Methods for Automatic Hydraulics Calibration in Construction Equipment

Abstract: In this thesis we investigate the problem of automatic calibration and control of hydraulic components in the domain of construction equipment. Methods that are able to remove a costly manual approach in favour of an automatic one are investigated and evaluated. The thesis aims to investigate what methods are available in achieving this goal as well as evaluate the performance and applicability of such methods in the domain of construction equipment. The literature indicates that a great focus is put on learning a model of the plant at run time in order to provide accurate control. Common approaches to the problem are the Recursive Least Square method and PID controllers for non-linear systems, but other methods are also present, such as the Nodal Link Perceptron Network (NLPN). The methods chosen to be compared are the existing method of manually calibrating two set points for start and end current and interpolating between them; the use of a PI controller with a static line inverse model; a PI controller with a static curve inverse model; a PI controller with an NLPN adaptive inverse model and lastly, a completely NLPN based control strategy. The methods were implemented in Matlab Simulink and evaluated in simulations based on data collected from real wheel loaders in the construction equipment domain, produced by Volvo CE. The simulations are performed on data from three machines and were evaluated twice for the adaptive methods in order to evaluate how well the methods improved. The results were then evaluated in terms of average absolute error, as well as a discussion of the behaviour shown in the plots. The evaluations indicate that the most effective method for control is the PI controller using a static line inverse model. The method produces the smallest average error of both actions evaluated, lifting and lowering of the boom, while the complete NLPN solution provide the worst results.