Bohdan T. Kulakowski

Rollover dynamics of road vehicles: literature survey

This paper presents a review of literature pertaining to vehicular rollover. It is by no means a complete review of all rollover literature available, but covers many of the most frequently cited papers and those that the author believes make a substantial contribution to the field of vehicle dynamics and rollover in particular. This review is limited to papers covering rollover of road vehicles, such as passenger cars, utility vehicles and heavy commercial trucks - both articulated and non-articulated, i.e. the review excludes papers regarding off-road vehicles. In addition, this review focuses mainly on cases of manoeuvre induced rollover such as rollover in cornering, lane-change manoeuvres, etc., though rollover by tripping is discussed to a certain degree. It begins with a general introduction to the rollover phenomenon that may be applied to both articulated and non-articulated vehicles. Non-articulated vehicles are then examined in more detail and a review of some research into stability metrics and the prediction of rollover for these vehicles is presented. Likewise, the stability metrics and prediction of rollover for articulated heavy trucks carrying rigid and liquid cargo is reviewed along with work into active suspensions, braking control and rollover warning devices.

Modal Approximation of Distributed Dynamics for a Hydraulic Transmission Line With Pressure Input-Flow Rate Output Causality

Based on analytical results obtained in the frequency domain, modal approximation techniques are employed to derive transfer function and state space models applicable to a pressure input-flow rate output causality case of a transmission line. The causality case considered here arises while modeling short connection lines to hydraulic accumulators. However, the modal approximation results presented apply also to other cases where the linear friction model is considered applicable. It is highlighted that the results presented can reduce the overall order of the hydraulic system model containing the transmission line being considered.

Cascade tuning for nonlinear position control of an electrohydraulic actuator

The nonlinear position control of an electrohydraulic actuator is approached by using two control structures. The first is a direct near IO linearization of the system model with piston position as output. The second is a cascade controller with a near IO linearizing pressure force controller as an inner-loop to a feedback plus feed forward outer-loop position controller. It is shown in this paper that the two control structures are theoretically equivalent. Furthermore, the equivalence is exploited to extract a simple, physically intuitive, tuning procedure for the gains of the two controller structures. This is particularly significant for the near IO linearizing position controller whose gains lack a physically tractable interpretation that guides their selection

Active air-suspension design for transit buses

The goal of this study is to design a robust active suspension that is capable of handling the combination of suspension non-linearities, uncertainty in the vehicle model parameters, and preload-dependent variation in the suspension characteristics. Since the loading condition of transit buses often varies from trip to trip, the robustness of the active suspension system with respect to the model parameter uncertainty becomes especially important. The effort of this study consists of two parts: one is to develop a non-linear air-spring model based on experimental data; and the other is to design a sliding mode controller to improve suspension system performance, more specifically, ride quality.

Performance of optical sensors in the control of flexible structures

This paper demonstrates the use of embedded single-mode optical fibers in the active control of smart structures. The experimental prototype structure, a composite lattice configuration fashioned from graphite/bismaleimide laminate, exhibits complex bending and torsional modes, and forms a sub-scale model of a similar structure being analyzed at the Air Force Astronautics Laboratory. Simulations that include effects of Gaussian observation and process noise on a linear quadratic regulator/ loop transfer recovery (LQG/LTR) controller for active vibration damping are presented. The fiber-optic sensor demonstrates advantages over conventional strain gage sensors in terms of reduced sensitivity to sensor placement, reduced problems with observation and control spillovers, and improved control stability.

A Near Input-Output Linearizing Force Tracking Controller for an Electrohydraulic Actuatorn

Under simple practical assumptions, the theory of feedback linearization can be applied to a physical model of an electrohydraulic rectilinear actuator. This paper presents the derivation of a near input-output (IO) linearizing force tracking controller and its experimental implementation on a fatigue testing electrohydraulic actuator. Comparisons are conducted against a linear state feedback with integral controller and a standard PID controller. It is shown that, within the limits of investigated system bandwidth and smoothness restrictions of the desired force trajectory, the near IO linearizing controller has better tracking properties. It is also noted that a sliding mode controller can be interpreted as a robust version of the near IO linearizing controller. Experiments are conducted to investigate the robustness of the controlled system to the parameters of the near IO linearizing controller.Copyright

Development of a software-based rollover warning device

This paper documents the research and development of a software-based rollover-warning device (RWD) to be used for road vehicles, with plans for hardware implementation. Although the RWD development concept is fairly general, the design described in this paper was geared towards heavy vehicles with high center-of-gravity height to track-width ratios. The RWD uses artificial neural networks to learn the dynamic input/output response of a road vehicle and estimate the instantaneous roll stability using inputs that are relatively easy to measure. The state of roll stability is quantified using a convenient measure called the load-transfer ratio (LTR) and used in conjunction with the rate of change of LTR as inputs to the RWD based upon a fuzzy logic rule-base for determination of an output warning level. Although the current RWD is based purely on computer simulation, experimental validation was performed and will be published at a later date.

