1,720,977 research outputs found
Special Issue on "recent Developments on Modeling and Control of Hybrid Electric Vehicles"
No full textRobust Composite Nonlinear Feedback Path-Following Control for Independently Actuated Autonomous Vehicles With Differential Steering
No full textRack-based Data Center Temperature Regulation Using Data-driven Model Predictive Control
Get PDFDue to the rapid and prosperous development of information technology, data centers are widely used in every aspect of social life, such as industry, economy or even our daily life. This work considers the idea of developing a data-driven model based model predictive control (MPC) to regulate temperature for a class of single-rack data centers (DCs). An auto-regressive exogenous (ARX) model is identified for our DC system using partial least square (PLS) to predict the behavior of multi-inputs-single-output (MISO) thermal system. Then an MPC controller is designed to control the temperature inside IT rack based on the identified ARX model. Moreover, fuzzy c-means (FCM) is employed to cluster the measured data set. Based on the clustered data sets, PLS is adopted to identify multiple locally linear ARX models which will be combined by appropriate weights in order to capture the nonlinear behavior of the highly-nonlinear thermal system inside the IT rack. The effectiveness of the proposed method is illustrated through experiments on our single-rack DC and it is also compared with proportional-integral (PI) control.ThesisMaster of Applied Science (MASc
Active Disturbance Estimation and Compensation for Improving Diesel Aftertreatment Performance
Get PDFDiesel engines are widely used in automotive sector due to their high fuel efficiency, distinguished durability and great reliability. However, NOx and particulate matters (PM) are main concerns of the Diesel engines due to their lean burn conditions. To reduce these emissions, Diesel engines are usually coupled with state-of-the-art Diesel aftertreatment systems including a Diesel Oxidation Catalyst (DOC), a Diesel Particulate Filter (DPF), and a Selective Catalytic Reduction system (SCR). With increasingly stringent regulations, the estimation and control strategies of Diesel after-treatment systems for NOx and PM reduction are becoming more and more critical and challenging, especially under transient conditions with unknown system dynamics including disturbances and model uncertainties. To address these problems, this thesis focuses on advanced strategies based on disturbance estimation and compensation for improving the performance of Diesel after-treatment systems.
Urea injection and ammonia storage ratio are critical for the SCR system to achieve high NOx reduction efficiency and low NH3 slip. Nevertheless, unknown system dynamics including input (urea injection) disturbances and model uncertainties of SCR system make it challenging to achieve high NOx reduction efficiency and low NH3 slip. To deal with these obstacles, Paper 1, Paper 2 and Paper 3 (Chapter 2, 3, and 4 respectively) proposed active disturbance estimation and compensation methods for enhancing the SCR performance. Paper 1 (Chapter 2) introduces two different methods to accurately detect urea injection and correct for urea dosing control. Paper 2 (Chapter 3) depicts a robust Nonlinear Disturbance Observer (robust NDO) to effectively estimate the ammonia storage ratio in a cost-effective way. Paper 3 (Chapter 4) presents a compound control strategies based on active disturbance rejection control (ADRC) to precisely keep NH3 slip low and achieve high NOx reduction efficiency.
DOC thermal management is critical to effectively burn the soot during DPF regeneration (PM reduction). But unknown system dynamics including DOC inlet emissions and model uncertainties make it difficult for DOC mean temperature estimation and DOC outlet temperature control during DPF regeneration. To deal with these challenges, Paper 4 and Paper 5 (Chapter 5 and 6 respectively) developed active disturbance estimation and compensation strategies for improving DOC thermal management during DPF regeneration. Paper 4 (Chapter 5) introduces a robust filter based on Smooth Variable Structure Filter (SVSF) with augmented disturbance states to estimate the mean temperature of DOC. Paper 5 (Chapter 6) presents a composite controller combining a feedforward controller and an modified Active Disturbance Rejection Controller (mADRC) with time delay compensation for the DOC outlet temperature control.
The proposed methods in the 5 papers are either validated by the calibrated GT-power model or experiments with Diesel after-treatment systems.ThesisDoctor of Philosophy (PhD
DRIVER BEHAVIOUR PREDICTION MODELS USING ARTIFICIAL INTELLIGENCE ALGORITHMS AND STATISTICAL MODELING
Get PDFTo improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes.
