control de retroalimentacion visual de un horno de bandas industrial

7/23/2019 Control de Retroalimentacion Visual de Un Horno de Bandas Industrial

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Vision-based feedback control of an industrial band

oven.C. Guarino Lo Bianco, M. Romano, and A. Piazzi

Univ. di Parma - Dip. di Ingegneria dell’Informazione,Parco Area delle Scienze, 181/A - 43100 Parma - Italy,

Tel. + 39 0521 905752, Fax. + 39 0521 905723,E-mail: {guarino, romano, piazzi }@ce.unipr.it

Keywords : vision inspection, non stationarythermal model, band oven, adaptive control.

Abstract

This paper describes a control application based onthe vision inspection of an industrial process: a con-tinuous band oven used to bake biscuits. The purposeof the paper is to propose a control strategy in orderto improve the quality of the baked products and theefficiency of the baking process. The controller feed-back is provided by a vision inspection system whichevaluates the cookies baking condition on the basisof the biscuits color.

1 Introduction.

Vision inspection systems are becoming very com-mon in industrial applications. Several reasons jus-tify the diffusion of such a technology. In many prac-tical cases, the most relevant characteristics of theindustrially produced items concern their shape andcolor. For example, high quality ceramic tiles mustbe inspected in order to detect and discard damagedor out of standard pieces. In other applications it isimportant to detect defects in the raw materials (pa-per and glass production). These inspection activitieshas been historically managed by means of the humanvision. The purpose of the automatic vision inspec-tion systems is to substitute the human operator inthis annoying activity and to improve the quality of the product by eliminating the subjectivity which isintrinsic in case of human inspection.

To this aim, the European Commission has re-cently activated a cluster of projects named “EU-ropean Take-up of essential Information SocietyTechnologies-Integrated Machine Vision” (EUTIST-IMV) devoted to the introduction of the machine vi-

sion in the industrial automation (see the web sitehttp://www.spt.fi/eutist/). This paper will de-

scribe one of these project named “quality COntrOlof baKIng status of ovEn productS” (COOKIES).The project name well synthesizes the application.The objective is to implement an autonomous feed-back controller to supervise the baking process in anindustrial band oven used to cook biscuits. The maininformation used by the closed loop controller is thebaking condition of the biscuits, which is evaluatedwith the help of a vision inspection system. The testquality of baked products made by means of visioninspection is considered in several works of the litera-ture. For example, in [4] the product is inspected by

analyzing, by means of a fuzzy algorithm, the lumi-nance signal of a vision system. The same problem isconsidered also in [7] but the product quality is eval-uated with the help of an artificial neural network.In the same paper, the relevance of considering colorimages, instead of monochromatic ones, is evidenced.The target of the COOKIES project is even moreambitious since the vision system is used not onlyto check the product characteristics, but also to acton the plant in order to control the baking process.The final target is to improve the product quality andreduce the food scraps.

Three units are collaborating to the COOKIESproject. The ATE unit is the industrial partner re-sponsible for the visual acquisition system, while theUniversity of Parma is developing the feedback con-trol system. The project end user is the ColussiS.p.A.: the supervisory feedback system will be usedto control one of its continuous band ovens.

This paper describes the adaptive control strategyproposed to handle the baking process. It is basedon an hybrid fuzzy supervisor. Fuzzy controllers areoften used for the control of heating processes [3, 6,1]. The purpose of the fuzzy supervisor used in thiswork is to select the most appropriate oven control

1



BiscuitsVideo camera

1 2 n

Figure 1: Schematic representation of a continuousband oven composed by n elements.

Figure 2: The Colussi band oven.

strategy.

In § 2 the plant is described and the structure of the controller is briefly summarized. The vision in-spection system is described in § 3 while § 4 reportswith details the control strategy adopted. In § 5 theresults of some simulations are reported and com-mented. Final conclusions are drawn in § 6.

2 The band oven and the over-

all control scheme.

