Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control

We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.Comment: To Appear in ICINCO 202

Real-time predictive control strategy for a plug-in hybrid electric powertrain

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.mechatronics.2015.04.020 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.NSERCToyotaMaplesoft Industrial Research Chair progra

An optimal power management strategy for power split plug-in hybrid electric vehicles

Originally published by Inderscience: Taghavipour, A., Azad, N. L., & McPhee, J. (2012). An optimal power management strategy for power split plug-in hybrid electric vehicles. International Journal of Vehicle Design, 60(3/4), 286. doi:10.1504/ijvd.2012.050085Model Predictive Control (MPC) can be an interesting concept for designing a power management strategy for Hybrid Electric Vehicles (HEVs) according to its capability of online optimisation by receiving current information from the powertrain and handling hard constraints on such problems. In this paper, a power management strategy for a power split plug-in HEV is proposed using the concept of MPC to evaluate the effectiveness of this method on minimising the fuel consumption of those vehicles. Also, the results are compared with dynamic programming

Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions

Taghavipour, A., Azad, N. L., & McPhee, J. Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(5), 624–640. Copyright © 2014 SAGE. Reprinted by permission of SAGE Publications. https://dx.doi.org/10.1177/0954407014547925In this article, a power management scheme for a plug-in power-split hybrid electric vehicle is designed on the basis of the model predictive control concept of charge depletion plus charge sustenance strategy and the blended-mode strategy. The commands of model predictive control are applied to the powertrain components through appropriate low-level controllers: standard proportional–integral controllers for electric machines, and sliding-mode controllers for engine torque control. Minimization of the engine emissions is a key factor for designing the engine’s low-level controller. Applying this control scheme to a validated high-fidelity model of a plug-in hybrid electric vehicle, developed in the MapleSim environment with a chemistry-based Lithium-ion battery model, results in considerable improvements in the fuel economy and the emissions performance.NSERCToyotaMaplesoft Industrial Research Chair progra

Design and evaluation of an observer-based disturbance rejection controller for electric power steering systems

Mehrabi, N., McPhee, J., & Azad, N. L. Design and evaluation of an observer-based disturbance rejection controller for electric power steering systems. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 230(7), 867–884. Copyright © 2015 SAGE. Reprinted by permission of SAGE Publications. http://dx.doi.org/10.1177/0954407015596275The goal of this paper is to develop an observer-based disturbance rejection electric power steering (EPS) controller to provide steering assistance and improve the driver’s steering feel. For the purpose of control design, a control-oriented model of a vehicle with a column-assist EPS system is developed and verified against a high-fidelity multibody dynamics model of the vehicle. The high-fidelity model is used to mimic vehicle dynamics to study controller performance in realistic driving conditions. Then, a linear quadratic Gaussian approach is used to design an EPS optimal controller, in which a Kalman filter estimates the unmeasured steering system’s states and external disturbance. A new formulation for the linear quadratic regulator objective function is proposed to take advantages of the known information about the system dynamics to attenuate the disturbance and magnify the driver’s torque., Finally, the EPS controller is applied to the high-fidelity vehicle model in a software-in-the-loop simulation to evaluate its robustness and performance under realistic conditions. The results show that the proposed controller can effectively reduce the disturbance induced in the steering rack, and simultaneously magnify the driver’s steering torque by use of a bi-linear EPS characteristic curve. Then, to show the disturbance rejection properties of this EPS controller, its performance is compared with H2/H∞ and PID control designs using time and frequency domain analysis.Ontario Centres of Excellence (OCE)Natural Sciences and Engineering Research Council of Canada (NSERC)ToyotaMaplesof

A Comparative Analysis of Route-Based Energy Management Systems for Phevs

This is the peer reviewed version of the following article: Taghavipour, A., Vajedi, M., Azad, N. L., & McPhee, J. (2015). A Comparative Analysis of Route-Based Energy Management Systems for Phevs. Asian Journal of Control, 18(1), 29–39, which has been published in final form at https://dx.doi.org/10.1002/asjc.1191. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Plug-in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero-emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A-ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine-tuned for the Toyota Prius plug-in hybrid powertrain and their performances are evaluated by using a high-fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A-ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A-ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules