Browsing by Author "Lin, Chenhui"

Now showing 1 - 8 of 8
  • Thumbnail Image
    ItemOpen Access
    Integrated Path-tracking and Control Allocation Controller for Autonomous Electric Vehicle under Limit Handling Condition
    (IEEE, 2021-01-08) Li, Boyuan; Ahmadi, Javad; Lin, Chenhui; Siampis, Efstathios; Longo, Stefano; Velenis, Efstathios
    In current literature, a number of studies have separately considered path-tracking (PT) control and control allocation (CA) method, but few of studies have integrated them together. This study proposes an integrated PT and CA method for autonomous electric vehicle with independent steering and driving actuators in the limit handling scenario. The high-level feedback PT controller can determine the desired total tire forces and yaw moment, and is designed to guarantee yaw angle error and lateral deviation converge to zero simultaneously. The low-level CA method is formulated as a compact quadratic programming (QP) optimization formulation to optimally allocate individual control actuator. This CA method is designed for a prototype experiment electric vehicle with particularly steering and driving actuator arrangement. The proposed integrated PT controller is validate through numerical simulation based on a high-fidelity CarMaker model on highspeed limit handling scenario.
  • Thumbnail Image
    ItemOpen Access
    An integrated path-tracking and control allocation method for autonomous racing electric vehicles
    (Taylor & Francis, 2023-08-08) Li, Boyuan; Lin, Chenhui; Ahmadi, Javad; Siampis, Efstathios; Longo, Stefano; Velenis, Efstathios
    In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance.
  • Thumbnail Image
    ItemOpen Access
    Multi-criteria evaluation for sorting motion planner alternatives
    (MDPI, 2022-07-11) Papaioannou, Georgios; Htike, Zaw; Lin, Chenhui; Siampis, Efstathios; Longo, Stefano; Velenis, Efstathios
    Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.
  • Thumbnail Image
    ItemOpen Access
    Path tracking control of a multi-actuated autonomous vehicle at the limits of handling.
    (Cranfield University, 2021-06) Lin, Chenhui; Velenis, Efstathios; Longo, Stefano; Siampis, Efstathios
    Over the past few decades, autonomous vehicles have been widely considered as the next generation of road transportation. As a result, relevant technology has been rapidly developed, and one specific topic is enabling autonomous vehicles to operate under demanding conditions. This requires the autonomous driving controller to have a good understanding of the vehicle dynamics at the limits of handling, and is expected to improve the performance as well as safety of autonomous vehicles especially in extreme situations. Furthermore, there has been application of techniques such as torque vectoring and four- wheel steering on modern vehicles as part of the driver assistance system, while such multi-actuation can be deployed on an autonomous vehicle in order to further enhance its performance in response to challenging manoeuvres and scenarios. This thesis aims to develop a real-time path tracking control strategy for an autonomous electric vehicle at the limits of handling, taking advantage of torque vectoring and four- wheel steering techniques for the enhanced control of vehicle dynamics. A nonlinear model predictive control formulation based on a three degree-of-freedom vehicle model is proposed for control design, which takes into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. In addition, steady-state references of steering inputs as well as vehicle states are generated based on a bicycle model and included in the control formulation to improve the performance. Two path tracking models with different coordinate systems are introduced to the control formulation, and compared to understand the more suitable one for the proposed path tracking purpose. Then the path tracking performance with different levels of actuation is investigated. According to the high-fidelity simulation results, the vehicle achieves the minimum lateral deviation with the over-actuation topology including both torque vectoring and four-wheel steering, which illustrates that the over-actuation formulation can enhance the path tracking performance by enduing the vehicle with the best flexibility as well as stability during operation at the limits of handling. Before being implemented on the vehicle, the performance of the proposed control strategy is further assessed with regards to real-time operation. After evaluating the control performance with different prediction horizons and sampling time, the most suitable setup is identified which compromises between the control performance and the capability of real-time execution. Finally, the control algorithm is implemented on a real vehicle for practical testing. The controller is tested in four different scenarios, and the results demonstrate that the proposed controller is capable of path tracking control and vehicle stabilisation for multi-actuated autonomous vehicles at the limits of handling. In general, this thesis has proposed a path tracking controller for autonomous vehicles which takes into account nonlinear vehicle dynamics at the limits of handling. Following some necessary simplification, the developed controller has been successfully deployed on a real vehicle in real time, and the control performance has been validated in several challenging scenarios. The controller proves itself to be able to improve the vehicle’s flexibility as well as to stabilise the vehicle at the limits of handling, and furthermore, it is able to accommodate relatively large side slip angles during the demanding manoeuvres as well.