For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time.
For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehicles’ intention. It can predict the surrounding vehicles’ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM).
Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiated and the application of the method is elaborated.ThesisDoctor of Philosophy (PhD)Lane changing and car following are the two most frequently encountered driving behaviours for intelligent vehicles. Substantial research has been carried out and several prototypes have been developed by universities as well as companies. However, the low accuracy and high computational cost prevent the existing lane changing models from providing safer and more reliable decisions for intelligent vehicles. In the existing car-following models, there are also few models that consider the effects of cut-ins from adjacent lanes which may result in their poor accuracy and efficiency. To address these obstacles, advanced artificial intelligence algorithms combined with sufficient driving environmental factors are proposed due to their promise of providing accurate, efficient, and robust lane changing and car-following models. The main part of this thesis is composed of three journal papers. Paper 1 proposed a gated branch neural network for a mandatory lane changing suggestion system at the on-ramps of highways; paper 2 developed a recurrent neural network time-series algorithm to predict the surrounding vehicles’ discretionary lane changing intention in advance; paper 3 researched the strategic car-following gap model considering the effect of cut-ins from adjacent lanes
A BI-DIRECTIONAL ACTIVE CELL BALANCING OPTIMIZATION BASED ON STATE-OF-CHARGE ESTIMATION
Get PDFRecently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-ion batteries are increasingly being considered in EVs due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. Conventionally, the batteries are connected in series to achieve the load voltage requirements. However, for the batteries with intrinsic discrepancies or different initial states, cell balancing is a concern because it is the weakest cell that determines the empty point for the battery and an undercharged series cell will shorten the lifetime of the entire pack. The imbalance potential of the battery behaves as the way of State-of-Charge (SOC) mismatch and it’s also temperature dependent. Therefore, in this thesis, an active cell balancing optimization was proposed and conducted in MATLAB to optimize battery unused capacity and thermal effect simultaneously based on bi-directional balancing system and pre-estimated SOC. The bi-directional balancing system was physically built based on “Fly-back” converter to compare balancing performance in discharging, idle, and plug-in charging mode. Moreover, a battery combined model worked collaboratively with robust state and parameter estimation strategies, namely Extended Kalman Filter (EKF) and Smooth Variable Structure Filter (SVSF) in order to estimate SOC for cell balancing. As a result, the proposed method can effectively optimize SOC mismatch around 2.5%. Meanwhile, more uniform temperature was achieved and the maximum temperature can be reduced about 7 ℃.ThesisMaster of Applied Science (MASc
The Development of a Multifunction UGV
Get PDFWith the increasingly prevalent use of robots, this paper presents the design and evaluation of a multifunctional Unmanned Ground Vehicle (UGV) with an adjustable suspension system, overmolding omni-wheels, and a unique tool head pick-up mechanism. The UGV addresses current adaptability, performance, and versatility limitations across various industries, including agriculture, construction, and surveillance. The adjustable suspension system enhances the UGV's stability and adaptability on diverse terrains, and the overmolding omni-wheels improve maneuverability and durability in off-road conditions. The tool head pick-up mechanism allows for the seamless integration of various tools, enabling the UGV to perform multiple tasks without manual intervention. A comprehensive performance evaluation assessed the UGVs' versatility, load capacity, passability, and adaptability. The results indicate that the proposed UGV design successfully addresses current limitations and has the potential to revolutionize various applications in different industries. Further research and development are necessary to optimize the UGV's performance, safety, and cost-effectiveness.ThesisMaster of Applied Science (MASc
HEV Energy Management Considering Diesel Engine Fueling Control and Air Path Transients
Get PDFThis thesis mainly focuses on parallel hybrid electric vehicle energy management problems considering fueling control and air path dynamics of a diesel engine. It aims to explore the concealed fuel-saving potentials in conventional energy management strategies, by employing detailed engine models. The contributions of this study lie on the following aspects: 1) Fueling control consists of fuel injection mass and timing control. By properly selecting combinations of fueling control variables and torque split ratio, engine efficiency is increased and the HEV fuel consumption is further reduced. 2) A transient engine model considering air path dynamics is applied to more accurately predict engine torque. A model predictive control based energy management strategy is developed and solved by dynamic programming. The fuel efficiency is improved, comparing the proposed strategy to those that ignore the engine transients. 3) A novel adaptive control-step learning model predictive control scheme is proposed and implemented in HEV energy management design. It reveals a trade-off between control accuracy and computational efficiency for the MPC based strategies, and demonstrates a good adaptability to the variation of driving cycle while maintaining low computational burden. 4) Two methods are presented to deal with the conjunction between consecutive functions in the piece-wise linearization for the energy management problem. One of them shows a fairly close performance with the original nonlinear method, but much less computing time.ThesisDoctor of Philosophy (PhD
ONE-DIMENSIONAL HIGH-FIDELITY AND REDUCED-ORDER MODELS FOR THREE-WAY CATALYTIC CONVERTER
Get PDFTo improve the performance of the three-way catalytic (TWC) converter, advanced control strategies and on-board diagnostics (OBD) systems are needed. Both rely on a relatively accurate but computationally efficient TWC converter model. This thesis aims to develop a control-oriented model that can be employed to develop the control strategies and OBD systems of the TWC converter.
