Three Levels Control System
Yuri V. Mitrishkin
Bauman Moscow State Technical University, Second Baumanskaya St.,5, Moscow, Russia
Rodolfo Haber Guerra
Instituto de Automática Industrial, CSIC, Madrid, Spain
Keywords: Complex dynamic systems, Feedback, Hierarchical, Robust, Adaptive, Self-organizing, Decision making,
Intelligent control, Plasma in tokamaks.
Abstract: The paper presents a concept of intelligent hierarchical control for complex dynamical processes and
suggests architecture of control system consisting of robust, adaptive, and self-organizing levels. Intelligent
features of the proposed system are mostly concentrated at self-organizing level incorporated into self-
learning, self-configuring, self-optimizing, and decision making algorithms. State-of-the-art at each level is
described. Case studies have been chosen from the area of plasma control in tokamak-reactors.
Recent advances in control strategies,
communications, hard and soft-computing
technologies have favoured an increasing trend
towards the new generation of networked control
systems for complex processes. The proposal
described herein will address the development of
scalable control methods and systems in accordance
with the Information and Communication
Technologies (ICT) Work Programme, ICT-2009
3.5a: Foundations of complex systems engineering:
To achieve robust, predictable and self-adaptive
behavior for large-scale networked systems
characterized by complex dynamic behavior through
the development of novel abstractions and scalable
methods for sensing, control and decision-making.
The scope covers foundational multi-disciplinary
research and proof of concept addressing the whole
chain from modeling, sensing, monitoring and
actuation, to adaptive and cooperative control and
decision making (European Commission, 2008).
To meet the goal stated by the EC a three level
intelligent hierarchical control system was suggested
by Bauman Moscow State Technical University to
be applied to solve control problems of complex
dynamic processes in science, engineering, and
The project is focused on design and
development of scalar (Single-Input/Single-Output:
SISO) and multivariable (Multi-Input/Multi-Output
MIMO) networked control systems based on
scalable control algorithms for uncertain time-
varying nonlinear complex dynamic processes.
The major innovation of the proposal implies the
elaboration of a new methodology for designing
hierarchical adaptive self-organizing control systems
to be applied to complex production processes, such
as: plasma energy release, chemical and biological
processes, casting in metallurgy, oil refinery, and so
Industry and academia have investigated a wide
range of decentralized control architectures ranging
from hierarchical decomposition to a completely
decentralized (heterarchical) approach where
individual controllers are assigned to subsystems
and may work independently or may share
V. Mitrishkin Y. and Haber Guerra R.
DOI: 10.5220/0002171003330336
In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), page
ISBN: 978-989-8111-99-9
2009 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The main disadvantage of heterarchical
approaches is that global optima cannot be
guaranteed and predictions of the system’s
behaviour can only be made at the aggregate level.
Hierarchical and heterarchical architectures lie at
opposite ends of the distributed control architectures
spectrum. The hierarchical approach is rigid and
suffers from many of the shortcomings of the
centralized approach, whereas it provides clear
advantages in terms of overall system coordination
alternatively. The heterarchical approach is flexible
and fault-tolerant, but arguably difficult to
Hierarchical control and supervision schemes
have been widely studied as a possible solution for
optimizing complex systems. A variety of schemes
can be implemented in order to profit from the
advantages of this architecture, and the applications
run all the way from fault-tolerant aircraft control
problems to servo systems supervision (Kwong et
al., 1995).
Hierarchical control allows any available data
from the low-level control system to be used at a
higher level to characterize the system’s current
behaviour. Moreover, hierarchical control can be
used to integrate extra information (in addition to
that concerning the usual control-loop variables such
as output, error, etc.) into the control decision-
making process. In many situations a hierarchical
approach is an advantageous option for process
optimization, instead of sophisticated design and
implementation of high-performance low-level
controllers, because the hierarchical approach can
compensate factors that are not taken into account in
the design of low-level controllers (Berenji et al.,
Thanks to its own structural essence, the
hierarchical control scheme ensures flexibility and
compatibility with other controllers that have
already been installed. It has other strong points as
well, such as the relatively low cost of investments
in improving automation scheme performance, the
possibility of exploiting already-installed low-level
regulation systems, and the relatively low cost of
measurement systems which makes hierarchical
control a wise choice from economic and practical
The hierarchical methodology will cover three
basic levels, namely:
I) physical control level composed of controlled
process interacting with robust or classical controller
through sensors and actuators;
II) adaptive level that implements scalable
adaptation algorithms and enables the robust
controller to satisfy a number of time-varying
III) knowledge-based self-organizing level
which executes a set of self-learning, self-
configuring, self-optimizing, and decision-making
algorithms allowing possible dynamic changes in the
system architecture aimed at optimal control and
dynamic reconfiguration of the robust controller and
making it easier to satisfy process-critical
constraints, such as respond to the deadlines,
saturations and so on.
