MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations

Adaptive and Robust Braking-Traction Control Systems

The designs of commercial Anti-Lock Braking Systems often rely on assumptions of a torsionally rigid tire-wheel system and heavily rely on hub-mounted wheel speed sensors to manage tire-road slip conditions. However, advancements in high-bandwidth braking systems, in-wheel motors, variations in tire/wheel designs, and loss of inflation pressure, have produced scenarios where the tire\u27s torsional dynamics could be easily excited by the braking system actuator. In these scenarios, the slip conditions for the tire-belt/ring will be dynamically different from what can be inferred from the wheel speed sensors. This dissertation investigates the interaction of tire torsional dynamics with ABS & traction controllers and offers new control designs that incorporate schemes for identifying and accommodating these dynamics. To this end, suitable braking system and tire torsional dynamics simulation models as well as experimental test rigs were developed. It is found that, indeed, rigid-wheel based controllers give degraded performance when coupled with low torsional stiffness tires. A closed-loop observer/nonlinear controller structure is proposed that adapts to unknown tire sidewall and tread parameters during braking events. It also provides estimates of difficult to measure state variables such as belt/ring speed. The controller includes a novel virtual damper emulation that can be used to tune the system response. An adaptive sliding-mode controller is also introduced that combines robust stability characteristics with tire/tread parameter and state estimation. The sliding mode controller is shown to be very effective at tracking its estimated target, at the expense of reducing the tire parameter adaptation performance. Finally, a modular robust state observer is developed that allows for robust estimation of the system states in the presence of uncertainties and external disturbances without the need for sidewall parameter adaptation

State and parameter estimator design for control of vehicle suspension system

Modern vehicle stability and navigational systems are mostly designed using inaccurate bicycle models to approximate the full-car models. This results in incomplete models with various unknown parameters and states being neglected in the controller and navigation system design processes. Earlier estimation algorithms using the bicycle models are simpler but have many undefined parameters and states that are crucial for proper stability control. For existing vehicle navigation systems, direct line of sight for satellite access is required but is limited in modern cities with many high-rise buildings and therefore, an inertial navigation system utilizing accurate estimation of these parameters is needed. The aim of this research is to estimate the parameters and states of the vehicle more accurately using a multivariable and complex full-car model. This will enhance the stability of the vehicle and can provide a more consistent navigation. The proposed method uses the kinematics estimation model formulated using special orthogonal SO3 group to design estimators for vehicles velocity, attitude and suspension states. These estimators are used to modify the existing antilock braking system (ABS) scheme by incorporating the dynamic velocity estimation to reduce the stopping distance. Meanwhile the semi-active suspension system includes suspension velocity and displacement states to reduce the suspension displacements and velocities. They are also used in the direct yaw control (DYC) scheme to include mass and attitude changes to reduce the lateral velocity and slips. Meanwhile in the navigation system, the 3-dimensional attitude effects can improve the position accuracy. With these approaches, the stopping distance in the ABS has been reduced by one meter and the vehicle states required for inertial navigation are more accurately estimated. The results for high speed lane change test indicate that the vehicle is 34% more stable and 16% better ride comfort on rough terrains due to the proposed DYC and the active suspension system control. The methods proposed can be utilized in future autonomous car design. This research is therefore an important contribution in shaping the future of vehicle driving, comfort and stability

Active suspension control of electric vehicle with in-wheel motors

In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance

Research on Advanced Control Strategies for Vehicle Active Seat Suspension Systems

Vehicle seat suspensions play a very important role in vibration reduction for vehicle drivers, especially for some heavy vehicles. Compared with small vehicles, these heavy vehicle drivers suffer much more from vibrations, which influence driving comfort and may cause health problems, so seat suspensions are necessary for those heavy vehicle drivers to reduce vibrations and improve driving comfort. Advanced control systems and control strategies are investigated for vehicle seat suspensions in this project. Firstly, for an active single-degree of freedom (single-DOF) seat suspension, a singular system-based approach for active vibration control of vehicle seat suspensions is proposed, where the drivers’ acceleration is augmented into the conventional seat suspension model together with seat suspension deflection and relative velocity as system states to make the suspen- sion model as a singular system. Then, an event-triggered H∞ controller is designed for an active seat suspension, where both the continuous and discrete-time event-triggered schemes are considered, respectively. The proposed control method can reduce the work- load of data transmission of the seat suspension system and work as a filter to remove the effect of noise, so it can decrease the precision requirement of the actuator, which can help to reduce the cost of the seat suspension. For complicated seat suspension systems, a singular active seat suspension system with a human body model is also established and an output-feedback event-triggered H∞ controller is designed. The accelerations of each part are considered as part of the system states, which makes the system a singular sys- tem. The seat suspension deflection, relative velocity, the accelerations of the seat frame, body torso, and head are defined as the system outputs. At last, to deal with whole-body vibration, a control system and a robust H∞ control strategy are designed for a 2-DOF seat suspension system. Two H∞ controllers are designed to reduce vertical and rotational vibrations simultaneously. All the proposed seat suspension systems and control methods are verified by simulations and some are also tested by experiments. These simulation and experimental results show their effectiveness and advantages of the proposed methods to improve the driving comfort and some can reduce the workload of data transmission

