Development of an Electronic Stability Control for Improved Vehicle Handling using Co-Simulation

The research project focuses on integrating the algorithms of recent automotive Electronic Stability Control (ESC) technologies into a commercial multi-body dynamics (MBD) software for full vehicle simulations. Among various control strategies for ESC, the sliding mode control (SMC) method is proposed to develop these algorithms, as it is proven to be excellent at overcoming the effect of uncertainties and disturbances. The ESC model integrates active front steering (AFS) system and direct yaw moment control (DYC) system, using differential braking system, therefore the type of the ESC model is called as integrated vehicle dynamic control (IVDC) system. The IVDC virtual model will be designed using a specialized control system software, called Simulink. The controller model will be used to perform full vehicle simulations, such as sine with dwell (SwD) and double lane change (DLC) tests on Simulink to observe its functionality in stabilizing vehicles. The virtual nonlinear full vehicle model in CarSim will be equipped with the IVDC virtual model to ensure that the proposed IVDC virtual model passes the regulations that describes the ESC homologation process for North America and European countries, each defined by National Highway Traffic Safety Administration (NHTSA) and United Nations (UN). The proposed research project will enable automotive engineers and researchers to perform full vehicle virtual simulations with ESC capabilities

Decoupled Fractional Super-Twisting Stabilization of Interconnected Mobile Robot Under Harsh Terrain Conditions

The four-wheel omnidirectional mobile robot usually suffers disturbed or unstable lateral motion under harsh terrain conditions (such as uneven or oiled ground). Generally for such a challenging situation, the lumped disturbances and interconnected states render available coupling solutions difficult to achieve demand-satisfied performance. This paper proposes a novel decoupled fractional super-twisting sliding mode control (FST-SMC) method by (i) constructing an inverse system-based decoupling to form a pseudolinear composition system; (ii) presenting an enhanced nominal sliding law for chattering mitigation and (iii) designing an unbiased multi-layer fuzzy estimator with gain-learning capacity to compensate for the lumped disturbances actively. Given that the identified disturbances can be directly reflected in the FST-SMC law, this method guarantees an accurate and robust control without causing gain overestimation. Theoretical analysis is offered to verify the asymptotic stability. Under harsh terrain conditions, experimental results validate the effectiveness of the proposed FST-SMC method

Development and Evaluation of Integrated Chassis Control Systems.

Integrated chassis control (ICC) systems can be used to reduce the economic and social costs of road accidents. If these systems are to achieve their full potential for improved safety, however, two critical issues must be resolved: (i) the design of a controller integrating all sub-control systems, and (ii) rigorous evaluation to ensure their functionalities. A decentralized design that coordinates the commands from sub-chassis control systems is achieved under the current business practice, in which suppliers provide OEMs with proprietary controllers. For effective coordination of sub-control commands and for avoidance of liability, the coordination strategy of saturating sub-control commands is used. A coordinator based on a hybrid approach--an offline model predictive control and an online fixed-point control allocation method--is designed, which has superior computational efficiency and flexibility. The effectiveness of the decentralized ICC system is verified via commercial software, CarSim. The simulation results show that ICC can resolve conflicts among subsystems and achieve improved stability. Reconfiguration in the control, for dealing with actuator failure in sub-control systems and robust control under uncertainties is presented. For the evaluation of ICC, the worst-case scenario evaluation (WCSE) method is enhanced and applied to find the worst possible scenarios, for rigorous evaluation of vehicles, especially vehicles with chassis control systems. Two optimization methods (Sequential Quadratic Programming and Mesh Adaptive Direct Search) are used because of their convergence and computation efficiency. The worst allowable persistent bounded disturbance input generation method is applied to populate the initial points for the optimization problem. The effectiveness of the proposed WCSE method was shown through a rollover prevention case study.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75945/1/ykou_1.pd

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

AAS/GSFC 13th International Symposium on Space Flight Dynamics

This conference proceedings preprint includes papers and abstracts presented at the 13th International Symposium on Space Flight Dynamics. Cosponsored by American Astronautical Society and the Guidance, Navigation and Control Center of the Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude dynamics; and mission design

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Vehicle Stability Control Considering the Driver-in-the-Loop

A driver‐in‐the‐loop modeling framework is essential for a full analysis of vehicle stability systems. In theory, knowing the vehicle’s desired path (driver’s intention), the problem is reduced to a standard control system in which one can use different methods to produce a (sub) optimal solution. In practice, however, estimation of a driver’s desired path is a challenging – if not impossible – task. In this thesis, a new formulation of the problem that integrates the driver and the vehicle model is proposed to improve vehicle performance without using additional information from the future intention of the driver. The driver’s handling technique is modeled as a general function of the road preview information as well as the dynamic states of the vehicle. In order to cover a variety of driving styles, the time‐ varying cumulative driver's delay and model uncertainties are included in the formulation. Given that for practical implementations, the driver’s future road preview data is not accessible, this information is modeled as bounded uncertainties. Subsequently, a state feedback controller is designed to counteract the negative effects of a driver’s lag while makes the system robust to modeling and process uncertainties. The vehicle’s performance is improved by redesigning the controller to consider a parameter varying model of the driver‐vehicle system. An LPV controller robust to unknown time‐varying delay is designed and the disturbance attenuation of the closed loop system is estimated. An approach is constructed to identify the time‐varying parameters of the driver model using past driving information. The obtained gains are clustered into several modes and the transition probability of switching between different driving‐styles (modes) is calculated. Based on this analysis, the driver‐vehicle system is modeled as a Markovian jump dynamical system. Moreover, a complementary analysis is performed on the convergence properties of the mode‐dependent controller and a tighter estimation for the maximum level of disturbance rejection of the LPV controller is obtained. In addition, the effect of a driver’s skills in controlling the vehicle while the tires are saturated is analyzed. A guideline for analysis of the nonlinear system performance with consideration to the driver’s skills is suggested. Nonlinear controller design techniques are employed to attenuate the undesirable effects of both model uncertainties and tire saturation

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Fault tolerant control for nonlinear aircraft based on feedback linearization

The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance