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The increased adoption of electronic controls in off-highway machines increases the complexity of typical machine systems and stresses the traditional process used to develop these machines. To address this issue design engineers are turning from the traditional design methods to Model-Based Design. By using models in the early design stages, engineers can create executable specifications that enable them to immediately validate and verify specifications against the requirements. These models also allow the machine designer to evaluate the complex interactions between mechanics, hydraulics, electronics and other physical phenomena and thereby detect design errors earlier when the cost to fix them is less. This paper presents a model-based approach for developing off-highway equipment machine systems. A dynamic model of the machine and the electro-hydraulic implement and propulsion system is developed and used to verify the overall machine behavior.

Over the past decade there has been a resurgence of interest in applying “artificial” intelligence techniques to control and decision-making systems. This paper surveys the applications of two artificial intelligence techniques, viz., neural networks and fuzzy logic, to earthmoving machine systems control. Neural networks can be thought of as a class of computational models that represent nonlinear input-output mappings, while fuzzy logic aims to capture the knowledge of experts in the form of rules expressed in linguistic terms. The papers reviewed cover their application to machine control, engine control, transmission control, diagnostics and prognostics, operator modeling, and other related applications.