Parámetros para la simulación de la interacción

Title: Computer based Control of the Separation Process in a Combine Harvester

Publication type: Conference Paper

Conference: EurAgEng VDI-MEG Conference, Hannover, Germany

Date: November 2017

Authors: Dan Hermann

Flemming Schøler Morten Leth Bilde Nils Axel Andersen Ole Ravn

Computer based Control of the Separation

Process in a Combine Harvester

D. Hermann?,??_{F. Schøler}?? _{M.L. Bilde}?? _{N.A. Andersen}? _{O. Ravn}? ? _{Automation and Control Group, Dept. of Electrical Engineering, Technical}

University Denmark, Lyngby, Denmark

?? _{AGCO A/S, Research and Advanced Engineering, Randers, Denmark} Abstract

This paper addresses the design of a control system for a rotary thresh- ing and separation system in a combine harvester. Utilising a distributed control architecture containing all observable crop flow parameters, the rotor speed is adjusted to maintain acceptable separation loss and grain damage using distributed impact sensors and a grain quality sensor (GQS). The GQS settling time for rotor speed changes is significantly reduced us- ing a model based observer facilitating faster adjustment for grain losses in varying conditions.

1

Introduction

During a busy harvest it is desirable to utilise the full capacity of the combine harvester by operating at high throughput whilst maintaining an acceptable grain loss and grain quality. Hence the rotor speed in the threshing and separa- tion system should be adjusted to separate grains from the chaff and straw par- ticles with least possible loss and grain damage. In modern combine harvesters the default machine settings are pre-set in the control computer for each crop type and in some cases adjusted by the operator during harvest after manual inspection of the residue, whereupon site specific conditions are often ignored. Advances within sensor technologies and material flow models have procured a potential for automatic control of the machine settings, that is faster and more precise than a human operator. A sensor for grain quality was shown in [10], an acoustic impact type sensor strip for loss detection was presented in [1], an online separation loss monitoring algorithm in [8] and an online throughput prediction method in [6]. Section 2 presents a novel overall distributed control architecture and a novel rotor speed control scheme for the separation process. In Section 3 the design and implementation of the control system is described. Section 4 and Section 5 presents simulation and field test results.

The threshing, separation and cleaning processes are assigned to numerous op- timisation parameters for throughput, loss and quality, where many are even conflicting, see Fig. 1. In order to optimise the overall process it is vital to

Figure 1: Overall optimisation problem for throughput, loss and quality param- eters.

Figure 2: Acquired field samples of rotor separation loss and broken kernels. understand the underlying interdependencies [2, 4, 5, 9] in the multiple-input- multiple-output (MIMO) system as most actuator adjustments affect multiple parameters. The overall optimisation problem can be formulated as one com- mon cost function for all process parameters. However, in order to simplify the controller complexity a hierarchical architecture is chosen with distributed

control schemes for each actuator, where the control loop can be designed us- ing standard methodologies for single-input-single-output (SISO) systems. This result in a shorter design and implementation phase as well as it reduces time for parameter tuning. For each actuator the dominant opposed process param- eters are used to describe a cost function related to the overall optimisation goal. However some parameters are not directly available using state of the art process sensors, such as un-threshed heads or straw quality. The speed of the threshing and separation rotor is the dominating effect on the opposed param- eters separation loss and grain damage; see field test samples in Fig. 2. An acoustic impact type sensor strip [1] with four membranes is used to measure the grain loss. Several sensor strips are placed strategically along the separa- tion rotor to obtain the best possible measurement of the separation loss. The GQS is located in the top of the clean grain elevator measuring the relation- ship of broken kernels and materials other than grain (MOG) in the clean grain throughput. The windows size of the GQS captures approximately 100 grain kernels for each sample in small grain, i.e. a large number of images is required provide an accurate observation of grain damage significantly below 1%.

3

Rotor Speed Control Scheme

The objective for the rotor speed controller is to optimise the distributed cost function for separations loss and grain damage, i.e. balance the two opposed parameters. The closed-loop implementation is shown in Fig. 3. Here the estimate of the broken kernels ˆΓp,b is subtracted from the separation loss ˙mp,l

normalised with the yield sensor reading ˙my. A model based observer is designed

to estimate grain damage ˆΓp,b using GQS reading Γp,b and rotor speed ωr, see

Fig. 4. A graphical user interface allows the operator to provide weights within a preselected range for grain loss up,l and grain damage uΓ,b.

