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Using neural networks, an algorithm has been developed to steer a wheel loader vehicle. Mathematical functions have been used in the past in an attempt to model a human in their operation of many types of vehicles. Since such functions can typically only be derived for situations in which the problem domain is thoroughly understood, research continues in an effort to develop a complete “operator model”. Neural Network algorithms were utilized in an attempt to determine the feasibility of accurately modeling the operator of a wheel loader construction vehicle. These algorithms were also used to determine how the control of different vehicle functions might be automated on a wheel loader.

In this paper we demonstrate our work to date on our underactuated two link robot called the Pendubot. First we will overview the Pendubot's design, discussing the components of the linkage and the interface to the PC making up the controller. Parameter identification of the Pendubot is accomplished both by solid modeling methods and energy equation least squares techniques. With the identified parameters, mathematical models are developed to facilitate controller design. The goal of the control is to swing the Pendubot up and balance it about various equilibrium configurations. Two control algorithms are used for this task. Partial feedback linearization techniques are used to design the swing up control. The balancing control is then designed by linearizing the dynamic equations about the desired equilibrium point and using LQR or pole placement techniques to design a stabilizing controller.

This paper describes the University of Illinois machining system research program. This program focuses on the development of mechanistic models for machining process simulation and the use of these models for the simultaneous engineering of products and processes. Models are presented for end milling, face milling, and cylinder boring which take into account the cutting conditions, tool geometry, workpiece geometry, and system element dynamics. Furthermore, these models explicitly recognize the presence of machining process noise factors such as cutter runout and tool wear. Representative applications for these models are given. A methodology is described for the simultaneous engineering of products and manufacturing processes which incorporates models for the unit manufacturing processes, the manufacturing system, and the product to be produced.