A Study of Combined Braking and Cornering Performance of a Transit Bus Using Validated Computer Simulation

In this paper, results from actual braking tests of a low-floor, two-axle transit bus are used to validate a dynamic model of the bus. The model, developed using the commercial multibody dynamic system simulation software ADAMS, is an 18 DOF full-bus model of a transit bus equipped with air suspension and pneumatic brakes with Anti-lock Braking System (ABS). An ABS algorithm was implemented in MATLAB/Simulink and linked to the ADAMS full-bus model using co-simulations. The validated full-bus model was then used to study lateral and roll stability of a transit bus during combined braking and cornering maneuvers on various road surface conditions. Numerical experiments were also conducted to study the effects of variations in total load on the dynamics of the bus during braking while cornering. The results indicate the danger of yaw-instability on slippery surfaces. Increasing the total load reduced the lateral acceleration of the bus with a less pronounced effect on the roll angle.© 2004 ASME

Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and Its Application to Heavy Duty Vehicles

This study aims at establishing an accurate yet efficient parameter estimation strategy for developing dynamic vehicle models that can be easily implemented for simulation and controller design purposes. Generally, conventional techniques such as Least Square Estimation (LSE), Maximum Likelihood Estimation (MLE), and Instrumental Variable Methods (IVM), can deliver sufficient estimation results for given models that are linear-in-the-parameter. However, many identification problems in the engineering world are very complex in nature and are quite difficult to solve by those techniques. For the nonlinear-in-the-parameter models, it is almost impossible to find an analytical solution. As a result, numerical algorithms have to be used in calculating the estimates. In the area of model parameter estimation for motor vehicles, most studies performed so far have been limited either to the linear-in-the-parameter models, or in their ability to handle multi-modal error surfaces. For models with nondifferentiable cost functions, the conventional methods will not be able to locate the optimal estimates of the unknown parameters. This concern naturally leads to the exploration of other search techniques. In particular, Genetic Algorithms (GAs), as population-based global optimization techniques that emulate natural genetic operators, have been introduced into the field of parameter estimation. In this paper, hybrid parameter estimation technique is developed to improve computational efficiency and accuracy of pure GA-based estimation. The proposed strategy integrates a GA and the Maximum Likelihood Estimation. Choices of input signals and estimation criterion are discussed involving an extensive sensitivity analysis. Experiment-related aspects, such as imperfection of data acquisition, are also considered. Computer simulation results reveal that the hybrid parameter estimation method proposed in this study shows great potential to outperform conventional techniques and pure GAs in accuracy, efficiency, as well as robustness with respect to the initial guesses and measurement uncertainty. Primary experimental validation is also implemented including interpretation and processing of field test data, as well as analysis of errors associated with aspects of experiment design. To provide more guidelines for implementing the hybrid GA approach, some practical guidelines on application of the proposed parameter estimation strategy are discussed.Copyright

Dynamic Modeling and Control of Engineering Systems: THERMAL SYSTEMS

Thermal components, processes and systems ........................................................................................... 1 Thermal systems ................................................................................................................................... 1 Thermal processes ................................................................................................................................. 3 Thermal components ............................................................................................................................. 4 Thermal (system) engineering (projects) .................................................................................................. 4 Thermal engineering tasks .................................................................................................................... 4 Thermal design ...................................................................................................................................... 5 Thermal instrumentation ....................................................................................................................... 7 Thermal data ......................................................................................................................................... 8 Thermal sciences: Thermodynamics, Fluid flow and Heat and mass transfer .......................................... 9 Thermal applications ............................................................................................................................... 11 Thermal conditioning. HVAC&R ....................................................................................................... 11 Ventilation ....................................................................................................................................... 12 Space heating .................................................................................................................................. 13 Heat dissipation ................................................................................................................................... 15 Coolers ............................................................................................................................................ 16 Heat generation ................................................................................................................................... 17 Heat sources (electrical, chemical...) .............................................................................................. 17 Heating systems (heaters, furnaces, boilers...) ................................................................................ 20 Power generation. Heat engines .......................................................................................................... 22 Steam power plants ......................................................................................................................... 23 Reciprocating power plants ............................................................................................................. 23 Gas turbine power plants................................................................................................................. 24 Cold generation. Refrigerators and freezers ....................................................................................... 24 Cold-producing processes ............................................................................................................... 25 Materials thermal processing .............................................................................................................. 26 Biological processing ...................................................................................................................... 26 Chemical processing ....................................................................................................................... 27 Physical processing ......................................................................................................................... 27 Type of problems ................................................................................................................................ 27