A band oven is made of several independent cookingstations. The product “travels” through these sta-tions carried by a band conveyer (see Fig. 1). Foreach station it is possible to set the burner temper-ature in order to warm up appropriately the air fluxused to cook the biscuits. Moreover, several mechani-cal valves are used to drive correctly the air flux insidethe cooking chambers and to regulate the air inter-change with the external environment. All theseregulations are normally performed by the so called“oven manager” whose decisions are mainly based onthe experience maturated over the years. Thus, thehuman factor is a relevant aspect of the automation

temperature

servos

visiondata processing

oven

process

visual output

FUZZY

SUPERVISOR

ADAPTIVE

CONTROLLER

temperatureset-points

manipulativeoven

temperatures

nputs o t e oven manager

Figure 3: Control scheme for the continuous bandoven.

of the baking process. Every day the process surveil-lance is entrusted to several oven managers. Each of them acts differently on the oven, basing his choices

on his own experience. As a consequence, differentresults could be obtained even when the operatingconditions are the same and the product quality couldchange along the day. Another problem arises due tothe human supervision. The baking process is verysensible to several environmental factors such as airtemperature, pressure, and humidity: if one of thesefactors changes, the oven manager has to modify theoven settings in order to keep constant the productquality. Unfortunately, the oven surveillance is notcontinuous, so that any drift in the environmentalconditions can cause large product losses.

The purpose of the vision based feedback controllerproposed in this paper is to overtake these problemsin order to guarantee a constant quality and to reducethe product losses. The feedback controller proposedwill act on the last oven of the chain, which is knownto be the major responsible for the overall biscuitsquality.

The control scheme is sketched in Fig. 3. A visiondata system evaluates the status of the baking pro-cess on the basis of the biscuits color. The outputis a real number defined in the following as “BakingStatus” (BS ), indicating the average cooking statusof the biscuits. The variable BS is used by the con-trol system to impose a proper set-point for the oventemperature. Owing to the plant characteristics, anadaptive control scheme scheme is required, governedby a fuzzy supervisor whose purpose is to select themost appropriate control strategy depending on theworking conditions. The video camera used to ac-quire the biscuit status is located at the outlet of theoven (see Fig. 1). We conventionally pose BS ∈ R:when BS = 0 the biscuits are considered well cooked,while BS > 0 and BS < 0 indicates overcooked andundercooked biscuits respectively. The purpose of thefeedback controller is to guarantee, by controlling the

2



Figure 4: a) Cookies with different baking statuses;b) Damaged cookies.

burner temperature of the last section, that the bak-ing status is always as close as possible to zero.

3 Baking status estimation via

visual data processing.

The main element of the vision system is a color dig-ital line scanning camera equipped with a specificoptics. The line scanning camera is placed abovethe oven belt. Its visual range covers the full bandtransversal section, so that it can inspect simultane-ously an entire row of biscuits. Biscuits are illumi-nated with a special light source in order to mini-mize the influence of the external undesired lighten-ing. The acquired data are analyzed by means of aDigital Signal Processing (DSP) board which evalu-

ates the baking status of the biscuits. The elabora-tion results are downloaded to a supervisory PersonalComputer through a fast serial link.

The color vision system has been completely devel-oped and manufactured by ATE. The use of a colorcamera is justified with the same reasonings reportedin [7]. Due to the use of color it is possible to senseeven small changes in the baking status, a relevantfeature in order to use the information in a closedloop control.

The vision system can inspect all biscuits on thebelt, so that it can detect any possible defect of eachbiscuit: nonuniform baking, wrong shape, scraps (seee.g. fig. 4). This information will be used to drive anautomatic discard system. The baking status signalBS for automatic closed loop control is obtained byaveraging the baking status of the biscuits. Thereare several reasons for using an averaged estimate of the baking status. Biscuit placed laterally on the con-veyer are normally more baked than biscuits placed inthe middle: this is typical for gas driven baking lines.The transversal averaging eliminates this problem.Moreover the baking status is also averaged alongthe longitudinal section of the oven in order to filterthe measurement noise.

BS

T

f T ( ; )h

0

1

-1

Figure 5: The static baking status as a function of the temperature.