Figure 3: Closed-loop control scheme.

3.1

Model generation

The static grain damage model is obtained from multiple rotor speed actuator curves, similar to Fig. 1b, obtained from material samples or averaged GQS readings over several minutes for each rotor speed set point, [5]. The obtained data points are then fitted to the model describing the static grain damage from rotor speed, given by Γp,b= cΓexp(pΓωp).

3.2

Observer design

The Luenberger observers state-space vector x = [cΓ, ωp]T is given by the

model scaling parameter cΓand ωp, the filtered state of the input vector u = ωr.

The state ωp is filtered with the time constant τp characterising the threshing

and separation system dynamics. The continuous time state-space model is given by ˙x = f (x) =  ˙cΓ ˙ωp  =  σc2 apωp+ bpωr  and Γp,g= y = h(x) = cΓexp(prωp)

for the zero-mean white noise variance σ2

c, threshing and separation system dy-

namics ap and bp as well as the grain damage loss model parameter pΓ. The

GQS has a varying sample time TGQS as it relies on a material flow in the

clean grain elevator, correct paddle sample synchronisation and image process- ing time. For each GQS measurement update, the estimate is updated using ˆ

x(k + 1) = F ˆx(k) + Gu(k) + K(y− ˆy), else the estimate is predicted according to ˆx(k + 1) = F ˆx(k) + Gu(k).

4

Simulation Results

The observer and closed-loop control system is tested by means of simulation, using a virtual combine [3, 5, 7]. The virtual combine facilitates simulation of all actuators inputs (u), states (x) and crop flow sensor readings (y). Fig. 5 shows an open-loop (a) and closed-loop (b) simulation in soybeans for observer and controller verification respectively. In Fig. 5a the first plot row shows the

Figure 5: Simulation of separation process with loss and grain damage sensors. Fig. 5a shows a simulation of the two observers for broken grain in open-loop. Fig. 5b shows a simulation with the closed-loop rotor speed controller.

estimated and true percentage of broken kernels. Second plot the estimated separation loss ˙mp,l and yield sensor reading ˙my, and third plot the rotor speed

ωr. The combine enters crop after 2 min and all estimates are initialised af-

ter 7 min. After 12 min the throughput increases and causes a separation loss increase. Rotor speed is increased after 22 min, reducing separation loss and increasing grain damage. Notice the rapid response from the model based ob- server compared to the average filter. After 32 min a change (disturbance) in the field conditions increases the grain damage significantly. Fig. 5b shows a similar sequence, except the operator optimisation focus (uΓ,b and up,l ) is ad-

justed after 22 min. The controller increases the rotor speed as the separation

When the disturbance in field conditions causes increased grain damage after 32 min. the rotor speed is reduced to maintain the control balance.

5

Field Test Results

In Fig. 6 field test sequence from barley is shown with same row division as in Fig. 5.

Figure 6: Field test results for rotor speed controller for barley. In Fig. 6 the combine enters barley after 20 sec, with a high initial rotor speed which is rapidly decreased. The combine is operating in consistent yield density but varying separation loss. A increase in separation loss is observed between two and six minutes of operation, where the rotor speed is temporarily increased to reduce separation loss, causing a minor increase in broken kernels.

6

Conclusion

The paper described the distributed control architecture derived from the ob- servable optimisation objectives. The rotor speed control scheme was presented explaining the interaction from separation loss and grain damage as well as op- erator interaction. Model generation and observer design was presented in order to reduce the settling for the GQS grain damage observation. Simulation showed a reduction of the maximum separation loss and grain damage. Results from full scale field tests verified the actuator response to varying crop conditions. The automatic adjustment of the grain damage and separation loss balance in- dicates a significant improvement of the overall threshing and separation system performance.

Acknowledgement

The authors gratefully acknowledge the support from the department of Au- tomation and Robot Technology, Technical University of Denmark and financial support from the AGCO cooperation.

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