MPC?

The difference between the MPC and PID controllers occurs due to the following reasons. PID controller handles only a single input and a single output (SISO systems). MPC controller is a more advanced method of process control used for MIMO systems (Multiple Inputs, multiple Outputs).

What is generalized predictive control?

Generalized Predictive Control (GPC) The basic idea of GPC is to calculate a sequence of future control signals in such a way that it minimizes a multistage cost function defined over a prediction horizon.

Is MPC adaptive control?

MPC control predicts future behavior using a linear-time-invariant (LTI) dynamic model. At each control interval, the adaptive MPC controller updates the plant model and nominal conditions. Once updated, the model and conditions remain constant over the prediction horizon.

What is the parameter measured by adaptive controller?

The foundation of adaptive control is parameter estimation, which is a branch of system identification. Common methods of estimation include recursive least squares and gradient descent. Both of these methods provide update laws which are used to modify estimates in real time (i.e., as the system operates).

What does receding horizon mean?

model predictive control
Receding horizon control (RHC), also known as model predictive control (MPC), is a general purpose control scheme that involves repeatedly solving a constrained optimization problem, using predictions of future costs, disturbances, and constraints over a moving time horizon to choose the control action.

What are the advantages of MPC?

Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account.