Model-based H2/H1 control design of integrated vehicle tracking systems

The paper presents the design of an integrated heavy vehicle system which consists of the driveline, the brake, the suspension and the steering components. The purpose of the integration is to create an adaptive cruise control which is able to keep the distance from the preceding vehicle and track the path on roads. The vehicle model consists of the dynamics of the sprung mass and the unsprung masses and handles the effects of road and wind disturbances. The design of the adaptive cruise-control system is based on the H2/H∞ control method. The operation of the controlled system is illustrated through simulation examples

The NASA Lewis integrated propulsion and flight control simulator

A new flight simulation facility was developed at NASA-Lewis. The purpose of this flight simulator is to allow integrated propulsion control and flight control algorithm development and evaluation in real time. As a preliminary check of the simulator facility capabilities and correct integration of its components, the control design and physics models for a short take-off and vertical landing fighter aircraft model were shown, with their associated system integration and architecture, pilot vehicle interfaces, and display symbology. The initial testing and evaluation results show that this fixed based flight simulator can provide real time feedback and display of both airframe and propulsion variables for validation of integrated flight and propulsion control systems. Additionally, through the use of this flight simulator, various control design methodologies and cockpit mechanizations can be tested and evaluated in a real time environment

A control framework for a remotely operated vehicle

A control framework enabling the automated maneuvering of a Remotely Operate Vehicle (ROV) is presented. The control architecture is structured according to the principle of composition of vehicle motions from a minimal set of elemental maneuvers that are designed and verified independently. The principled approach is based on distributed hybrid systems techniques, and spans integrated design, simulation and implementation as the same model is used throughout. Hybrid systems control techniques are used to synthesize the elemental maneuvers and to design protocols, which coordinate the execution of elemental maneuvers within a complex maneuver. This work is part of the Inspection of Underwater Structures (IES) project whose main objective is the implementation of a ROV-based system for the inspection of underwater structures

Neural network application to aircraft control system design

The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design

A Flexible Hierarchical Model-Based Control Methodology for Vehicle Active Safety Systems.

To improve the stability and safety performance of active safety systems, vehicle control systems are increasingly incorporating sophisticated chassis actuator control functions. Thus vehicle control systems require efficient control algorithms to reduce the amount of duplicate hardware or adopt various actuator combinations in response to diverse customer demands and various hardware combinations from different suppliers. One of the main challenges for efficient controller design is how to create a flexible modular design which can respond to different reference vehicle behaviors and provide optimal actuator apportionment, while avoiding functional conflicts between chassis sub-systems. The proposed control methodology offers a total vehicle motion control solution for an active safety system by addressing the issue of flexible modular design in integrated vehicle control systems. In this thesis, modularity is realized by a model-based hierarchical control structure consisting of three layers: an upper layer for reference vehicle motions, an intermediate layer for actuator apportionment, and a lower layer for stand-alone actuator control. The reference vehicle motions can be determined by any type of reference model for vehicle stability control or collision avoidance control. The actuator apportionment uses Model Predictive Control (MPC) to provide flexibility by balancing tire forces to track target reference vehicle motions while simultaneously considering constrained conditions, such as actuator limits or available actuator combinations. Moreover, the MPC is designed to be feasible in real-time using a linear time-varying MPC approach which avoids the complexity of full nonlinear MPC and addresses the vehicle nonlinearity. The effectiveness of the proposed control structure is investigated for vehicle stability control in terms of handling stability and handling responsiveness. The handing stability and responsiveness of the control structure appear to be robust with respect to various uncertain environments including model-plant mismatch and diverse driving conditions. It is then applied to collision avoidance systems by adapting the reference vehicle motions at the upper level of the controller. Collision avoidance is found to be nearly as effective as that of the combination of an optimizing driver supported by the MPC-based stability controller; this suggests that the MPC approach could be used in future high performance collision avoidance systems.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57610/2/sehyun_1.pd