Advanced algorithms designed for all control
levels I, II, III and their interactions provide a high
degree of system flexibility, robustness, accuracy,
reliability, dependability, and survivability to deal
with disruptive, uncertain, and unforeseen events in
automatic mode (without human intervention).
The philosophy of the three-levels intelligent
hierarchic control system proposed is a strategy of
the project to be organized. Complex processes
under control should be first of all thoroughly
investigated, then classical or robust control systems
are to be developed. After that, improvement of
lower level should be done by means of levels II and
III including decision making approach which may
be performed by experts at the beginning of system
design and not in automatic mode.
The design of the main SISO and MIMO control
loops should be based on, in a certain sense,
classical approaches which have been used in
modeling and practice and demonstrated efficacy in
a number of applications. These approaches are
based on design principles for which reliable
dynamic processes models are created by means of
First Principle Equations methodology
(Khayrutdinov et al., 1993, Leonov et al., 2005),
identification (Mitrishkin, 2004), linearization and
subsequent reduction procedure. The resulting real
world models have been used in system closed-loop
to predict optimal control actions at each discrete
timing interval to achieve the control goal taking
into account input constraints (Mitrishkin and
Korostelev, 2008). Decoupling control leads to the
design of astatic multivariable systems (Leonov et
al., 2005). A number of optimization approaches,
specifically off-line Linear Quadratic techniques
(Belyakov et al., 1999),
and μ synthesis
ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics
(Mitrishkin et al., 2003, Ariola and Pironti, 2008) as
well as online automatic optimization of extremum
search (Mitrishkin, 2004), gave acceptable results.
Adaptive Kalman filter made it possible to estimate
process parameters in real time and gave a chance to
adapt the system by adaptive algorithm to time-
varying process parameters (Mitrishkin and
Kuznetsov, 1993). As this took place, an external
disturbance was estimated and estimation value was
used to compensate the disturbance itself
(Mitrishkin, 2004).
Some results in the topics in relation to this
proposal are the design and implementation of:
intelligent hierarchical control and supervisory
systems (Peres et al., 1999); control strategies by
fuzzy, neural, neuro-fuzzy, and evolutionary neuro-
fuzzy internal model control systems (Haber et al.,
2004); rapid control prototyping for networked and
embedded control (Haber et al., 2008); embedded
intelligent control systems in open architectures
(Haber and Alique, 2007), and networked control and
intelligent monitoring for macro- and micro-scale
manufacturing processes (Haber et al., 2008).
The synthesis of intelligent hierarchical control
system (Andrikov and Konykov, 2004) was applied
to improve car braking controllability by H
A number of new important complex control
problems have to be studied, discussed and
formulated to achieve control goals of acceptable
trade-off between robust stability and performance
of feedback systems. The problem statements
concern the stabilization and tracking process output
signals, optimal distribution of process parameters in
space in the presence of non-modeled process
dynamics, unobserved disturbances, nonlinearities,
in particular saturations, wideband insufficiently
known noise in output signals, non-minimum-phase
dynamics, and time-varying parameters. To solve
these control problems a set of approaches from
linear and nonlinear control theory will be explored
and developed to achieve scalable
decoupling, model predictive, adaptive, hierarchy,
cascade, soft-computing based control (e.g., neuro-
fuzzy control systems), and facilitate decision-
making in new appropriate combinations within
continuous and discrete time of the three-level
hierarchic control system. Scalable control
algorithms mean that the algorithms may be
generalized to any numbers of controlled plant
inputs, outputs, and space states.
In order to validate the suggested approaches plasma
energy release case study is planned to be
investigated. Control methodologies will be applied
to plasma vertically unstable position, shape, and
current in the presence of voltages and current
saturations in poloidal magnetic coils in ITER
(International Thermonuclear Experimental Reactor, Multivariable robust controller
design (level I) with adaptation on the level II is
proposed to be done for the whole plasma discharge
of plasma current ramp-up, ramp-down, and at quasi
stationary stages. It is planned to be applied for
ITER reference scenario No 2 with plasma current
on flat-top of 15 MA and for reversed share scenario
No 4 of plasma current of 9 MA. Mathematical
modeling of control systems to be developed on
plasma-physics code DINA (Khayrutdinov et al.,
1993) is assumed to be fulfilled in tracking and
stabilizing modes at disturbances of minor
disruption type.
The project control methodologies are planned to
be applied to solve plasma kinetic control problem
as well. Plasma kinetic control means creation and
maintenance of optimal plasma current, temperature,
and density profiles by means of additional heating
sources. Such regimes are necessary for stationary
operation of tokamak-reactors. Development of
kinetic plasma models of plasma current,
temperature, and density profiles and their
identification are supposed to be created. Then
design and modeling of plasma profiles control
systems are assumed to be performed.
The final issue of this activity is integration of
plasma magnetic and kinetic control systems.
