Browsing by Author "Siampis, Efstathios"

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    Fundamentals of motion planning for mitigating motion sickness in automated vehicles
    (IEEE, 2021-12-28) Htike, Zaw; Papaioannou, Georgios; Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluatemotion sickness. The tradeo between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the eect of driving style, vehicle speed, and road width, which can be considered among the main factors aecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The eect of the road width is negligible on both sickness and journey time when travelling on a longer road.The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper.
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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.
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    Integration of torque blending and slip control using nonlinear model predictive control
    (Unknown, 2016-09-30) Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
    Antilock Braking System (ABS) is an important active safety feature in preventing accidents during emergency braking. Electrified vehicles which include both hydraulic and regenerative braking systems provide the opportunity to implement brake torque blending during slip control operation. This study evaluates the design and implementation of a new torque allocation algorithm using a Nonlinear Model Predictive Control (NMPC) strategy that can run in real-time, with results showing that wheel-locking can be prevented while also permitting for energy recuperation.
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    Model Predictive torque vectoring control for electric vehicles near the limits of handling
    (Institute of Electrical and Electronics Engineers, 2015-11-16) Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    In this paper we propose a constrained optimal control architecture to stabilize a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model is employed to find reference steady-state cornering conditions as well as to design a linear Model Predictive Control (MPC) strategy using the rear wheels' slip ratios as input. A Sliding Mode Slip Controller then calculates the necessary motor torques according to the requested wheel slip ratios. After analysing the relative trade-offs between performance and computational effort for the MPC strategy, we validate the controller and compare it against a simpler unconstrained optimal control strategy in a high fidelity simulation environment.
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    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.
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    Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling
    (Cranfield University, 2016-10) Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicle’s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a ‘terminal understeer’ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit.
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    Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling
    (Cranfield University, 2016-10) Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicle’s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a ‘terminal understeer’ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit.
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    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.
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    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.
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    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.
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    A real-time nonlinear model predictive control strategy for stabilisation of an electric vehicle at the limits of handling
    (IEEE, 2018-10-09) Siampis, Efstathios; Velenis, Efstathios; Gariuolo, Salvatore; Longo, Stefano
    In this paper, we propose a real-time nonlinear model predictive control (NMPC) strategy for stabilization of a vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear four-wheel vehicle model that neglects the wheel dynamics is coupled with a nonlinear tire model to design three MPC strategies of different levels of complexity that are implementable online: one that uses a linearized version of the vehicle model and then solves the resulting quadratic program problem to compute the necessary longitudinal slips on the rear wheels, a second one that employs the real-time iteration scheme on the NMPC problem, and a third one that applies the primal dual interior point method on the NMPC problem instead until convergence. Then, a sliding mode slip controller is used to compute the necessary torques on the rear wheels based on the requested longitudinal slips. After analyzing the relative tradeoffs in performance and computational cost between the three MPC strategies by comparing them against the optimal solution in a series of simulation studies, we test the most promising solution in a high-fidelity environment.
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    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.
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    Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles
    (Taylor and Francis, 2015-07-20) Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.
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    Simultaneous optimisation of vehicle design and control for improving vehicle performance and energy efficiency using an open source minimum lap time simulation framework
    (MDPI AG, 2024-08-13) Jiménez Elbal, Alberto; Zarzuelo Conde, Adrián; Siampis, Efstathios
    This paper presents a comprehensive framework for optimising vehicle performance, integrating advanced simulation techniques with optimisation methodologies. The aim is to find the best racing line, as well as the optimal combination of parameters and control inputs to make a car as fast as possible around a given track, with a focus on energy deployment and recovery, active torque distribution and active aerodynamics. The problem known as the Minimum Lap Time Problem is solved using optimal control methods and direct collocation. The solution covers the modelling of the track, vehicle dynamics, active aerodynamics, and a comprehensive representation of the powertrain including motor, engine, transmission, and drivetrain components. This integrated simulator allows for the exploration of different vehicle configurations and track layouts, providing insights into optimising vehicle design and vehicle control simultaneously for improved performance and energy efficiency. Test results demonstrate the effect of active torque distribution on performance under various conditions, enhanced energy efficiency and performance through regenerative braking, and the added value of including parameter optimisation within the optimisation framework. Notably, the simulations revealed interesting behaviours similar to lift-and-coast strategies, depending on the importance of energy saving, thereby highlighting the effectiveness of the proposed control strategies. Also, results demonstrate the positive effect of active torque distribution on performance under various conditions, attributed to the higher utilization of available adherence. Furthermore, unlike a simpler single-track model, the optimal solution required fine control of the active aerodynamic systems, reflecting the complex interactions between different subsystems that the simulation can capture. Finally, the inclusion of parameter optimisation while considering all active systems, further improves performance and provides valuable insights into the impact of design choices.
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    A torque vectoring optimal control strategy for combined vehicle dynamics performance enhancement and electric motor ageing minimisation*
    (Elsevier, 2016-08-21) Kampanakis, Angelos; Siampis, Efstathios; Velenis, Efstathios; Longo, Stefano
    In this paper we propose a control architecture that combines velocity, sideslip angle and yaw rate regulation with motor temperature regulation on a electric vehicle with four independent electric motors. The linear controller incorporates both the vehicle dynamics and the electric motor dynamics by combining a four-wheel vehicle model with a motor degradation model. It is found that the resulting controller not only enhances the vehicle stability of the vehicle, but also extends the lifetime of motors by regulating their temperatures.
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    Wheel slip control with torque blending using linear and nonlinear model predictive control
    (Taylor & Francis, 2017-03-31) Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
    Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.