Optimal Sensor Configuration and Fault-Tolerant Estimation of Vehicle States

© SAE, Zarringhalam, R., Rezaeian, A., Fallah, S., Khajepour, A. et al., "Optimal Sensor Configuration and Fault-Tolerant Estimation of Vehicle States," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 6(1):83-92, 2013, doi:10.4271/2013-01-0175.This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.Automotive Partnership CanadaOntario Research Fun

Optimal Torque Control for an Electric-Drive Vehicle with In-Wheel Motors: Implementation and Experiments

© SAE, Athari, A., Fallah, S., Li, B., Khajepour, A. et al., "Optimal Torque Control for an Electric-Drive Vehicle with In-Wheel Motors: Implementation and Experiments," SAE Int. J. Commer. Veh. 6(1):82-92, 2013, doi:10.4271/2013-01-0674.This paper presents the implementation of an off-line optimized torque vectoring controller on an electric-drive vehicle with four in-wheel motors for driver assistance and handling performance enhancement. The controller takes vehicle longitudinal, lateral, and yaw acceleration signals as feedback using the concept of state-derivative feedback control. The objective of the controller is to optimally control the vehicle motion according to the driver commands. Reference signals are first calculated using a driver command interpreter to accurately interpret what the driver intends for the vehicle motion. The controller then adjusts the braking/throttle outputs based on discrepancy between the vehicle response and the interpreter command. A test vehicle equipped with four in-wheel electric motors, vehicle sensors, communication buses, and dSPACE rapid prototyping hardware is instrumented and the control performance is verified through vehicle handling tests under different driving conditions.Automotive Partnership CanadaOntario Research FundGeneral Motor

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

© IEEE 2017. Rasekhipour, Y., Khajepour, A., Chen, S.-K., & Litkouhi, B. (2016). A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1255–1267. https://doi.org/10.1109/TITS.2016.2604240Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities

Corner-based estimation of tire forces and vehicle velocities robust to road conditions

The final publication is available at Elsevier via https://doi.org/10.1016/j.conengprac.2017.01.009 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.Automotive Partnership Canada, Ontario Research Fund, General Motors Co

Longitudinal vehicle state estimation using nonlinear and parameter-varying observers

The final publication is available at Elsevier via https://doi.org/10.1016/j.mechatronics.2017.02.004 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A corner-based velocity estimation approach is proposed which is used for vehicle’s traction and stability control systems. This approach incorporates internal tire states within the vehicle kinematics and enables the velocity estimator to work for a wide range of maneuvers without road friction information. Tire models have not been widely implemented in velocity estimators because of uncertain road friction and varying tire parameters, but the current study utilizes a simplified LuGre model with the minimum number of tire parameters and estimates velocity robust to model uncertainties. The proposed observer uses longitudinal forces, updates the states by minimizing the longitudinal force estimation error, and provides accurate outcomes at each tire. The estimator structure is shown to be robust to road conditions and rejects disturbances and model uncertainties effectively. Taking into account the vehicle dynamics is time-varying, the stability of the observer for the linear parameter varying model is proved, time-varying observer gains are designed, and the performance is studied. Road test experiments have been conducted and the results are used to validate the proposed approach.Automotive Partnership Canada [APCPJ 395996-09], Ontario Research Fund [ORF-RE-04-039], General Motors Co

Resilient Corner-Based Vehicle Velocity Estimation

© 2017 IEEE. Pirani, M., Hashemi, E., Khajepour, A., Fidan, B., Kasaiezadeh, A., Chen, S.-K., & Litkouhi, B. (2017). Resilient Corner-Based Vehicle Velocity Estimation. IEEE Transactions on Control Systems Technology, 1–11. https://doi.org/10.1109/TCST.2017.2669157This paper presents longitudinal and lateral velocity estimators by considering the effect of the suspension compliance (SC) at each corner (tire) for ground vehicles. The estimators are developed to be resilient to sensor measurement inaccuracies, model and tire parameter uncertainties, switchings in observer gains, and measurement failures. More particularly, the stability of the observer is investigated, and its robustness to road condition uncertainties and sensor noises is analyzed. The sensitivity of the observers' stability and performance to the model parameter changes is discussed. Moreover, the stability of the velocity observers for two cases of arbitrary and stochastic switching gains is investigated. The stochastic stability of the observer in the presence of faulty measurements is also studied, and it is shown that if the probability of a faulty measurement occurring is less than a certain threshold, the observer error dynamics will remain stochastically stable. The performance of the observer and the effect of the SC are validated via several road experiments.Automotive Partnership Canada || Ontario Research Fund || General Motors Co. [grant numbers APCPJ 395996-09 and ORF-RE-04-039

Integrated estimation structure for the tire friction forces in ground vehicles

© IEEE 2017 Hashemi, E., Pirani, M., Khajepour, A., Fidan, B., Kasaiezadeh, A., Chen, S.-K., & Litkouhi, B. (2016). Integrated estimation structure for the tire friction forces in ground vehicles (pp. 1657–1662). IEEE. https://doi.org/10.1109/AIM.2016.7577008This paper presents a novel corner-based force estimation method to monitor tire capacities required for the traction and stability control systems. This is entailed for more advanced vehicle stability systems in harsh maneuvers. A novel estimation structure is proposed in this paper for the longitudinal, lateral, and vertical tire forces robust to the road friction condition. A nonlinear and a Kalman observer is utilized for estimation of the longitudinal and lateral friction forces. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties and does not require knowledge of the road friction. The proposed method is experimentally tested in several maneuvers on different road surface conditions and the results illustrate the accuracy and robustness of the state estimators.Automotive Partnership Canada, Ontario Research Fund, General Motors Co

Distributed robust vehicle state estimation

© IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners’ velocity estimates, which are obtained by an unscented Kalman filter. The corners’ estimates from a Kalman observer, which is formed by integrating the model-based and kinematic-based velocity estimation approaches, are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Road tests confirm the robustness of the methods independent of the powertrain configuration on surfaces with various friction conditions in pure and combined-slip maneuvers, which are arduous for the current vehicle state estimators.Automotive Partnership Canada [APCPJ 395996-09] Ontario Research Fund [ORF-RE-04-039] General Motors Co