Linear Parameter Varying Approaches as Advanced Control Techniques: Application to Vehicle Dynamics

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Ce travail de Fin-d’études présente plusieurs techniques de modélisation, identification et de la commande avancée appliqués a l’étude des systèmes de suspensions semi-actifs. Ce travail est divisé en trois domaines principaux: développement et l’application des techniques LPV pour l’identification des défauts sur les actionneurs dans les systèmes de suspension; développement et mise-en-œuvre d’un système de contrôle prédictif basé sur modèle appliqué en temps réel sur des suspensions semi-actifs; développement des techniques LPV de reconfiguration pour la commande tolerant aux défauts des systèmes de suspension. Les stratégies de commande développées sont analysées par simulation et validation et se montrent satisfaisantes.This End-of-Studies Work presents a range of techniques of Modeling, Identification and Advanced Control applied to the study of Semi-Active Suspensions in Vehicular Systems. This work is divided into three main branches: i) development and application of LPV fault identification techniques on actuators of suspension systems; ii) development and implementation of a real-time model predictive control scheme applied the control of semi- active suspensions; iii) development and application of LPV reconfiguration techniques for fault tolerant control of suspension system. The developed control strategies are analysed through simulation and validation on a mechatronic test-bench and prove themselves satisfactory.Este Trabalho de Conclusão de Curso apresenta diversas técnicas de Modelagem, Identifi- cação e Controle Avançado aplicadas ao estudo de Suspensões Semi-Ativas em Sistemas Veiculares. Este trabalho é divido em três eixos principais: i) Desenvolvimento e aplicação de técnicas LPV de Identificação de Falhas em amortecedores de sistemas de suspensão; ii) Desenvolvimento e implementação de um sistema de Controle Preditivo baseado em modelo aplicado em tempo-real para o controle de suspensões semi-ativas; iii) Desenvolvimento e aplicação de técnicas de reconfiguração LPV para o Controle Tolerante a Falhas de sistemas de suspensão. As técnicas e o desenvolvimento feito são analisados através de simulação e validação em uma plataforma mecatrônica experimental e demonstram-se satisfatórios

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation

Steering control for haptic feedback and active safety functions

Steering feedback is an important element that defines driver–vehicle interaction. It strongly affects driving performance and is primarily dependent on the steering actuator\u27s control strategy. Typically, the control method is open loop, that is without any reference tracking; and its drawbacks are hardware dependent steering feedback response and attenuated driver–environment transparency. This thesis investigates a closed-loop control method for electric power assisted steering and steer-by-wire systems. The advantages of this method, compared to open loop, are better hardware impedance compensation, system independent response, explicit transparency control and direct interface to active safety functions.The closed-loop architecture, outlined in this thesis, includes a reference model, a feedback controller and a disturbance observer. The feedback controller forms the inner loop and it ensures: reference tracking, hardware impedance compensation and robustness against the coupling uncertainties. Two different causalities are studied: torque and position control. The two are objectively compared from the perspective of (uncoupled and coupled) stability, tracking performance, robustness, and transparency.The reference model forms the outer loop and defines a torque or position reference variable, depending on the causality. Different haptic feedback functions are implemented to control the following parameters: inertia, damping, Coulomb friction and transparency. Transparency control in this application is particularly novel, which is sequentially achieved. For non-transparent steering feedback, an environment model is developed such that the reference variable is a function of virtual dynamics. Consequently, the driver–steering interaction is independent from the actual environment. Whereas, for the driver–environment transparency, the environment interaction is estimated using an observer; and then the estimated signal is fed back to the reference model. Furthermore, an optimization-based transparency algorithm is proposed. This renders the closed-loop system transparent in case of environmental uncertainty, even if the initial condition is non-transparent.The steering related active safety functions can be directly realized using the closed-loop steering feedback controller. This implies, but is not limited to, an angle overlay from the vehicle motion control functions and a torque overlay from the haptic support functions.Throughout the thesis, both experimental and the theoretical findings are corroborated. This includes a real-time implementation of the torque and position control strategies. In general, it can be concluded that position control lacks performance and robustness due to high and/or varying system inertia. Though the problem is somewhat mitigated by a robust H-infinity controller, the high frequency haptic performance remains compromised. Whereas, the required objectives are simultaneously achieved using a torque controller