What can Systems and Control Theory do for Agricultural Science?

While many professionals with a background in agricultural and bio-resource sciences work with models, only few have been exposed to systems and control theory. The purpose of this paper is to elucidate a selection of methods from systems theory that can be beneficial to quantitative agricultural science. The state space representation of a dynamical system is the corner stone in the mainstream of systems theory. It is not well known in agro-modelling that linearization followed by evaluation of eigenvalues and eigenvectors of the system matrix is useful to obtain dominant time constants and dominant directions in state space, and offers opportunities for science-based model reduction. The continuous state space description is also useful in deriving truly equivalent discrete time models, and clearly shows that parameters obtained with discrete models must be interpreted with care when transferred to another model code environment. Sensitivity analysis of dynamic models reveals that sensitivity is time and input dependent. Identifiability and sensitivity are essential notions in the design of informative experiments, and the idea of persistent excitation, leading to dynamic experiments rather than the usual static experiments can be very beneficial. A special branch of systems theory is control theory. Obviously, control plays an important part in agricultural and bio-systems engineering, but it is argued that also agronomists can profit from notions from the world of control, even if practical control options are restricted to alleviating growth limiting conditions, rather than true crop control. The most important is the idea of reducing uncertainty via feed-back. On the other hand, the systems and control community is challenged to do more to address the problems of real life, such as spatial variability, measurement delays, lacking data, environmental stochasticity, parameter variability, unavoidable model uncertainty, discrete phenomena, variable system structures, the interaction of technical ad living systems, and, indeed, the study of the functioning of life itself. [ABSTRACT FROM AUTHOR]

Iako mnogi profesionalci s područja agrotehnike i bio-znanosti rade s modelima, tek je nešto malo onih koji su izloženi uporabi teorije sustava i automatskog vođenja. Namjena je ovog rada raščišćavanje izbora metoda s područja teorije sustava koji mogu biti od koristi kvantitativnoj agronomskoj znanosti. Predstavljanje dinamičkih sustava prostorom stanja temeljno je usmjerenje teorije sustava. U agro-modelima je slabo poznato da je linearizacija, koju slijedi evaluacija svojstvenih vrijednosti i svojstvenih vektora matrice sustava, korisna pri dobivanju dominantnih vremenskih konstanti i dominantnih usmjeravanja u prostoru stanja te pri nuđenju mogućnosti znanstveno zasnovanog pojednostavljenja modela. Opis kontinuiranog prostora stanja također je koristan u dobivanju stvarno ekvivalentnih diskretno-vremenskih modela te jasno pokazuje da se dobiveni parametri diskretnih modela moraju pažljivo interpretirati kada se prenose u okoliš s drugim kodnim modelom. Analiza osjetljivosti dinamičkih sustava otkriva da je osjetljivost ovisna o vremenu i ulazima. Mogućnost identifikacije i osjetljivost bitni su pojmovi u projektiranju informativnih pokusa, a ideja trajne pobude koja vodi dinamičkim pokusima a ne uobičajenim statičkim pokusima može biti vrlo korisna. Posebna grana sustavske teorije jest teorija automatskog vođenja. Očigledno, vođenje igra važnu ulogu u agronomskom i bio-sustavskom inženjerstvu, ali se naglašava da agronomi mogu profitirati iz pojmova svijeta automatike, čak i ako je automatizacijska inačica ograničena na olakšavanje uvjeta ograničenja rasta, a ne i pravog vođenja usjeva. Najvažnija je ideja da se uporabom povratne veze smanji neodređenost. S druge strane zajednica je sustavskih inženjera i automatičara izazvana, da se više posveti stvarnim problemima života poput prostorne promjenjivosti, mjernih kašnjenja, nedostataka podataka, stohastičnosti okoliša, varijabilnosti parametara, nezaobilazne neodređenosti modela, fenomena diskretizacije, varijabilnosti sustavskih struktura, međudjelovanja tehničkih i živih sustava, i napose studiju funkcioniranja života kao takvog. [ABSTRACT FROM AUTHOR]

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