K.G. Arvanitis
Agricultural University of Athens
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by K.G. Arvanitis.
IEEE Control Systems Magazine | 2001
L.D. Albright; K.G. Arvanitis; A.E. Drysdale
Plant production systems have become more sophisticated. Climate control has changed over the past several decades from manual to digital operations, and control computers have become faster and more capable. The paper focuses first on the environment control of plant production in commercial greenhouses and plant growth chambers, and then contrasts that growing system with the needs for rather different control strategies to grow plants in space applications. To demonstrate key characteristics of greenhouse environment control, a nonlinear controller for coupled air temperature and humidity, with various methods of feedback and feedforward control appropriate for this system, is presented.
Computers and Electronics in Agriculture | 2000
K.G. Arvanitis; P.N. Paraskevopoulos; A.A. Vernardos
Abstract A new adaptive technique is proposed for the control of the temperature in a greenhouse whose parameters vary with operating conditions. The proposed adaptive controllers are derived by solving either the pole-placement or the linear quadratic regulation (LQR) problem and use multirate controllers in which the greenhouse temperature is sampled many times over a fundamental sampling period. On the basis of the proposed control scheme, the problem is reduced to an associated discrete-time problem for which constant state feedback controllers are derived. Thus, the proposed technique essentially becomes one of computing a constant gain controller rather than one of deriving state observers or output feedback controllers, as in other indirect adaptive control techniques. As a consequence, the exogenous dynamics introduced in the control loop is of low order. Furthermore, the proposed adaptive controllers can be designed to possess any degree of stability, since their transition matrices can be chosen arbitrarily. The proposed scheme estimates the unknown parameters of the greenhouse on-line from sequential data on the greenhouse temperature and the heating power, which are recursively updated. Persistency of excitation of the controlled system is assured without making assumptions on the system under control, other than controllability, observability and known order. Simulation results of the greenhouse dynamics illustrate the effectiveness of the proposed scheme.
IEEE Transactions on Control Systems and Technology | 2006
Paraskevas N. Paraskevopoulos; George D. Pasgianos; K.G. Arvanitis
The control of unstable first-order plus dead-time (UFOPDT) processes using proportional-integral (PI) and proportional-integral-differential (PID) type controllers is investigated in this brief. New tuning rules based on the exact satisfaction of gain and phase margin specifications are proposed. The tuning rules are given in the form of iterative algorithms, as well as in the form of accurate, analytical approximations. Moreover, several specific functions, related to the crossover frequencies of the Nyquist plot and to the feasible design specifications for a given process, are derived. These functions, which are particularly useful for the general design of PI- and PID-type controllers for UFOPDT processes are accurately approximated, in order to simplify the tuning procedure. With the proposed approximations, the tuning rules reported in this brief require relatively small computational effort and are particularly useful for online applications
Applied Soft Computing | 2012
George Kyriakarakos; Anastasios I. Dounis; K.G. Arvanitis; George Papadakis
Autonomous polygeneration microgrids (APM) are a relatively new approach in covering specific needs like power, potable water and fuel for transportation, in remote areas. This approach has been proved to be technically feasible nowadays and even present itself as an economically viable investment. The initial management system built for this approach is a simple ON/OFF supervisor which can make the APM operate, but not in an optimal way. The devices cannot be operated in part load and as a consequence there is little room for optimization. A combined fuzzy cognitive maps (FCMs)-petri nets (PN) approach has been developed for the energy management of such a system. The PN is used as an activator in the fuzzy cognitive map structure so as to enable different FCMs to be activated depending on the state of the microgrid. This combination forms an integrated approach to the energy management of the microgrid. Using this approach considerable optimization in the design and operation of the microgrid is possible. A methodology for simultaneous and interactive optimization of the energy management system along with the sizing of the various devices of the actual microgrid is implemented. A software platform consisting of TRNSYS, TRNOPT and GenOPT software packages was used for simulation and optimization. Particle swarm optimization is applied both for the sizing of the system and the optimization of the FCM weights and PN parameters. Two microgrids were designed, one based on the FCM-PN energy management system (FPEMS) and one on the ON/OFF approach. The results show that FPEMS manages the energy flows more effectively throughout the year which leads to a considerable decrease in the sizing of the various components of the microgrid.
