Dynamic payload estimation in four wheel drive loaders (2008)

Knowledge of the mass of the manipulated load (i.e.payload) in off-highway machines, particularly Four-Wheel-Drive Loaders is useful information for a variety of reasons ranging from knowledge of machine stability to ensuring compliance with transportation regulations.This knowledge is difficult to ascertain however.This dissertation concerns itself with delineating the motivations for, and difficulties in development of a dynamic payload weighing algorithm.The dissertation will describe how the new type of dynamic payload weighing algorithm was developed and progressively overcame some of these difficulties.The payload mass estimate is dependent upon many different variables within the off-highway vehicle.These variables include static variability such as machining tolerances of the revolute joints in the linkage, mass of the linkage members, etc as well as dynamic variability such as whole-machine accelerations, hydraulic cylinder friction, pin joint friction, etc.Some initial effort was undertaken to understand the static variables in this problem first by studying the effects of machining tolerances on the working linkage kinematics in a four-wheel-drive loader.This effort showed that if the linkage members were machined within the tolerances prescribed by the design of the linkage components, the tolerance stack-up of the machining variability had very little impact on overall linkage kinematics.Once some of the static dependent variables were understood in greater detail significant effort was undertaken to understand and compensate for the dynamic dependent variables of the estimation problem.The first algorithm took a simple approach of using the kinematic linkage model coupled with hydraulic cylinder pressure information to calculate a payload estimate directly.This algorithm did not account for many of the aforementioned dynamic variables (joint friction, machine acceleration, etc) but was computationally expedient.This work however produced payload estimates with error far greater than the 1% full scale value being targeted.Since this initial simplistic effort met with failure, a second algorithm was needed.The second algorithm was developed upon the information known about the limitations of the first algorithm.A suitable method of compensating for the non-linear dependent dynamic variables was needed.To address this dilemma, an artificial neural network approach was taken for the second algorithm.The second algorithm's construction was to utilise an artificial neural network to capture the kinematic linkage characteristics and all other dynamic dependent variable behaviour and estimate the payload information based upon the linkage position and hydraulic cylinder pressures.This algorithm was trained using empirically collected data and then subjected to actual use in the field.This experiment showed that the dynamic complexity of the estimation problem was too large for a small (and computationally feasible) artificial neural network to characterize such that the error estimate was less than the 1% full scale requirement.A third algorithm was required due to the failures of the first two.The third algorithm was constructed to take advantage of the kinematic model developed and utilise the artificial neural network's ability to perform informal starts at the beginning of life and, if life is lived well, does not culminate.I believe this realization is of more significant importance than any of the research contained herein.There are a multitude of people that have been directly and indirectly responsible for this work.Acknowledgement should be made to John Deere, specifically Lee Tucker, Mac Klingler and Brian Rauch who really started me down this path in life with their understanding that technical skills are a valuable commodity and are worth investing in.I am also indebted to a special group of Deere researchers and engineers on two continents that have impacted this work in some way with their critiques, suggestions or data gathering.That group in no particular order consists of Aki Putkonen, Marko Paakkunainen, Timo Kappi, Jeff Dobchuk, Eric Anderson, Cory Brant and Kevin Campbell.I am grateful for your assistance.I