4 The control strategy.

The controlled system (i.e. the backing process) is

nonlinear and is influenced by a large number of fac-tors: some of them depend on human actions (valvespositions, band velocity) while others depend on en-vironmental conditions (external temperature, pres-sure, humidity). These factors can be quantifiedby means of real numbers collected into a vectorh ∈ H := [h−1 , h+

1 ] × · · · × [h− p , h+ p ] ⊂ R p, where p

indicates the number factors influencing the process.The system behavior is uncertain since it depends onh. It is reasonable to suppose that the baking processis stable: given the oven temperature T and the en-vironmental factors h, the baking status BS always

converges to a finite value. Thus, it is possible torepresent the static behavior of BS by means of thefollowing function

f : T × H → R

(T ; h) → f (T ; h)

where T := [T −, T +] ⊂ R is the range of temper-atures used in the last section of the oven. Severalcharacteristics of the baking function f (T ; h) can beassumed owing to simple physical reasonings.

Assumption 1 The baking function f (T ; h) is con-

tinuous with its first derivative, i.e. f ∈ C 1(T × H).Moreover, for any assigned h ∈ H, it is monotoni-cally increasing with respect to T

∂f

∂T > 0 ∀T ∈ T . (1)

The baking function f (T ; h) is shown in Fig. 5 bymeans of a family of nonlinear functions dependingon h.

The function f (T ; h) is normally unknown: toidentify f (T ; h) it should be necessary to burn outor undercook a large amount of product and this is

3



BS

T

f T ( ; )h

0

1

-1

T 0

T 1

T 2

T 3

Figure 6: The first strategy to approach the optimalburner temperature.

unacceptable for economic reasons. Thus, the ovencontroller must robustly converge to BS = 0 indepen-

dently from the knowledge of the function f (T ; h).The controller proposed aims to mimic the human be-havior. If the baking status is different from zero theburner temperature is appropriately changed. Then,the controller monitors the baking status to detectthe end of the transient: new changes of the burnertemperature are allowed only when a new (almost)stable state is reached. The purpose is to gain thecondition BS = 0 passing through a sequence of static states.

Two possible strategies are proposed in the follow-ing to approach the condition B S = 0 by evaluating,through a recursive algorithm, the correct burner set-

point. This optimal set-point is, theoretically, the so-lution of the equation f (T ; h) = 0 evaluated for anyvalue of h ∈ H and, as a consequence, it dependson the parameters h. In the following the optimalset-point will be indicated as T ∗(h).

Strategy 1 - Single point approach.

Given an initial burner temperature T i and the cor-responding steady-state baking status BS i, the sub-sequent set-point T i+1 is evaluated by means of thefollowing equation

T i+1 =

T i −

BS iK .

(2)

where K is an average slope of the baking functionevaluated in the neighborhood of the point whereBS = 0. More precisely K is evaluated during thenormal operation of the band oven by averaging theslopes corresponding to several values of h. The ap-proaching strategy is shown in Fig. 6.

Strategy 2 - Two points approach.

The last two burner set-points (T i, T i−1) andthe corresponding measured baking statuses(BS i, BS i−1) are required to evaluate the next

BS

T

f T ( ; )h

0

1

-1

T 0

T 1

T 2

T 3

T 4

Figure 7: The second strategy to approach the opti-mal burner temperature.

burner set-point according to the equation

T i+1 = T

i −

T i−1 − T i

BS i−1 − BS iBS

i . (3)

The converging evolution of this strategy is shownin Fig. 7.

There are several reasons to introduce two differentsearching strategies for the optimal burner tempera-ture T ∗. The first strategy robustly approach the op-timal set-point value (it will be demonstrated in the

following that an appropriate selection of K guaran-tees with certainty the convergence to T ∗) but theconvergence rate can be slow. The second strategyspeed up the algorithm convergence but has several

drawbacks. For example, if T i and T i−1 are too farfrom T ∗ the algorithm can diverge. Moreover, equa-tion (3) cannot be used if T i = T i−1 (this happens, forexample, after the algorithm has converged to T ∗) orwhen a single point is available (this happens whenthe searching algorithm firstly starts).

The actual burner set-point is “decided” by thefuzzy supervisor. At each iteration the supervisor an-alyzes the set-point candidates proposed by the twoapproaching strategies. Then it suggests, as the ac-tual set-point, a value obtained by weighting the twocandidates. Depending on the working conditions,the slow but safe Strategy 1 can or cannot be pre-ferred to the fast but risky Strategy 2.

