Browsing by Author "Ahmadi, Javad"

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    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.
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    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.