have you ever noticed that control
engineers really like three-letter
acronyms in this series will focus on
MPC which might be the most popular
control for some engineers but what it
really stands for is model predictive
control in this video we'll discuss the
reasons why you will use it
but first let's briefly look at the
basic idea behind MPC MPC is a feedback
control algorithm that uses a model to
make predictions about future outputs of
a process here's an analogy to explain
what we mean say that you're driving and
your goal is to keep the car within the
lane the decisions you make to achieve
the Skoll are very similar to have a
model predictive controller works you
know your car's characteristics how fast
it goes or how much it turns based on
the control actions you take using this
model of the car you do simulations in
your head these give you predictions
about your future trajectory based on
the control actions you choose then you
select the optimal action that drives
the predicted trajectory as close as
possible to the desired trajectory this
is a simplistic example to give you the
general idea but in the next video we'll
have a much more detailed discussion
about how MPC works here are some of the
reasons why you would want to use model
predictive control MPC can handle
multi-input multi-output systems that
might have interactions between their
inputs and outputs for example in this
MIMO system a change in the second
output also affects the first output if
we want to use PID controllers designing
them will be challenging because the two
control loops will operate independent
of each other as if there are no
interactions between the two loops and
designing larger systems will be even
more challenging as they will require
tuning too many controller gains the
advantage of MPC is that it's a multi
variable controller that controls the
output simultaneously
by taking into account all the
interactions between system variables
another strength of MPC is that it can
handle constraints constraints are
important because while aiding them can
lead to undesired consequences for
example some of the safety constraints
while driving are that cars must obey
speed limits and maintain a safe
distance from other cars
there are also constraints due to
physical limitations of the car such as
limits on acceleration if this was an
autonomous car controlled by an PC the
controller will track a desired
trajectory while satisfying all these
constraints another feature of MPC's
it's preview capability which is similar
to feed forward control let's say the
autonomous car travels on a curvy road
if the controller doesn't know that
there's a corner coming ahead it will be
only able to apply brakes while it's
taking the corner however if the car has
a camera in the front which provides
information about the feature trajectory
the controller will know in advance
about the upcoming corner so it can
break sooner to safely stay in the lane
and PC can easily incorporate future
reference information into the control
problem to improve controller
performance model predictive controllers
have been used in the process industry
since the 1980s with the increasing
computing power of microprocessors their
use has spread to other fields as well
some of these areas are listed here
MPC has all these benefits but something
to notice that it requires a powerful
fast processor with a large memory and
the reason is that MPC stores an online
optimization problem at each time step
which we'll talk in much more detail in
the next video
in summary MPC utilizes the model of a
system to predict its future behavior
and it solves an online optimization
problem to select the best control
action that drives the predicted output
to the reference
can control multi-input multi-output
systems that might have interactions
between inputs and offers MPC can handle
constraints and it has preview
capability it is also widely used in
many industries in the next video we
will discuss how model predictive
controllers work
you