The thesis consists of two parts, i.e., the high-fidelity model development and the model reduction. Firstly, a high-fidelity model is built using the energy and mass conservation principles. In this model, a constant inlet simulation is used to validate the warming-up characteristics, and a driving cycle simulation is used to calibrate the reaction rate parameters. The results of the simulation show that the high-fidelity model has adequate accuracy. Secondly, a reduced-order model is developed based on phase and reaction simplifications of the high-fidelity model. The aim of the development of the reduced-order model is to propose a computationally efficient model for further development of control strategies and state estimators for OBD systems. The accuracy of the reduced-order model is then validated by means of simulations.ThesisMaster of Applied Science (MASc
ROBUST PATH FOLLOWING CONTROL FOR INDEPENDENTLY ACTUATED AUTONOMOUS GROUND VEHICLES WITH TRANSIENT PERFORMANCE IMPROVEMENT
Get PDFThis thesis addresses the path following control of independently actuated (IA) autonomous ground vehicles (AGVs), and focuses on the constraint control and transient performance improvement. The control aim is to inhibit overshoots and eliminate steady-state errors in path following manoeuvres, and thus to reduce the possibilities of AGVs to surpass safe driving bounds. Centering on this focus, this thesis proposes a novel path-following modeling method, a novel output constraint control approach, and two novel nonlinear control strategies for transient performance improvement. The contributions of this study lie on the following five aspects: 1) A novel definition for the desired heading is proposed using the sideslip-angle compensation, to reduce the steady-state errors in path following caused by the non-zero sideslip-angle and the large/changing path curvature; 2) A novel output constraint control approach using hyperbolic projection technique is proposed to compactly restrain the lateral offset, and thus to prevent the vehicle from surpassing safe lanes; 3) A novel integral sliding mode (ISM)-based composite nonlinear feedback (CNF) technique is proposed to improve the transient performance of path following, considering the system uncertainties, disturbances and actuator saturations; 4) A novel disturbance observer-based CNF technique is proposed to improve the transient performance for path following with differential drive assisted steering (DDAS) when the active steering system is in complete failure. Corresponding comparative simulations based on CarSim-Simulink were conducted to verify the proposed modeling and control strategies in 1)-4); 5) Experiments have been conducted to verify the existence and effectiveness of the DDAS mechanism.ThesisDoctor of Philosophy (PhD)This thesis focuses on the constraint control in the path following of autonomous ground vehicles with transient performance improvement, where the system uncertainties, external disturbances and input saturations have been considered. The constraint control is used to constrain the path following errors in the safety regions, and the transient control is used to improve the transient performance. The ultimate control objective is to increase the path following accuracy and convergence speed, and thus to improve the vehicle stability, safety and ride comfort. This thesis proposed a novel desired-heading amendment to modify the traditional path following model to reduce the system overshoot, a novel output constraint strategy to constrain the lateral offset in reasonable bound, and two novel nonlinear control strategies to improve the transient performance. Also, experiments were conducted to verify the effectiveness of the differential drive assisted steering
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