21-02-2012, 04:14 PM
Design of Linear Model Predictive Controller for a Catalytic CSTR
[attachment=17640]
What is Model Predictive Control?
MPC is a form of control in which
the current control action is obtained by solving on-line, at each sampling instant, a finite horizon open-loop optimal control problem,
using the current state of the plant as the initial state;
the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant
What makes MPC successful in industry
It handles multivariable control problems naturally
It can take account of actuator limitations
It allows operation closer to constraints, hence increased profit
It has plenty of time for on-line computations
It can handle non-minimal phase and unstable processes
It is an easy to tune method and
It handles structural changes
How Model Predicitve Control works
Internal model predicts plant behavior over a future horizon
in time.
Plant interactions or time delays are captured by the model.
Constraints on plant inputs and outputs are explicitly handled.
Feedback compensates for modeling error and rejects disturbances.
[attachment=17640]
What is Model Predictive Control?
MPC is a form of control in which
the current control action is obtained by solving on-line, at each sampling instant, a finite horizon open-loop optimal control problem,
using the current state of the plant as the initial state;
the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant
What makes MPC successful in industry
It handles multivariable control problems naturally
It can take account of actuator limitations
It allows operation closer to constraints, hence increased profit
It has plenty of time for on-line computations
It can handle non-minimal phase and unstable processes
It is an easy to tune method and
It handles structural changes
How Model Predicitve Control works
Internal model predicts plant behavior over a future horizon
in time.
Plant interactions or time delays are captured by the model.
Constraints on plant inputs and outputs are explicitly handled.
Feedback compensates for modeling error and rejects disturbances.