Developments in Estimation and Control for Cloud-Enabled Automotive Vehicles.

Cloud computing is revolutionizing access to distributed information and computing resources that can facilitate future data and computation intensive vehicular control functions and improve vehicle driving comfort and safety. This dissertation investigates several potential Vehicle-to-Cloud-to-Vehicle (V2C2V) applications that can enhance vehicle control and enable additional functionalities by integrating onboard and cloud resources. Firstly, this thesis demonstrates that onboard vehicle sensors can be used to sense road profiles and detect anomalies. This information can be shared with other vehicles and transportation authorities within a V2C2V framework. The response of hitting a pothole is characterized by a multi-phase dynamic model which is validated by comparing simulation results with a higher-fidelity commercial modeling package. A novel framework of simultaneous road profile estimation and anomaly detection is developed by combining a jump diffusion process (JDP)-based estimator and a multi-input observer. The performance of this scheme is evaluated in an experimental vehicle. In addition, a new clustering algorithm is developed to compress anomaly information by processing anomaly report streams. Secondly, a cloud-aided semi-active suspension control problem is studied demonstrating for the first time that road profile information and noise statistics from the cloud can be used to enhance suspension control. The problem of selecting an optimal damping mode from a finite set of damping modes is considered and the best mode is selected based on performance prediction on the cloud. Finally, a cloud-aided multi-metric route planner is investigated in which safety and comfort metrics augment traditional planning metrics such as time, distance, and fuel economy. The safety metric is developed by processing a comprehensive road and crash database while the comfort metric integrates road roughness and anomalies. These metrics and a planning algorithm can be implemented on the cloud to realize the multi-metric route planning. Real-world case studies are presented. The main contribution of this part of the dissertation is in demonstrating the feasibility and benefits of enhancing the existing route planning algorithms with safety and comfort metrics.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120710/1/zhaojli_1.pd

Sparse and Constrained Stochastic Predictive Control for Networked Systems

This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to be affected by additive stochastic noise. Our proposed class of policies is affine in the past dropouts and saturated values of the past disturbances. We further consider a regularization term in a quadratic performance index to promote sparsity in control. We demonstrate how to augment the underlying optimization problem with a constant negative drift constraint to ensure mean-square boundedness of the closed-loop states, yielding a convex quadratic program to be solved periodically online. The states of the closed-loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control and any non-zero probability of successful transmission

Stabilizing Stochastic Predictive Control under Bernoulli Dropouts

This article presents tractable and recursively feasible optimization-based controllers for stochastic linear systems with bounded controls. The stochastic noise in the plant is assumed to be additive, zero mean and fourth moment bounded, and the control values transmitted over an erasure channel. Three different transmission protocols are proposed having different requirements on the storage and computational facilities available at the actuator. We optimize a suitable stochastic cost function accounting for the effects of both the stochastic noise and the packet dropouts over affine saturated disturbance feedback policies. The proposed controllers ensure mean square boundedness of the states in closed-loop for all positive values of control bounds and any non-zero probability of successful transmission over a noisy control channel

State Estimation Filtering using Recent Finite Measurements and Inputs for Active Suspension System with Temporary Uncertainties

In this paper, the finite memory structure(FMS) filter using most recent finite measured outputs and control inputs is applied for the state estimation filtering of automotive suspension systems to verify intrinsic robustness of FMS filter. Firstly, the single-corner model for the automotive suspension system and its state-space model are described. Secondly, FMS as well as infinite memory structure(IMS) filters are briefly introduced and represented by the summation form. Thirdly, a couple of temporary uncertainties, model uncertainty and unknown input, are discussed. Finally, extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the IMS filter for both temporary uncertainties. In addition, the FMS filter can be shown to be comparable to the IMS filter after the effects of a couple of temporary uncertainties have completely disappeared

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

H∞ optimal control of vehicle active suspension systems in two time scales

In this paper, a novel H∞ control method in two time scales is proposed for active suspension systems. Two time scales are considered based on the natural time scale separation existing in the active suspension systems, i.e. the sprung mass part corresponding to the fast dynamics and the unsprung mass part corresponding to the slow dynamics. Singular perturbation theory is used to establish a dual time scale active suspension model and design a H∞ controller. Compared with the commonly used H∞ controller, the time-sharing dynamic characteristics make the proposed two time scales H∞ controller have better dynamic response when encountering dynamic road input, so as to better meet the control performance requirements of active suspension. The effectiveness of the proposed H∞ control method in two time scales is illustrated through co-simulations

A literature review on connected vehicle use cases

Abstract. Explosive growth of Internet of Things and cloud computing technologies has enabled the creation of more connected vehicles. As vehicles are becoming more connected the use cases of surrounding them are ambiguous. The goal of this thesis is to perform a literature review to see what use cases there are for connected vehicles, and if it is possible to categorize these use case scenarios to clear categories. The results of this thesis indicate that there are three main use case categories for connected vehicles. First category was vehicle state monitoring which was used to monitor vehicle’s diagnostic data, to monitor emission control tampering and to determine if the vehicle has had a collision. Second category were about vehicle management where connected vehicle technologies were used to deliver smart vehicle information services and to perform remote updating of a vehicle. Third category was vehicle state controlling, where use case scenarios of this category created an adaptive suspension control application and battery usage optimization application by utilizing cloud backend. Safe route assisting application also utilized cloud technologies. As well as remote control of a vehicle and digital twin based driving assist system. These use case categories could also be differentiated by their communication method between the vehicle and remote backend. There are three main communication methods; active communication where remote backend only sent data to vehicle, passive communication in which the remote backend was passive receiver of the vehicle data and hybrid two-way communication between vehicle and remote backend. Primary contribution of this thesis was that framework for defining different connected vehicle use case scenarios was established. Primary limitation of this thesis was that low amount of publications were considered for review, and for future research it is recommended to expand this topic to include more publications for review and to also consider what are the common characteristics in each use case scenario