The robustness of the first strategy is proved in thefollowing by verifying the existence of a K (h) ableto guarantee the convergence with certainty of thealgorithm to the optimal burner temperature T ∗(h).Some preliminary assumptions have to be considered.The baking function f (T ; h) is affected by h whichis a vector of slowly varying parameters. It will beassumed that h does not change until the algorithmhas converged to T ∗(h). Moreover, it will be assumedthat a solution T ∗(h) such that f (T ∗(h); h) exists.This is not an obvious condition because if the first

4



n − 1 ovens are not working correctly, it can hap-pen that f (T ; h) > 0 or f (T ; h) < 0 for all T ∈ T .Finally, the baking function f (T ; h) satisfies the As-sumption 1. For this reason it is possible to assertthat the partial derivative of f (T ; h) with respect to

T is bounded over the compact interval T . Thus, forany h ∈ H, it is possible to define

K (h) := maxT ∈T

∂f

∂T

. (4)

It is possible to guarantee with certainty the con-vergence of the Strategy 1 toward the optimal bakingtemperature T ∗(h) by by means of a proper selection

of the parameter K .

Property 1 Let us consider a baking function f (T ; h) satisfying Assumption 1, where h is a vec-tor of constant parameters affecting the plant be-havior. Moreover assume that an optimal solution T ∗(h) such that f (T ∗(h); h) = 0 exists and choose K := maxh∈H K (h). Then, Strategy 1 converges with certainty to T ∗(h) for any given h ∈ H.

Proof - The vector h is supposed to be constant. Forthis reason it will be omitted along this proof. Selecta starting temperature T i < T ∗. It will be demon-strated in following that Strategy 1 converges fromthe left to the optimal baking temperature T ∗. Simi-lar reasonings permit asserting that an approach fromthe right is obtained for any starting point T i > T ∗.

The updating equation (2) can be rewritten usingthe baking function

T i+1 = T i − f (T i)K

. (5)

Obviously f (T i) < 0 (remember that f (T i) = 0 if and

only if T i = T ∗) and K > 0 so that for any T i < T ∗

it is possible to write T i+1 > T i. Moreover, by virtueof (5) the following inequality holds

f (T i+1) = f (T i) +

T i+1T i

∂f

∂T (τ )dτ

≤ f (T i) + T i+1T i

Kdτ

= f (T i) + K (T i+1 − T i) = 0 .

Since f (T i+1) ≤ 0 and T i+1 > T i, it is possible toconclude that Strategy 1 generates a succession of temperature set-points T i monotonically increasingbut always placed on the left of T ∗. A well knownresult of the analysis permits asserting that such suc-cession must converge to a finite value T c. Thus, thesuccession satisfies the Cauchy condition

limi→∞

|T i+1 − T i| = 0 (6)

Taking again into account (5), it is possible to write

limi→∞

|T i+1 − T i| = limi→∞

f (T i)

K

= f (T c)

K

= 0 (7)

and conclude that, evidently, T c = T ∗.

Remark 1 In the Property 1 the parameter vector h is supposed to be constant. This is not a lim-iting condition since a part of the parameters in h

changes slowly (meteorological conditions) while oth-ers change suddenly but rarely (valves position): in both cases the control algorithm has enough time togain the optimal temperature T ∗.

Remark 2 By selecting K according to Property 1and using Strategy 1 the optimal temperature T ∗ is gained with certainty with the drawback of a slow con-vergence rate. For this reason, in the actual applica-tion K is obtained by averaging values of ∂f

∂T (T ∗) col-

lected in previous runs of the algorithm. The choice is sufficiently safe and, in any case, non converging be-haviors can be easily corrected by increasing the cur-rent value of K .

The overall control algorithm is based on the com-bination of the two searching strategies and can besummarized as follows

1. T ∗-init(T ∗); T old ← T ∗;

2. Equilibrium procedure (BS old);

3. Strategy 1 procedure ( T ∗

); T new ← T ∗

;4. Equilibrium procedure (BS new);

5. Repeat

6. T ∗-update( T ∗);

7. T old ← T new; BS old ← B S new;

8. Equilibrium procedure (BS new);

9. T new ← T ∗;

10. Until Stop

The procedure T ∗-init evaluates the initial ovenset-point temperature T ∗ on the basis of h. It uses afunction

T ∗(h) estimated during previous runs of the

controller. The equilibrium procedures are used eachtime the oven set-point is changed in order to waituntil the end of the thermal transients. Finally, theT ∗-update procedure evaluate the new T ∗ by prop-erly combining the set-point proposal of the twostrategies.

