Saber Fallah

Vehicle Optimal Torque Vectoring Using State-Derivative Feedback and Linear Matrix Inequality

A controller assistant system is developed based on the closed-form solution of an offline optimization problem for a four-wheel-drive front-wheel-steerable vehicle. The objective of the controller is to adjust the actual vehicle attitude and motion according to the drivers manipulating commands. The controller takes feedback from acceleration signals, and the imposed conditions and limitations on the controller are studied through the concept of state-derivative feedback control systems. The controller gains are optimized using linear matrix inequality (LMI) and genetic algorithm (GA) techniques. Reference signals are calculated using a driver command interpreter module (DCIM) to accurately interpret the drivers intentions for vehicle motion and to allow the controller to generate proper control actions. It is shown that the controller effectively enhances the handling performance and stability of the vehicle under different road conditions and driving scenarios. Although controller performance is studied for a four-wheel-drive front-wheel-steerable vehicle, the algorithm can also be applied to other vehicle configurations with slight changes.

A Fast and Parametric Torque Distribution Strategy for Four-Wheel-Drive Energy-Efficient Electric Vehicles

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.

Novel Tire Force Estimation Strategy for Real-Time Implementation on Vehicle Applications

This paper proposes a novel unified structure to estimate tire forces. The proposed structure uses estimation modules to calculate/estimate tire forces by means of nonlinear observers. The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions. In the proposed structure, we have a dedicated module to estimate the longitudinal tire forces and another to calculate the vertical tire forces. Subsequently, these forces are fed into a third module that utilizes a nonlinear observer to estimate lateral tire forces. The proposed structure is validated through experimental studies.

Energy Management of Planetary Rovers Using a Fast Feature-Based Path Planning and Hardware-in-the-Loop Experiments

This paper presents a novel feature-based technique for path optimization problems, in which the performance index is defined to minimize the energy consumption of a rover with consideration of terrain, kinematic, and dynamic constraints. The proposed method estimates rover energy consumption by discretizing a path and by extracting statistical data for fast calculation of the performance index. The concepts of grouped data and data discretization techniques are used to analyze the energy-related data obtained from the search environment. The method improves runtime computation by statistically calculating the energy consumption of a rover for a defined path, rather than solving the dynamic equations of the rover. This technique is computationally more efficient than other energy optimization approaches when it estimates rover energy consumption with sufficient accuracy. The Genetic Algorithm (GA) solver is integrated to the approach to illustrate the efficiency of the algorithm. Additionally, a hardware-in-the-loop (HIL) simulation is developed for the validation of the rovers power flow as it traverses through the optimal path by incorporating rover hardware components within real-time simulation.

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

For an autonomous vehicle to operate safely and effectively, an accurate and robust localization system is essential. While there are a variety of vehicle localization techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g., cornering and intersections) and different environmental conditions (e.g., darkness and snow). This paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localization systems with high accuracy and robustness, however, their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

Abstract Trajectory planning and trajectory tracking constitute two important functions of an autonomous overtaking system and a variety of strategies have been proposed in the literature for both functionalities. However, uncertainties in environment perception using the current generation of sensors has resulted in most proposed methods being applicable only during low-speed overtaking. In this paper, trajectory planning and trajectory tracking approaches for autonomous overtaking systems are reviewed. The trajectory planning techniques are compared based on aspects such as real-time implementation, computational requirements, and feasibility in real-world scenarios. This review shows that two important aspects of trajectory planning for high-speed overtaking are: (i) inclusion of vehicle dynamics and environmental constraints and (ii) accurate knowledge of the environment and surrounding obstacles. The review of trajectory tracking controllers for high-speed driving is based on different categories of control algorithms where their respective advantages and disadvantages are analysed. This study shows that while advanced control methods improve tracking performance, in most cases the results are valid only within well-regulated conditions. Therefore, existing autonomous overtaking solutions assume precise knowledge of surrounding environment which is not representative of real-world driving. The paper also discusses how in a connected driving environment, vehicles can access additional information that can expand their perception. Hence, the potential of cooperative information sharing for aiding autonomous high-speed overtaking manoeuvre is identified as a possible solution.

Torque Distribution Strategies for Energy-Efficient Electric Vehicles with Multiple Drivetrains

The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to driving/braking and cornering are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of an analytically derived algorithm are contrasted with those from two other control allocation strategies, based on the offline numerical solution of more detailed formulations of the control allocation problem (i.e., a multiparametric nonlinear programming (mp-NLP) problem). The control allocation algorithms are experimentally validated with an electric vehicle with four identical drivetrains along multiple driving cycles and in steady-state cornering. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity.

Towards connected autonomous driving: review of use-cases

ABSTRACT Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption and pollutant emissions that current road transportation system suffers from. Connected autonomous vehicles utilise communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute to improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i) vehicle platooning, (ii) lane changing, (iii) intersection management, (iv) energy management and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to the improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, traffic scenarios and the way of augmenting off-board information into vehicle control systems.

Explicit model predictive control of an active suspension system

Model predictive control (MPC) is increasingly finding its way into industrial applications, due to its superior tracking performance and ability to formally handle system constraints. However, the real-time capability problems related to the conventional implicit model predictive control (i-MPC) framework are well known, especially when targeting low-cost electronic control units (ECUs) for high bandwidth systems, such as automotive active suspensions, which are the topic of this paper. In this context, to overcome the real-time implementation issues of i-MPC, this study proposes explicit model predictive control (e-MPC), which solves the optimization problem off-line, via multi-parametric quadratic programming (mp-QP). e-MPC reduces the on-line algorithm to a function evaluation, which replaces the computationally demanding on-line solution of the quadratic programming (QP) problem. An e-MPC based suspension controller is designed and experimentally validated for a case study Sport Utility Vehicle (SUV), equipped with the active ACOCAR suspension system from the Tenneco Monroe product family. The target is to improve ride comfort in the frequency range of primary ride ( 40% compared to the passive vehicle set-up for frequencies < 4 Hz, and by up to 19% compared to the same vehicle with a skyhook controller on the 0-100 Hz frequency range.

A Novel Robust Optimal Active Control of Vehicle Suspension Systems

Abstract Using Lyapunov theory, Pontryagins minimum principle, and affine quadratic stability, a novel robust optimal control strategy is developed for active suspension systems to enhance vehicle ride comfort and handling performance. The controller has a simple structure, making its suitable for real-time implementation. The required sensor configuration includes a six-axis IMU and four LVDTs. The proposed controller is suitable for on-road commercial vehicles where ride comfort over bump disturbances and handling performance are the most concerns. The effectiveness of the controller is verified through simulation results using IPG CarMaker software.