Trajectory-Based Loads for the Ares I-X Test Flight Vehicle

In trajectory-based loads, the structural engineer treats each point on the trajectory as a load case. Distributed aero, inertial, and propulsion forces are developed for the structural model which are equivalent to the integrated values of the trajectory model. Free-body diagrams are then used to solve for the internal forces, or loads, that keep the applied aero, inertial, and propulsion forces in dynamic equilibrium. There are several advantages to using trajectory-based loads. First, consistency is maintained between the integrated equilibrium equations of the trajectory analysis and the distributed equilibrium equations of the structural analysis. Second, the structural loads equations are tied to the uncertainty model for the trajectory systems analysis model. Atmosphere, aero, propulsion, mass property, and controls uncertainty models all feed into the dispersions that are generated for the trajectory systems analysis model. Changes in any of these input models will affect structural loads response. The trajectory systems model manages these inputs as well as the output from the structural model over thousands of dispersed cases. Large structural models with hundreds of thousands of degrees of freedom would execute too slowly to be an efficient part of several thousand system analyses. Trajectory-based loads provide a means for the structures discipline to be included in the integrated systems analysis. Successful applications of trajectory-based loads methods for the Ares I-X vehicle are covered in this paper. Preliminary design loads were based on 2000 trajectories using Monte Carlo dispersions. Range safety loads were tied to 8423 malfunction turn trajectories. In addition, active control system loads were based on 2000 preflight trajectories using Monte Carlo dispersions

Modeling and Robust Control of Integrated Ride and Handling of Passenger Cars

Vehicle industries in the last decade have focused on improving ride quality and safety of passenger cars. To achieve this goal, modeling and simulation of dynamic behaviour of vehicles have been widely studied to design model based and robust control strategies. This PhD work presents a new integrated vehicle model and a nonlinear robust controller. The thesis is divided into two main sections: dynamic modeling and controller design. A new fourteen Degrees of Freedom integrated ride and handling vehicle model is proposed using Lagrangian method in terms of quasi-coordinates. The governing equations are derived considering the interaction between the ride and handling systems, Euler motion of the frames attached to the wheels and body, the load transfer among the wheels, acceleration and braking. A non-dimensional factor called coupling factor is introduced to study the coupling among different DOFs of the dynamic system for a defined vehicle maneuver. The coupling factor is considered as an indicator parameter to demonstrate the advantages of the developed model over the existing dynamic models. The improved model is validated using ADAMS/Car for different manoeuvres. The simulation results confirm the accuracy of the improved dynamic model in comparison with the ADAMS/Car simulations and the models available in the literature. Considering the proposed nonlinear integrated ride and handling vehicle model, a nonlinear robust controller is designed for an intermediate passenger car. The H∞ robust control strategy is designed based on the Hamiltonian-Jacobi-Isaacs (HJI) function, Linear Matrix Inequality and State Feedback techniques. In order to improve the ride and handling quality of the vehicle, a Magneto-rheological (MR) damper and a differential braking system are used as control devices. A frequency dependent MR damper model is proposed based on the Spencer MR damper model. The parameters of the model are identified using a combination of Genetic algorithms and Sequential Quadratic Programming approaches based on the experimental data. A mathematical model is validated using the experimental results which confirm the improvement in the accuracy of the model and consistency in the variation of damping with frequency. Based on the proposed MR damper model, an inverse model for the MR damper is designed. A differential braking system is designed to assign desired braking action. The dynamic behavior of the controlled vehicle is simulated for single lane change and bump input, considering three different road conditions: dry, rainy and snowy. The robustness of the designed controller is investigated when the vehicle is under these road conditions. The simulation results confirm the interactive nature of the ride and handling systems and the robustness of the designed control strategy

