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A growing population is driving automatization in agricultural industry to strive for more productive arable land. Being part of this process, this work is aimed to investigate the possibility to implement sensor-based automation in a particular system called Front End Loader, which is a lifting arms that is commonly mounted on the front of a tractor. Two main tasks are considered here, namely Electronic Self Leveling (ESL) and payload estimation. To propose commercially implementable solutions for these tasks, specific objectives are set, which are: 1) to propose a controller to perform ESL under typical disturbances 2) to propose a methodology for payload estimation considering realistic estimation conditions. Lastly, aligned with these goals, 3) to propose models for the Front End Loader under consideration for derivation of solutions of the specified tasks.

The self-leveling task assists farmers in maintaining the angular position of the mounted implements, e.g. a bale handler or a bucket, with respect to the ground when the loader is manually lifted or lowered. Experimental results show that different controllers are required in lifting and lowering motions to maintain the implement's angular position with a required accuracy due to principle differences in gravity impact. The gravity helps the necessary correction in lifting motion, but works against the correction in lowering motions. This led us to propose a controller with a proportional term, a discontinuous term and an on-line disturbance estimation and compensation as well as the tuning procedure to achieve a 2 degrees tracking error for lowering motions in steady state. The proposed controller shows less sensitive performance to lowering velocity, as the main disturbance, in comparison to a linear controller.

The second task, payload estimation, assists farmers to work within safety range as well as to work with a weight measurement tool. A mechanical model derived based on equations of motion is improved by a pressure based friction to sufficiently accurately represent the motion of the front end loader under consideration. The proposed model satisfies the desired estimation accuracy of 2\% full scale error in a certain estimation condition domain in constant velocity regions, with off-line calibration step and off-line payload estimation step. An on-line version of the estimation based on Recursive Least Squares also fulfills the desired accuracy, while keeping the calibration step off-line.

Automation of various agricultural tasks, which are nowadays routinely executed by operators of hydraulically actuated tractors equipped with front-end loaders, is an important open problem. The so-called self-leveling task is considered here, where the lifting and lowering motions of the loader are performed manually while the orientation of the tool must be adjusted automatically. The proposed controller is constituted by a proportional feedback, a disturbance compensator based on an observer and a relay controller. A model-based tuning procedure for the controller parameters is discussed and an implementation is validated experimentally on an industry-standard commercial set-up.

A methodology based on an equation of motion and a two-steps off-line optimization procedure is proposed and investigated to estimate a payload in an industry-standard Front-End Loader. The location of the boom center of gravity is first calibrated via optimization using a known payload to become the base to estimate an unknown payload. This method is compared to an existing method and is verified to be less sensitive to different estimation conditions. Moreover, it also requires less additional operations in the calibration phase.

Due to some limitations of mechanical solution for automation of front end loaders, specifically self-leveling task, we investigate here the sensor-based solution. We present a model of ront end loaders and the comparison of different controllers to realize this task. The best performance is observed from the controller comprising linear terms, compensation of input nonlinearity known as dead zone, and compensation of disturbances from its estimation.

We consider here the development of a better payload estimation system for an agricultural Front-End Loader, equipped with position and pressure sensors, that enable monitoring of various routine operations. We show that an additional pressure-based friction model improves the achievable accuracy of the estimation procedure for payload estimation based on equations of motion. Preceded by estimating the friction parameters in the calibration step, the improvement is verified by employing the updated model in off-line payload estimations for an industry standard Front-End Loader with different estimation conditions under consideration. The overall estimation accuracy is below 2% full scale error. This performance is also maintained in the online implementation of the second step.