The optimization goal will be to minimize the distance and time it takes for the predicted output to match up with the reference trajectory over the prediction horizon.ģ. It performs an on-the-fly calculation to determine the impact of a control action at any timestep, let's say “k+1”, on the predicted output of “k+1” and then measures the distance of the predicted output from the reference trajectory.ī. In order to determine what future control actions are required, the MPC controller runs an optimization algorithm over the prediction horizon (p), from “k+1” to “k + p”.Ī. As a first step, the controller samples the current measured output at present time “k”Ģ. However, the controller needs to know what control actions to take in future steps, “k+1” to “k + p”, to get there.ġ. At this point, the controller has an idea of what the desired target is, denoted as the “reference trajectory” in Figure 2.
The MPC controller starts at present time, denoted as “k” in Figure 2. MPC Receding Horizon (Image by Martin Behrendt on Wikimedia Commons, CC BY-SA 3.0) To understand how all these components fit together, we need to review the concept of a “receding horizon”, as illustrated in Figure 2.įigure 2. reducing deviations from a target product quality Receding Horizon objectives: the optimization goal the operator seeks to achieve, i.e. a compliance limit on discharge concentrationsĤ. constraints: specifies the boundary of acceptable future states that the optimizer cannot exceed, i.e. an optimizer: a mathematical solver that can iteratively determine the “optimal” control action based on outputs from the simulation modelģ. it can predict what the future state of a process will be based on control actionsĢ.
a simulation model: a mathematical representation of the process dynamics, i.e. Block diagram of an MPC Controller (Image by author)Īs previously discussed, the core components of an MPC controller include:ġ. This is particularly useful for complex, nonlinear systems such as bioreactors or distillation columns where the impact of tighter controls can yield substantial cost savings.Īt is Figure 1. MPC allows operators to run their processes more efficiently by operating much closer to constraints than would be possible with conventional reactive controllers in order to save on costs or increase production. Meanwhile, examples of constraints include regulatory limits or maintenance requirements. Examples of process disturbances include changes in raw material quality, environmental conditions (i.e. Predictive Control operates by performing dynamic, real-time optimization to generate control actions that are adaptive to process disturbances and are compliant with user-specified constraints. In this article, we’ll explore how MPC controllers work in more detail. We reviewed how PID controllers work, discussed their limitations and introduced the concept of predictive control. So far, we’ve covered the fundamentals of process control.
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