Abstract
Model-based methods are used in industry for prototyping concepts based on mathematical models. With our forest industry partners, we have established a model-based workflow for rapid development of motion control systems for forestry cranes. Applying this working method, we can verify control algorithms, both theoretically and practically. This paper is an example of this workflow and presents four topics related to the application of nonlinear control theory. The first topic presents the system of differential equations describing the motion dynamics. The second topic presents nonlinear control laws formulated according to sliding mode control theory. The third topic presents a procedure for model calibration and control tuning that are a prerequisite to realize experimental tests. The fourth topic presents the results of tests performed on an experimental crane specifically equipped for these tasks. Results of these studies show the advantages and disadvantages of these control algorithms, and they highlight their performance in terms of robustness and smoothness.
Highlights
Hydraulic technology has had a great impact on the development of modern industry, and it prevails as the primary component in mechanized heavy-duty equipment
We see a breakthrough of this technology in the forest industry, a business that has profoundly advance from manual labor to the use of very sophisticated machinery
These results demonstrate that the models are able to capture the motion dynamics with sufficient accuracy
Summary
Hydraulic technology has had a great impact on the development of modern industry, and it prevails as the primary component in mechanized heavy-duty equipment. The timber exploited with these machines provides the raw material for countless industrial products. Forestry machines are equipped with highly robust and efficient hydraulic technology. The efficiency and the work profit depend heavily on the operators working with these machines. This working assignment is difficult and demands highly skilled people. For this reason, operators, beginners, practice the work tasks by combining real and virtual training [1, 2]
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