Dissertation in the field of Automation Technology, Jouko Kalmari

The title of thesis is Nonlinear Model Predictive Control of a Hydraulic Forestry Crane

In forestry, the level of mechanization has increased significantly in recent decades. Modern forest harvesters, used widely in the Nordic countries, are capable of efficiently felling, delimbing and bucking trees. There is also interest in mechanizing other silvicultural tasks to increase the productivity and decrease the demand for labor. Increasing the level of automation in forest machines could be the next logical step. Forest machines are usually equipped with some sort of hydraulic crane. A variety of different silvicultural tasks can be performed using different purpose-built tools that are attached to the tip of the boom of the forest machine. Therefore, control of the boom is essential if automatic or semiautomatic forest machines are to be developed. The tool is usually attached to the boom so that it can sway relatively freely, therefore the motion of the tool should also be taken into account when designing automatic control.

The main objective of this thesis is to propose a solution to the problem of controlling the hydraulic crane while simultaneously damping the swaying of the tool. This makes it possible to increase the level of automation and productivity. The main hypothesis is that the hydraulic forestry crane can be controlled using nonlinear model predictive control (NMPC), a predefined path can be followed, and the unwanted oscillations of the tool can be damped. The second hypothesis is that the swaying of the tool can be estimated using inertial measurements. The third hypothesis is that early cleaning is a forestry operation where automatic forestry crane can be used together with machine perception. NMPC is a control method designed for systems having nonlinear dynamics and constraints in the states and controls. In NMPC, the optimal control trajectory is solved at each control interval using some optimization algorithm. The estimation and control of the system are based on the same dynamic models. The model of the hydraulic cylinders is relatively simple as a load sensing hydraulic system was used. Experiments using real hardware were conducted in order to test the performance of the NMPC. Three different target paths and different boom tip velocities were tested. The results show that the proposed method is able to control the tip of the boom with reasonable accuracy while simultaneously damping the oscillations of the tool. In addition, the developed estimators seem to function quite well.

Opponent: Professor Jouni Mattila, Tampere University of Technology, Finland

Supervisor: Professor Arto Visala, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation