29,886 research outputs found
Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms
This paper deals with the estimation of unknown
signals in bioreactors using sliding observers. Particular
attention is drawn to estimate the specific growth rate of
microorganisms from measurement of biomass concentration.
In a recent article, notions of high-order sliding modes have
been used to derive a growth rate observer for batch processes.
In this paper we generalize and refine these preliminary results.
We develop a new observer with a different error structure to
cope with other types of processes. Furthermore, we show that
these observers are equivalent, under coordinate transformations
and time scaling, to the classical super-twisting differentiator
algorithm, thus inheriting all its distinctive features.
The new observers’ family achieves convergence to timevarying
unknown signals in finite time, and presents the best
attainable estimation error order in the presence of noise. In
addition, the observers are robust to modeling and parameter
uncertainties since they are based on minimal assumptions
on bioprocess dynamics. In addition, they have interesting
applications in fault detection and monitoring. The observers
performance in batch, fed-batch and continuous bioreactors is
assessed by experimental data obtained from the fermentation
of Saccharomyces Cerevisiae on glucose.This work was supported by the National University of La Plata (Project 2012-2015), the Agency for the Promotion of Science and Technology ANPCyT (PICT2007-00535) and the National Research Council CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain; and by the project FEDER of the European Union.De Battista, H.; Picó Marco, JA.; Garelli, F.; Navarro Herrero, JL. (2012). Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess and Biosystems Engineering. 35(9):1-11. https://doi.org/10.1007/s00449-012-0752-yS111359Aborhey S, Williamson D (1978) State amd parameter estimation of microbial growth process. Automatica 14:493–498Bastin G, Dochain D (1986) On-line estimation of microbial specific growth rates. Automatica 22:705–709Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier, AmsterdamBejarano F, Fridman L (2009) Unbounded unknown inputs estimation based on high-order sliding mode differentiator. In: Proceedings of the 48th IEEE conference on decision and control, pp 8393–8398Corless M, Tu J (1998) State and input estimation for a class of uncertain systems. Automatica 34(6):757–764Dabros M, Schler M, Marison I (2010) Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass. Bioprocess Biosyst Eng 33:1109–1118Davila A, Moreno J, Fridman L (2010) Variable gains super-twisting algorithm: a lyapunov based design. In: American control conference (ACC), 2010, pp 968–973Dávila J, Fridman L, Levant A (2005) Second-order sliding-mode observer for mechanical systems. IEEE Transact Automatic Control 50(11):1785–1789De Battista H, Picó J, Garelli F, Vignoni A (2011) Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. J Process Control 21:1049–1055Dochain D (2001) Bioprocess control. Wiley, HobokenDochain D (2003) State and parameter estimation in chemical and biochemical processes: a tutorial. J Process Control 13(8):801–818Edwards C, Spurgeon S, Patton R (2000) Sliding mode observers for fault detection and isolation. Automatica 36(2):541–553Evangelista C, Puleston P, Valenciaga F, Fridman L (2012) Lyapunov designed super-twisting sliding mode control for wind energy conversion optimization. Indus Electron IEEE Transact. doi: 10.1109/TIE.2012.2188256Farza M, Busawon K, Hammouri H (1998) Simple nonlinear observers for on-line estimation of kinetic rates in bioreactors. Automatica 34(3):301–318Fridman L, Davila J, Levant A (2008) High-order sliding modes observation. 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Bioprocess Eng 23(4):337–341Kiviharju K, Salonen K, Moilanen U, Eerikainen T (2008) Biomass measurement online: the performance of in situ measurements and software sensors. J Indus Microbiol Biotechnol 35(7):657–665Levant A (1998) Robust exact differentiation via sliding mode technique. Automatica 34(3):379–384Levant A (2003) Higher-order sliding modes, differentiation and output-feedback control. Int J Control 76(9/10):924–941Lubenova V, Rocha I, Ferreira E (2003) Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses. Bioprocess Biosyst Eng 25:395–406Moreno J, Alvarez J, Rocha-Cozatl E, Diaz-Salgado J (2010) Super-twisting observer-based output feedback control of a class of continuous exothermic chemical reactors. In: Proceedings of the 9th IFAC international symposium on dynamics and control of process systems, pp 719–724. Leuven, BelgiumMoreno J, Osorio M (2008) A Lyapunov approach to second-order sliding mode controllers and observers. In: Proceedings of the 47th IEEE conference on decision and control. Cancún, México, pp 2856–2861Moreno J, Osorio M (2012) Strict Lyapunov functions for the super-twisting algorithm. IEEE Transact Automatic Control 57:1035–1040Navarro J, Picó J, Bruno J, Picó-Marco E, Vallés S (2001) On-line method and equipment for detecting, determining the evolution and quantifying a microbial biomass and other substances that absorb light along the spectrum during the development of biotechnological processes. Patent ES20010001757, EP20020751179Neeleman Boxtel (2001) Estimation of specific growth rate from cell density measurements. Bioprocess Biosyst Eng 24(3):179–185November E, van Impe J (2002) The tuning of a model-based estimator for the specific growth rate of Candidautilis. Bioprocess Biosyst Eng 25:1–12Park Y, Stein J (1988) Closed-loop, state and input observer for systems with unknown inputs. Int J Control 48(3):1121–1136Perrier M, de Azevedo SF, Ferreira E, Dochain D (2000) Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters. Control Eng Pract 8:377–388Picó J, De Battista H, Garelli F (2009) Smooth sliding-mode observers for specific growth rate and substrate from biomass measurement. J Process Control 19(8):1314–1323. Special section on hybrid systems: modeling, simulation and optimizationSchenk J, Balaszs K, Jungo C, Urfer J, Wegmann C, Zocchi A, Marison I, von Stockar U (2008) Influence of specific growth rate on specific productivity and glycosylation of a recombinant avidin produced by a Pichia pastoris Mut + strain. Biotecnol Bioeng 99(2):368–377Shtessel Y, Taleb M, Plestan F (2012) A novel adaptive-gain supertwisting sliding mode controller: Methodol Appl Automatica (in press)Soons Z, van Straten G, van der Pol L, van Boxtel A (2008) On line automatic tuning and control for fed-batch cultivation. Bioprocess Biosyst Eng 31(5):453–467Utkin V, Poznyak A, Ordaz P (2011) Adaptive super-twist control with minimal chattering effect. In: Proceedings of 50th IEEE conference on decision and control and European control conference. Orlando, pp 7009–7014Veloso A, Rocha I, Ferreira E (2009) Monitoring of fed-batch E. coli fermentations with software sensors. Bioprocess Biosyst Eng 32(3):381–388Venkateswarlu C (2004) Advances in monitoring and state estimation of bioreactors. J Sci Indus Res 63:491–498Zamboni N, Fendt S, Rühl M, Sauer U (2009) 13c-based metabolic flux analysis. Nat Protocols 4:878–892Zorzetto LFM, Wilson JA (1996) Monitoring bioprocesses using hybrid models and an extended kalman filter. Comput Chem Eng 20(Suppl 1):S689–S69
Robust exact differentiators with predefined convergence time
The problem of exactly differentiating a signal with bounded second
derivative is considered. A class of differentiators is proposed, which
converge to the derivative of such a signal within a fixed, i.e., a finite and
uniformly bounded convergence time. A tuning procedure is derived that allows
to assign an arbitrary, predefined upper bound for this convergence time. It is
furthermore shown that this bound can be made arbitrarily tight by appropriate
tuning. The usefulness of the procedure is demonstrated by applying it to the
well-known uniform robust exact differentiator, which the considered class of
differentiators includes as a special case
Design and frequency analysis of continuous finite-time-convergent differentiator
In this paper, a continuous finite-time-convergent differentiator is
presented based on a strong Lyapunov function. The continuous differentiator
can reduce chattering phenomenon sufficiently than normal sliding mode
differentiator, and the outputs of signal tracking and derivative estimation
are all smooth. Frequency analysis is applied to compare the continuous
differentiator with sliding mode differentiator. The beauties of the continuous
finite-time-convergent differentiator include its simplicity, restraining
noises sufficiently, and avoiding the chattering phenomenon
Function based control for bilateral systems in tele-micromanipulation
Design of a motion control system should take into
account (a) unconstrained motion performed without interaction
with environment or any other system, and (b) constrained
motion with system in contact with environment or other systems.
Control in both cases can be formulated in terms of maintaining
desired system configuration what makes essentially the same
structure for common tasks: trajectory tracking, interaction force
control, compliance control etc. The same design approach can be
used to formulate control in bilateral systems aimed to maintain
desired functional relations between human and environment
through master and slave motion systems. Implementation of
the methodology is currently being pursued with a custom built
Tele-micromanipulation setup and preliminary results concerning
force/position tracking and transparency between master and
slave are clearly demonstrated
Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes
Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure
Channel exist. Random Linear Codes (RLC), where redundancy packets consist of
random linear combinations of source packets over a certain finite field, are a
simple yet efficient coding technique, for instance massively used for Network
Coding applications. However the price to pay is a high encoding and decoding
complexity, especially when working on , which seriously limits the
number of packets in the encoding window. On the opposite, structured block
codes have been designed for situations where the set of source packets is
known in advance, for instance with file transfer applications. Here the
encoding and decoding complexity is controlled, even for huge block sizes,
thanks to the sparse nature of the code and advanced decoding techniques that
exploit this sparseness (e.g., Structured Gaussian Elimination). But their
design also prevents their use in convolutional use-cases featuring an encoding
window that slides over a continuous set of incoming packets.
In this work we try to bridge the gap between these two code classes,
bringing some structure to RLC codes in order to enlarge the use-cases where
they can be efficiently used: in convolutional mode (as any RLC code), but also
in block mode with either tiny, medium or large block sizes. We also
demonstrate how to design compact signaling for these codes (for
encoder/decoder synchronization), which is an essential practical aspect.Comment: 7 pages, 12 figure
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