In a chemical process, independent variables that can be adjusted by the controller are often either the setpoints of regulatory PID controllers (pressure, flow, temperature, etc.) or the final control element (valves, dampers, etc.). MPC models predict the change in the dependent variables of the modeled system that will be caused by changes in the independent variables. Common dynamic characteristics that are difficult for PID controllers include large time delays and high-order dynamics. The additional complexity of the MPC control algorithm is not generally needed to provide adequate control of simple systems, which are often controlled well by generic PID controllers. The models used in MPC are generally intended to represent the behavior of complex dynamical systems. Generalized predictive control (GPC) and dynamic matrix control (DMC) are classical examples of MPC. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry. PID controllers do not have this predictive ability. Also MPC has the ability to anticipate future events and can take control actions accordingly. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from a linear–quadratic regulator ( LQR). The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. In recent years it has also been used in power system balancing models and in power electronics. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. MPCtools has been developed by Johan Åkesson.Model predictive control ( MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints.
If you find any bugs, please contact Developers MPCtools can be freely downloaded as a compressed archive including two application examples.įor installation instructions, see the reference manual. MPCtools is designed to run with Matlab 7 (R14) and Simulink 6, but should work also with Matlab 6 (R13). Two different QP solvers for solving the optimization problem.Integral action by means of disturbance estimation.Observer support for state and disturbance estimation.Linear inequality constraints on states and controls.Support for linear state space models for prediction.MPCtools provides easy to use functions to create and simulate basic MPC controllers based on linear state space models. MPCtools is a freely available Matlab/Simulink-based toolbox for simulation of MPC controllers. A toolbox for simulation of MPC controllers in Matlab
0 kommentar(er)