  • Thumbnail Image
    ItemOpen Access
    Path-tracking control at the limits of handling of a prototype over-actuated autonomous vehicle
    (Taylor & Francis, 2024-05-31) Lin, Chenhui; Siampis, Efstathios; Velenis, Efstathios
    Considering the vehicle dynamics at the limits of handling is vital to improve the performance and safety of autonomous vehicles especially in extreme situations. This paper presents the development of a path-tracking controller for an over-actuated autonomous vehicle. The vehicle is an electric prototype equipped with torque vectoring and four-wheel steering, which enable enhanced control of vehicle dynamics. A model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. The controller is examined in both high-fidelity simulation and practical testing to validate the vehicle's handling performance. Both the simulation and testing results illustrate that the over-actuation topology can enhance the handling performance as well as vehicle stability at conditions close to the limits of handling. With additional references such as side slip angle, the vehicle's attitude under such extreme condition can also be manipulated. The testing also demonstrates the real-time capability of the controller. Further testing has been done to confirm that side slip angle reference plays an important role in path-tracking control at the limits of handling, and to push the vehicle to the friction limits.
  • Thumbnail Image
    ItemOpen Access
    Path-tracking control strategy for enhanced comfort in all-wheel-steering autonomous vehicles
    (Springer, 2024-10-13) Lin, Chenhui; Papaioannou, Georgios; Siampis, Efstathios; Velenis, Efstathios
    In this paper, a path-tracking controller is developed for an autonomous vehicle with All-Wheel-Steering (AWS) capability. Based on nonlinear model predictive control, the proposed controller is formulated in a way that allows the manipulation of vehicle’s attitude during path-tracking. With high-fidelity vehicle dynamics simulation, the controller is examined at various velocities up to the limit handling condition. Comparison is carried out in the aspects of path-tracking, ride comfort and motion sickness, between the implementation with a constant yaw angle reference (referred to as crab steering) and the nominal steering behaviour for negotiating the same path. The ride comfort metric suggested by ISO-2631 is used to capture the benefits of the crab steering approach against the nominal case, and the simulation results reveal that crab steering is able to enhance the ride comfort for AWS vehicles in double lane-change and slalom manoeuvres.
  • Thumbnail Image
    ItemOpen Access
    Predictive path-tracking control of an autonomous electric vehicle with various multi-actuation topologies
    (MDPI, 2024-02-28) Lin, Chenhui; Li, Boyuan; Siampis, Efstathios; Longo, Stefano; Velenis, Efstathios
    This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time.
  • Thumbnail Image
    ItemOpen Access
    Real-time path-tracking MPC for an over-actuated autonomous electric vehicle
    (IEEE, 2022-09-05) Lin, Chenhui; Siampis, Efstathios; Velenis, Efstathios
    This paper illustrates the development of a nonlinear constrained predictive path-tracking controller, including realistic vehicle dynamics and multiple actuator inputs and its implementation in real time on an experimental vehicle platform. The controller is formulated for a particular over-actuated vehicle equipped with Torque Vectoring (TV) as well as All-Wheel-Steering (AWS) functionalities, which allow for the enhanced control of vehicle dynamics. The proposed Nonlinear Model Predictive Controller (NMPC) takes into account the nonlinearities in vehicle dynamics across the range of operation up to the limits of handling as dictated by the adhesion limits of the tyres. In addition, crucial constraints regarding the actuators’ physical limits are included in the formulation. The performance of the controller is demonstrated in a high fidelity simulation environment, as well as in real-time on a test vehicle, during the execution of demanding driving scenarios.