Control of vehicle lateral dynamics based on longitudinal wheel forces

Trends show that on board vehicle technology is becoming increasingly complex and that this will continue to be the case. This complexity has enabled both driver assistance systems and fully automatic systems to be introduced. Driver assistance systems include anti-lock braking and yaw rate control, and these differ from fully automatic systems which include collision avoidance systems, where control of the car may be taken away from the driver. With this distinct difference in mind, this work will focus on driver assist based systems, where emerging technology has created an opportunity to try and improve upon the systems which are currently available. This work investigates the ability to simultaneously control a set of two lateral dynamics using primarily the longitudinal wheels forces. This approach will then be integrated with front wheel steering control to assess if any benefits can be obtained. To aid this work, three different vehicle models are available. A linear model is derived for the controller design stage, and a highly nonlinear validated model from an industrial partner is available for simulation and evaluation purposes. A third model, which is also nonlinear, is used to integrate the control structures with a human interface test rig in a Hardware in the Loop (HiL) environment, which operates in real-time. Frequency based analysis and design techniques are used for the feedback controller design, and a feedforward based approach is used to apply a steering angle to the vehicle model. Computer simulations are initially used to evaluate the controllers, followed by evaluation via a HiL setup using a test rig. Using a visualisation environment in Matlab, this interface device allows driver interaction with the controllers to be analysed. It also enables driver reaction without any controllers present to be compared directly with the controller performance whilst completing the test manoeuvres. Results show that during certain manoeuvres, large variations in vehicle velocity are required to complete the control objective. However, it can be concluded from both the computer simulation and HiL results that simultaneous control of the lateral dynamics, based on the longitudinal wheel forces can be achieved using linear control methods

PHYSICS-BASED MODELING AND CONTROL OF POWERTRAIN SYSTEMS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Low Temperature Combustion (LTC) holds promise for high thermal efficiency and low Nitrogen Oxides (NOx) and Particulate Matter (PM) exhaust emissions. Fast and robust control of different engine variables is a major challenge for real-time model-based control of LTC. This thesis concentrates on control of powertrain systems that are integrated with a specific type of LTC engines called Homogenous Charge Compression Ignition (HCCI). In this thesis, accurate mean value and dynamic cycleto- cycle Control Oriented Models (COMs) are developed to capture the dynamics of HCCI engine operation. The COMs are experimentally validated for a wide range of HCCI steady-state and transient operating conditions. The developed COMs can predict engine variables including combustion phasing, engine load and exhaust gas temperature with low computational requirements for multi-input multi-output realtime HCCI controller design. Different types of model-based controllers are then developed and implemented on a detailed experimentally validated physical HCCI engine model. Control of engine output and tailpipe emissions are conducted using two methodologies: i) an optimal algorithm based on a novel engine performance index to minimize engine-out emissions and exhaust aftertreatment efficiency, and ii) grey-box modeling technique in combination with optimization methods to minimize engine emissions. In addition, grey-box models are experimentally validated and their prediction accuracy is compared with that from black-box only or clear-box only models. A detailed powertrain model is developed for a parallel Hybrid Electric Vehicle (HEV) integrated with an HCCI engine. The HEV model includes sub-models for different HEV components including Electric-machine (E-machine), battery, transmission system, and Longitudinal Vehicle Dynamics (LVD). The HCCI map model is obtained based on extensive experimental engine dynamometer testing. The LTC-HEV model is used to investigate the potential fuel consumption benefits archived by combining two technologies including LTC and electrification. An optimal control strategy including Model Predictive Control (MPC) is used for energy management control in the studied parallel LTC-HEV. The developed HEV model is then modified by replacing a detailed dynamic engine model and a dynamic clutch model to investigate effects of powertrain dynamics on the HEV energy consumption. The dynamics include engine fuel flow dynamics, engine air flow dynamics, engine rotational dynamics, and clutch dynamics. An enhanced MPC strategy for HEV torque split control is developed by incorporating the effects of the studied engine dynamics to save more energy compared to the commonly used map-based control strategies where the effects of powertrain dynamics are ignored. LTC is promising for reduction in fuel consumption and emission production however sophisticated multi variable engine controllers are required to realize application of LTC engines. This thesis centers on development of model-based controllers for powertrain systems with LTC engines