In the process of hierarchical structure control
systems design the synthesis, analysis, and
numerical modeling approaches are proposed to be
performed in MATLAB/SIMULINK environment.
The ITER functionality is planned to be
performed by means of CODAC software
specifically Control, Data Access, and
Communications ( via which one can
install hierarchical control scheme proposed.
The concept of three levels hierarchic control system
was presented and discussed namely: architectural
details, state-of-the-art, statement of control
problems, and practical applications.
The project will result in the creation of new
process models, procedures of their identification
and reduction, efficient, robust, predictable, and safe
ICT control methodologies, scalable control
algorithms, and high-performance controllers with
reconfigurable architecture for the problem oriented
hierarchical systems under consideration. Scientific,
engineering, and industrial results will be
accumulated in the data and knowledge bases with
accurate classification, qualitative and quantitative
assessment, and generalization.
Albus J. S., 1991. Outline for a theory of intelligence,
IEEE Trans. Syst., Man, Cybern. A Vol. 21 (3), pp.
Andrikov D., Konykov V., 2004. Robust Н
controller for car with ABS in emergency situation in
slip mode. Herald of BMSU, Instrument engineering
series. Vol. 57, No. 4, pp. 44–57 (in Russian).
Ariola M., Pironty A., 2008. Magnetic Control of
Tokamak Plasmas. Springer-Verlag.
Belyakov V., Kavin A., Kharitonov V., Misenov B.,
Mitrishkin Y. et al. 1999. Linear Quadratic Gaussian
Controller Design for Plasma Current, Position and
Shape Control System in ITER, Fusion Engineering
and Design, Vol. 45, pp. 55-64.
Berenji H. R., Chen Y.-Y., Lee C.-C., Yang J.-S.,
Murugesan S., 1991. A hierarchical approach to
designing approximate reasoning-based controllers for
dynamic physical systems, in Uncertainty in Artificial
Intelligence Vol. 6, pp. 331-343, 1991.
European Commission C (2008)6827, 17 November 2008.
Work Programme 2009. Cooperation, Theme 3. ICT –
Information and Communication Technologies, p. 38.
Haber R. E., Alique J. R., 2004. Nonlinear internal model
control using neural networks: an application for
machining processes. Neural Computing &
Applications, vol. 13, pp. 47-55.
Haber R.E., Villena P., Haber-Haber R., Alique J.R.,
2008. Fast design and implementation of intelligent
controllers. DYNA, vol. 83 (8), pp. 127-134.
Haber R. E., Alique J. R., 2007. Fuzzy logic-based torque
control system for milling process optimization. IEEE
Trans. on Systems Man and Cybernetics. Part C-
Applications and Reviews, vol. 37, pp. 941-950.
Haber R. E., Martin D., Haber-Haber R., Alique A., 2008.
Networked fuzzy control system for a high-
performance drilling process. Journal of
Manufacturing Science and Engineering-Trans. of the
ASME, vol. 130, pp. 68-75.
Khayrutdinov R.R., Lukash V.E., 1993. Studies of Plasma
Equilibrium and Transport in a Tokamak Fusion
Device with the Inverse-Variable Technique. Journal
Comp. Physics, Vol. 109, pp. 193–201.
Kwong W. A., Passino K.M., Laukonen E.G., Yurkovich
S., 1995. Expert supervision of fuzzy learning systems
for fault tolerant aircraft control, Proc. of IEEE Vol.
83 (3), pp. 466-483.
Leonov V., Mitrishkin Y., Zhogolev V., 2005. Simulation
of Burning ITER Plasma in Multi-Variable Kinetic
Control System. Proc. of 32nd Plasma Physics Conf.
of European Physics Society, Tarragona, Spain, ID
Mitrishkin Y., Kuznetsov E., 1993. Estimation of
Parameters of Stabilized Plasma. Plasma Devices and
Operations, No. 3, Vol. 2, pp. 277-286.
Mitrishkin Y., Kurachi K., Kimura H., 2003. Plasma
multivariable robust control system design and
simulation for a thermonuclear tokamak-reactor,
International Journal of Control, Vol. 76, No. 13, pp.
Mitrishkin, Y., 2004. Comprehensive Design and
Implementation of Plasma Adaptive Self-Oscillations
and Robust Control Systems in Thermonuclear
Installations. Proc. of 8
World Multi-Conference on
Systemics, Cybernetics and Informatics, Orlando, FL,
USA, Vol. XV, pp. 247-252.
Mitrishkin Y., Korostelev A., 2008. System with
Predictive Model for Plasma Shape and Current
Control in Tokamak. Control Sciences, No.5, pp. 22-
34 (in Russian).
Peres C. R., Haber R. E., Haber R. H., Alique A., Ros S.,
1999. Fuzzy model and hierarchical fuzzy control
integration: an approach for milling process
optimization. Computers in Industry, vol. 39, pp. 199-
ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics