| 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-parameters. 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-parameters 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 are limited either to the linear-in-the-parameters models, or in their ability to handle multi-modal error surfaces. For models with non-differentiable 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 thesis, a 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.; Experimental validation is also implemented including interpretation and processing of vehicle 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.; As an extension of developing vehicle dynamic models with suitable model parameters, an active suspension is developed to ensure robustness for a wide range of operating conditions by considering both the nonlinearity and the preload-dependence of the air-suspension systems.; Up to this point, most researchers have dealt with a linear suspension model for developing control laws. However, since a real vehicle suspension has inherent nonlinearities and uncertainties, it is not sufficient to represent the real system with a linear model. In the early 1990s many studies began to consider non-linearities, uncertainties and un-modeled parts of a real suspension system, which requires the use of nonlinear model and some adaptive or robust form of control scheme.; Therefore, a robust control scheme, namely sliding mode control, is developed for an active suspension system such that it maintains satisfactory performance in the presence of nonlinearities and uncertainties (e.g., preload-dependent model parameters) in the air-suspension systems. (Abstract shortened by UMI.)... |