5 Simulation results.

The control strategy proposed in the previous sectionhas been simulated by means of Simulink. The dy-namic baking model [2] used for the simulations takes

5



0 500 1000 1500 2000 2500 3000 3500-0.2

-0.15

-0.1

-0.05

0

B S

[s]

Strategy 1

Strategy 1

Strategy 1 & Strategy 2

Figure 8: Comparison between transient times: thesimultaneous use of both Strategy 1 and 2 (contin-

uous line) permits shorter transient than the use of the sole Strategy 1 (dashed line).

into account a lot of factors that normally influencethe cooking process. For example, the oven thermaltransients are accurately modelled as well as severalnonlinear effects that are typical of the baking pro-cess [5]. The model is time variant. Figure 8 shows atypical transient and evidences the benefits derivingfrom the double strategy proposed. In a first simu-lation (dashed line) the condition BS = 0 is gainedby using exclusively the Strategy 1 while the com-bined approach proposed in the previous section wasused for the second simulation (continuous line). Bycomparing the two responses, it is possible to ob-serve that, at the beginning, the two transients coin-cide since both controllers use Strategy 1. Then, thefuzzy supervisored controller start using also Strat-egy 2: the status B S = 0 is reached faster.

6 Conclusions.

In the paper a strategy for the control of a contin-uous band oven has been proposed. A feedback ac-tion based on the visual inspection of the biscuits hasbeen used to evaluate the most appropriate burnertemperature. The simulation results reported in § 5show that the proposed controller is able to evalu-ate the optimal burner temperature with a limitednumber of attempts. This is a very relevant featurein order to reduce the product scraps. Moreover thecontroller can robustly handle the variability typicalof the baking processes.

Acknowledgments

The authors wish to acknowledge Renzo Santi, Fed-erico Ghirelli, Roberto Martini and Daniela Oliviero(Colussi S.p.A.) and Guy Lemstrom (ATE) for their

precious collaboration. A special thank to OtelloMazzoni (“The lord of the ovens”- Colussi S.p.A.)for his witty descriptions of the oven behaviors.

References

[1] Gao, Z., Trautzsch, T. and Dawson, J.: 2002, Astable self-tuning fuzzy logic control system forindustrial temperature regulation, IEEE Trans-actions on Industry Applications 38(2), 414–424.

[2] Guarino Lo Bianco, C., Romano, M. and Piazzi,A.: 2002, A nonlinear dynamic model for thebaking process in continuos band ovens, Techni-cal Report TSC-01/02 , University of Parma.

[3] Jikai, Y., Hongping, Y., Hongtao, S. and Yuan-bin, H.: 1997, Fuzzy control technique based ongenetic algorithms optimizing and its applica-tion, IEEE International Conference on Intelli-gent Processing Systems, ICIPS97 , Vol. 1, Bei- jing, China, pp. 329–333.

[4] Perrot, N., Bonazzi, C., Trystram, G. and Guely,

F.: 1999, Estimation of the food product qual-ity using fuzzy sets, 18th International Confer-ence of the North American Fuzzy Information Processing Society, NAFIPS , New York, NY,pp. 487–491.

[5] Trysram, G., Fahoul, D., Duquenoy, A. and Al-lache, M.: 1993, Dynamic modelling and simula-tion of the biscuit baking oven process, Comput-ers Chemical Engineering 17(supp), 203–208.

[6] Xinxin, Y., Lei, Y., Kezhong, H., Shengle, H.,Muhe, G. and Bo, Z.: 1996, A double-level fuzzycontroller with an intelligently adjusting strat-egy of quantization and scale factors, IEEE In-ternational Conference on Systems, Man, and Cybernetics , Vol. 1, Beijing, China, pp. 280–285.

[7] Yeh, J., Hamey, L., Westcott, T. and Sung, S.:1995, Colour bake inspection system using hy-brid artificial neural networks, Proceedings of the IEEE International Conference on Neural Net-works , Vol. 1, Perth, Western Australia, pp. 37–42.

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control de retroalimentacion visual de un horno de bandas industrial

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