Greedy vs Centralized MPC for platooning | Combine

Greedy vs Centralized MPC for platooning

Have you seen a commercial of a train of cars traveling on a highway and keeping the same inter-vehicle distances and speeds? Yes! You think it is fun, comfortable and makes our trips easier and not nerve-racking. Guess what? It is more advantageous than what you think. Believe it or not, it can be a significant contribution to save the planet from the increasing atmospheric concentration of carbon dioxide and a great deal for the economy as well! However, the latest control strategies proposed that varying the distances and speeds at hilly terrains could even save more fuel. Curious about how the control design will look like? Then you are in the right place.


One of the things that has been always drawing my attention is the automated
vehicular control strategies and how they could reshape the transport sector
dramatically. One of the methods that many automotive manufacturers have
been recently developing is what is called platooning. A platoon is a convoy of
trucks that maintains fixed inter-vehicular distances, as shown in the Figure 1,
and usually applied on highways.

Figure 1: Trucks Platoon

The advantages go beyond the driver’s convenience and comfort. Having a lead
truck with a large frontal-area would reduce the air drag force acting on the
succeeding vehicles. Therefore, the required torque to drive the trucks at cer-
tain speed will be decreased which lead to less fuel consumption. That means,
of course, less CO2 emissions and lower financial burdens.
However, in a single-vehicle level, there is another approach that has been inves-
tigated for a better fuel economy. This approach utilizes the future topography
information in order to optimize the speed and the gear for a vehicle travelling
in a hilly terrain by exploiting the vehicles’ potential and kinetic energies stor-
ages. In this approach the velocity will vary along the road depending on the
road gradient. The look-ahead strategy could be seen as a contradiction to the
platooning approach in which vehicles maintain almost the same speed along
the road.


A combination between these approaches could be implemented using the model
predictive control (MPC) scheme. Since there are many process constraints,
such as inter-vehicular distances, engine maximum torque, road velocity limits,
etc. MPC is a perfect candidate to handle these constraints especially that in
many cases the system will be operating close to the limits. The control design
could be handled in two approaches, the centralized control design and the
decoupled control design. In the centralized controller, as shown in the Figure
2, all the vehicles’ private data such as mass, engine specs, etc. in addition to
their states such as velocity and time headway are sent to the central predictive
controller via vehicle to vehicle communication, could be in one of the trucks
probably the lead vehicle or even in a cloud. One of the methods used for optimal
control is the convex quadratic programming problem (CQPP) in which every
local minimum is a global minimum. The problem is as follows

$$ min\,z = f_0(x) \\
f_i(x) \leq 0 \\
Ax = b $$

Where f0,f1,f2, …, fm, is the objective function, and the inequality constraints
are convex functions. However, the equality constraints are affine functions.
In the platoon case, some convexification is needed in order to get CQPP. Hense,
the problem is solved and the optimal speed and time headway references are
sent back to the vehicles’ local controllers. This approach optimizes the fuel
consumption for the whole platoon rather than individual vehicles in which the
group interest comes first. One of the drawbacks of this approach is that in order
to solve the problem you need to handle huge matrices since all the vehicles info
is handled at once. In other words, this approach is rather computationally

Figure 2: Centralized adaptive cruise control

The decoupled architecture, as depicted in the Figure 3, could be a solution for
the computation capacity issues. Instead of handling the quadratic program-
ming (QP) problem for the whole platoon, each vehicle considers itself, which is
why called greedy. The problem starts to be solved from the leading vehicle and
goes backwards. Each vehicle solves the QP, considering the gaps in front of the
vehicle and the road topography, and sends states to the succeeding vehicles.
The pros of this approach are that trucks need not to share their private data
and the matrices sizes are much smaller. So the computation time is less than in
the greedy control strategy but the solution is not as optimal as the centralized

Figure 3: Greedy approach


As it is mentioned above, formulating a convex quadratic programing problem
is used to get the fuel-saving velocities. Since the vehicle dynamics are quite
nonlinear, linear approximations are needed, therefore, finding an appropriate
velocity reference is essential, assuming that the vehicle will be driven close
to the reference. Finding such reference should consider many factors such as
maximum traction force along the road, road limits and the cruise speed set by
the driver. One of the other challenges is gear optimization which could be solved
using dynamic programming. The complexity of dynamic programing problem
increases exponentially with the rise of the vehicles number, as a result, the
problem become computationally demanding, therefore, it is not very reliable
for the real-time implementation.

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