Implementation of a fuzzy logic based seeding depth control system
| dc.contributor.author | Tessier, Thomas R. | en_US |
| dc.date.accessioned | 2007-05-15T15:16:50Z | |
| dc.date.available | 2007-05-15T15:16:50Z | |
| dc.date.issued | 1997-01-20T00:00:00Z | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Imprecise seeding depth can lead to lower crop yields and increased power requirements. For example, a variation of 2.56 cm (1 inch) from optimum depth results in 45%, 25%, and 15% reduction in the emergence of soybeans, sorghum, and wheat, respectively. Problems encountered with traditional linear seeding depth control systems include oscillation, instability, and inaccurate depth control in undulating terrain. Variations of 1.27 cm to 4.45 cm (0.5 to 1.75 inches) are not uncommon. The control system in this research utilizes a microprocessor-based fuzzy logic control process in combination with a novel seeding depth gauge system. This system combines the human control reasoning of fuzzy logic with an electro-hydraulic seeding depth controller. The fuzzy algorithm is modelled upon expert human control by means of linguistic variables that describe the error between the measured seeding depth and the desired depth set point. The inputs of the fuzzy control rules are the error between the measured seeding depth and the desired seeding depth, and the rate of change in error (the difference between the present and a past sampling instance). These variables describe the direction of the error (positive, P or negative, N) as well as the magnitude (small, S, medium, M or big B). The change in error is monitored in order to provide differential control. Stability of the control system is ensured by monitoring these proportional and differential input signals. The microprocessor unit used in the controller is the Motorola MC68HC16Z1. Three scenarios were examined: a 3-membership function, a 5-membership function, and a 7-membership function fuzzy control algorithm. In each algorithm simulation, the variation in output gain, complexity, and number of MPU cycles to execute was observed. The 5-membership function algorithm us ng triangular membership functions was found to provide the best combination of control and complexity for this application. Two controller units were constructed, for testing in laboratory simulations and under field conditions. The 5-membership function algorithm was tested under field conditions. Data was logged from the field trials, with error mean from the set point being 2 mm, with variation and standard deviation being 17.1 cm and 2.8 cm, respectively, based on converted A/D readings. | en_US |
| dc.format.extent | 12224222 bytes | |
| dc.format.extent | 184 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | text/plain | |
| dc.identifier.uri | http://hdl.handle.net/1993/784 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.title | Implementation of a fuzzy logic based seeding depth control system | en_US |
| dc.type | master thesis | en_US |