Fault-tolerant control of electric vehicles with in-wheel motors using actuator-grouping sliding mode controllers

Although electric vehicles with in-wheel motors have been regarded as one of the promising vehicle architectures in recent years, the probability of in-wheel motor fault is still a crucial issue due to the system complexity and large number of control actuators. In this study, a modified sliding mode control (SMC) is applied to achieve fault-tolerant control of electric vehicles with four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID). Unlike in traditional SMC, in this approach the steering geometry is re-arranged according to the location of faulty wheels in the modified SMC. Three SMC control laws for longitudinal velocity control, lateral velocity control and yaw rate control are designed based on specific vehicle motion scenarios. In addition the actuator-grouping SMC method is proposed so that driving actuators are grouped and each group of actuators can be used to achieve the specific control target, which avoids the strong coupling effect between each control target. Simulation results prove that the proposed modified SMC can achieve good vehicle dynamics control performance in normal driving and large steering angle turning scenarios. In addition, the proposed actuator-grouping SMC can solve the coupling effect of different control targets and the control performance is improved

Development of a Distributed Model Predictive Controller for Over-Actuated Autonomous Vehicle Path Tracking

Widespread interest in the advancement of autonomous vehicle technology is motivated by multiple outstanding issues associated with vehicular travel despite the decades-long ubiquity of this mode of transportation. It is well known that the leading cause of accidents on the road is human error. Furthermore, vehicle hardware faults and harsh environmental circumstances are also common collision factors due to the challenges that they introduce to the driving task. Autonomous vehicles have the potential to greatly exceed the perception, decision making, and control capabilities of human drivers in some applications, and the large-scale adoption of this technology will thereby mitigate the primary driving-related safety concerns. Numerous additional benefits will be realized as a result; for instance, complex planning algorithms will help to reduce traffic congestion, and transportation- and insurance-related costs will be minimized due to the lower collision rates. Though it may be many years before the technology sees extensive use for passenger transportation applications due to the complexity of standard driving environments, autonomous vehicles will likely find use over the short-term in other specialized domains. For example, these vehicles can be used to transport payloads over short distances in a wide variety of applications, including agriculture, mining, and shipping, where the operating environment is less complex. In these scenarios, autonomous vehicle technology will help to lessen the effects of labour shortages while enabling longer operating hours at a lower cost. A key component of the autonomous stack is the motion controller, which serves to regulate the longitudinal and lateral motion of the vehicle according to a defined set of objectives by precisely manipulating the available actuators. Model predictive control (MPC) is a powerful control strategy commonly used for this purpose; the algorithm can coordinate a large set of control inputs such that the system meets all defined objectives while satisfying any constraints on the states and inputs. Many prior works investigate the use of MPC, and its variants, for vehicle path tracking and stability control applications. One such variant is distributed MPC; with this approach, the controlled plant is modelled as a set of interacting subsystems, each subsystem using its own MPC controller to select a set of optimal control actions in combination with all others. An extension of distributed MPC, agent-based MPC (AMPC), enhances the control capabilities by allowing the controller to additionally consider both the effect of subsystems that are not controllable by the optimal controller and the effects of hardware faults on the system dynamics. While previous works have investigated the application of AMPC to vehicle stability control tasks, in this thesis, AMPC is utilized to perform path tracking. The vehicle hardware platform considered in this work, WATonoTruck, is modular and over-actuated in design, making it a suitable test platform for AMPC. Built using the corner module platform, the wheels at each corner can be independently driven and steered. A vehicle dynamics reference model to represent the behaviour of WATonoTruck is constructed; this model utilizes a nonlinear tire force model to accurately characterize the tire-road interaction, and incorporates Ackermann geometry to prevent unecessary wheel slip and reduce the control task complexity that results from the over-actuated nature of the system. This model serves as the prediction model for the designed AMPC controller. The controller also considers numerous constraints on the vehicle states, inputs, and input rates to ensure stability, and can incorporate an external longitudinal controller and account for actuator faults. The controller is validated over several simulated and experimental tests that demonstrate its ability to provide effective path tracking and velocity control performance in a varied set of scenarios, including those where actuator failures occur or the driving environment is harsh

Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

This paper presents the kinematic analysis of the SCORBOT-ER 4u robot arm using a Multi-Layered Feed-Forward (MLFF) Neural Network. The SCORBOT-ER 4u is a 5-DOF vertical articulated educational robot with revolute joints. The Denavit-Hartenberg and Geometrical methods are the forward kinematic algorithms used to generate data and train the neural network. The learning of forward-inverse mapping enables the inverse kinematic solution to be found. The algorithm is tested on hardware (SCORBOT-ER 4u) and reliable results are obtained. The modeling and simulations are done using MATLAB 8.0 software

New trends in electrical vehicle powertrains

The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft

Advanced Sensing and Control for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

Holistic Vehicle Control Using Learning MPC

In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

Casco Bay Weekly : 19 October 1995

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