  This work investigates traffic control via controlled connected and automated
vehicles (CAVs) using novel controllers derived from the linear-quadratic
regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks
impacting the surrounding traffic with their speeds as control inputs. An
iterative controller algorithm based on the LQR theory is proposed along with a
variant that allows for penalizing abrupt changes in platoons speeds. The
controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an
extended cell transmission model (CTM) which considers the capacity drop
phenomenon for a realistic representation of traffic in congestion. The impact
of various parameters of the proposed controller on the control performance is
analyzed. The effectiveness of the proposed traffic control algorithms is
tested using a traffic control example and compared with existing
proportional-integral (PI)- and model predictive control (MPC)- based
controllers from the literature. A case study using the TransModeler traffic
microsimulation software is conducted to test the usability of the proposed
controller as well as existing controllers in a realistic setting and derive
qualitative insights. It is observed that the proposed controller works well in
both settings to mitigate the impact of the jam caused by a fixed bottleneck.
The computation time required by the controller is also small making it
suitable for real-time control.
