Constrained Model Predictive Control using Kinematic Model of Vehicle Platooning in VISSIM Simulator Rongkai Zhang, Anuj Abraham, Soumya Dasgupta and Justin Dauwels Abstract—Driving Heavy Duty Vehicles (HDVs) as a platoon has potential to significantly reduce the fuel consumption, human labor and increase the safety. A suitable controller which can maintain the vehicle movement in a defined topology is essential for HDV platooning. This paper proposes a controller based on the combination of Constant Distance (CD) and Headway Time (HT) topologies using Model Predictive Control (MPC) for a longitudinal HDVs platoon. In addition to this, a MPC controller is compared with conventional PID (Proportional-Integral-Derivative) controller. The controller aims to maintain intervehicular distance and headway time between the vehicles for two cases, namely unconstrained and constrained optimization problem. The predictive control algorithm uses a kinematic model of vehicle platooning. A systematic handling of constraints yields significant improve- ments in the performance of the proposed MPC strategy over conventional PID controller. A road network of a U.S. freeway I5 has been built in VISSIM for the simulations. The results and discussions are at the end of the paper. I. INTRODUCTION The idea of heavy duty platooning is analogous to the concept of railways on a highway road networks. This is a promising solution to increase the safety on roads, reduce human labor and fuel cost. In the last decade, platooning was originally designed for Automated Highway System (AHS) and enables a number of vehicles to drive within a short, acceptable intervehicular distance. The improvements in wireless communication and vehicle control technology make platooning feasible for partially automated vehicles, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) vehicles [1]. While the technical feasibility of platooning has been analyzed world- wide under numerous projects, the implementation details of the platooning vary since different objectives and motivations are envisioned. The common part in all is that the platooning strategies depend on a proper controller to guarantee the following vehicles can track the first vehicle quickly without huge overshoot and oscillations [2]. Currently, there are many research works carried out in the design and implementations of controllers for platooning with different topologies [3], [4]. In ACC control, an individual vehicle has a sensor to measure the distance and apply the control strategy to itself in order to maintain the required distance and velocity. This is a decentralized approach and requires larger distance gap *This work is supported by the NTU-NXP Intelligent Transport System Test-Bed Living Lab Fund S15-1105-RF-LLF from the Economic Develop- ment Board(EDB), Singapore. Rongkai Zhang, Anuj Abraham, Soumya Dasgupta and Justin Dauwels are with Faculty of Electrical and Electronic Engineering, Nanyang Tech- nological University, Singapore, aabraham@ntu.edu.sg between the vehicles for safety aspect [5]. CACC control has shown improved dynamics by regularly monitoring the movement of the platoon vehicles using wireless communica- tion. However, CACC will not be able to provide appropriate control action when the communication between the vehicles is affected severely due to congestion in the network or temporary disruptions due to surrounding road conditions and infrastructure [6]. Hence, a predictive technique plays a significant role in overcoming the effect of packet drops. In the last few years, MPC schemes can be found in the literature [7], [8], [9], [10] for the design and implementa- tion of ACC or CACC controller considering intersection collision avoidance and other vehicle interventions with V2X communication. CACC control aims for improved throughput and reduced fuel consumption [11]. The control objective is to maintain a desired intervehicular distance between the vehicles. Cruise control is a robust approach which overcomes the effects of packet drops. Also, MPC provides a prediction of the future desired acceleration for a horizon defined which is applied as feedforward action in the preceding vehicle. In this robust predictive design approach, a buffer is used to reduce the control errors during the time intervals of packet drops [12]. In general, MPC application maintains equal velocity for all the vehicles, while focusing on fuel saving [13]. Also, the constraints used to formulate the MPC scheme vary among different works. The proposed control method in this work is also based on MPC, for which the objective is to minimize the control errors over a prediction horizon, given certain constraints. The intended acceleration over a prediction hori- zon is determined by an MPC using a kinematic model of the platoon in VISSIM using combined CD and HT topologies. Here, VISSIM simulator is invoked with MATLAB and both of them communicate via VISSIM COM interface. Whereas, ACC and CACC have been modeled in Dynamic Link Library coded in C++ [14]. Further, simulation results show that MPC design improves the overall control performance of platoon. The rest of the paper is organized as follows: Section II indicates the terminology and notation. Section III deals with the modeling and controller design. Section IV shows the simulation results using conventional PID controller and the MPC strategy with 14 vehicles platooning for a U.S. freeway I5 in VISSM. Section V shows the discussion on comparative results of PID controller, unconstrained MPC and constrained MPC. The conclusion and scope of future work are indicated in Section VI.