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Researches for automation of hydraulic excavators have been conducted for laborsaving, improved efficiency of operations and increased worker's safety improvement. Authors' final goal is to develop automatic digging system which can realize the high efficiency. Therefore, it is thought that appropriate digging control algorithm is important for the automation. For this goal, this paper shows a dynamics model of the backhoe excavator and simulations using such models. Detailed dynamic models are needed from the point of view of the control engineering. Authors evaluate effectiveness of automatic digging algorithm by simulation models. In this research, the linkage mechanism which contains the closed loops is modeled based on the Newton-Euler formulation, where motion equation is derived. Moreover, we apply a soil model for simulation, based on the two dimensional distinct element method (DEM), in order to reproduce reaction force from grounds.

A high performance digging algorithm for a hydraulic excavator has not been established because the relationship between digging parameters and digging performance is complex. An examination process for a high-performance digging algorithm is proposed. In this paper, the digging efficiency is defined as the soil volume derived by the applied energy to drive the bucket in order to evaluate digging performance. The digging algorithm, which we study for high digging efficiency, decreases the reaction force to the bucket from the soil by moving the bucket upward when the reaction force exceeds a threshold during digging. Digging tests are performed with a miniature test device and a simulation model by two-dimensional distinct element methods (2D-DEM). The device and the simulation assess the effectiveness of the digging algorithm. It is quantitatively shown that the digging performance obtained by the feedback digging system is improved to prevent growing of reaction force.

In this paper, the simulation of the multi-objective optimization problem of a diesel engine is performed using the phenomenological model of a diesel engine and the genetic algorithm. The target purpose functions are Specific fuel consumption, NOx, and Soot. The design variable is a shape of injection rate. In this research, we emphasize the following three topics by applying the optimization techniques to an emission problem of a diesel engine. Firstly, the multiple injections control the objectives. Secondly, the multi-objective optimization is very useful in an emission problem. Finally, the phenomenological model has a great advantage for optimization. The developed system is illustrated with the simulation examples.

The present study extends the recently developed HIDECS-GA computer code to optimize diesel engine emissions and fuel economy with the existing techniques, such as exhaust gas recirculation (EGR) and multiple injections. A computational model of diesel engines named HIDECS is incorporated with the genetic algorithm (GA) to solve multi-objective optimization problems related to engine design. The phenomenological model, HIDECS code is used for analyzing the emissions and performance of a diesel engine. An extended Genetic Algorithm called the ‘Neighborhood Cultivation Genetic Algorithm’ (NCGA) is used as an optimizer due to its ability to derive the solutions with high accuracy effectively. In this paper, the HIDECS-NCGA methodology is used to optimize engine emissions and economy, simultaneously. The multiple injection patterns are included, along with the start of injection timing, and EGR rate.