international conference on computational intelligence for measurement systems and applications | 2005
Konstantinos P. Ferentinos; Theodore A. Tsiligiridis; K.G. Arvanitis
In this paper we propose an approach to op t im al design of application-specific wireless sen so r networks based on th e optim ization prop erties of genetic algorithm s. S p eci fi c requirem en ts fo r a precision agriculture applicatio n of sen so r networks are taken in t o a cco u nt by the genetic algo rithm system , together with connectivity an d en erg y co n s erva t i on lim ita tions. We d evel o p an appropriate fitness function to inco rporate many aspects of network performance. The design characteristics optim ized by the genetic algorithm system includ e the sta tu s of sen so r nodes (whether they are active or inactive), network clustering with the ch o i ce of app ro p r ia t e cl u st erh ea d s and fina lly the ch o i ce b et w een two signal ran g es fo r the norm al sen so r nodes. Op tim a l sen so r network designs co nstructed by the genetic algo rithm system satisfy al l application-specific req u irem en ts, fulfill th e ex istent connectivity co nstraints and inco rporate en erg y co n s erva t i o n characteristics.
Computers and Electronics in Agriculture | 2000
N. Sigrimis; K.G. Arvanitis; G.D. Pasgianos
The advantages of using artificial intelligence (AI) decision support tools in synergism with low level process controllers or schedulers are investigated in this paper. The development of a modern control and management system for greenhouses used recent advances in software design, and development tools, to provide an open system for rapid program development. To effectively integrate expert system applications in a control and management system, an environment was built that supports all required interfaces between AI applications and the greenhouse management system (GMS). This environment incorporates a native fuzzy knowledge based system (KBS) and a number of procedural control functions, in the GMS, that can effectively interact. The programmable logic controller (PLC) houses all well-known control function blocks, in library form, callable to implement various control loop designs. Functions that have not been foreseen in the PLC control library can be instantly implemented using the open KBS system. The innovative addition of integral initial conditions on a proportional-integral-derivative (PID) controller, for repetitive load switching applications, is an example, demonstrated in this paper. The usefulness of other control blocks such as a self-adjusting Smith predictor is also tested for a real application of a mixing process with long dead time. Synergism of fuzzy decisions and fuzzy controllers, at the supervisory level, with low level process regulators provide adaptive systems, which can optimize both long-term objectives and the short time dynamic responses.
European Journal of Control | 1999
Ioannis K. Kookos; Argyrios I. Lygeros; K.G. Arvanitis
This paper presents two new methods for tuning PI controllers for integrator plus dead time models (IPDT). The simple IPDT model was found to be a useful approximation of more complex models such as first-order plus dead time models with large time constants. The use of IPDT models can, in these cases, simplify and also accelerate the auto-tuning step especially in MIMO processes. The advantages of the proposed methods over the known tuning methods are demonstrated through simulation examples.
IEEE Transactions on Control Systems and Technology | 2004
Paraskevas N. Paraskevopoulos; George D. Pasgianos; K.G. Arvanitis
The use of pseudoderivative feedback (PDF) in the control and identification of unstable first order plus dead-time (UFOPDT) processes is investigated. Several new methods for tuning the PDF feedback controller are presented. In contrast to known tuning rules for conventional proportional integral derivative (PID) controllers, which result in excessive overshoot in the closed-loop response, the proposed control structure and tuning methods ensure a smooth response to set-point changes, fast attenuation of step-load disturbances, and satisfactory robustness against parametric uncertainty. Moreover, two simple methods for identifying the UFOPDT process parameters, based on this controller structure, are proposed in this paper. Both methods rely on a single experiment on a closed-loop system with a step change in the set point of a PDF controller. They are very accurate, as well as simpler and less sensitive than existing identification methods. Finally, an application of the proposed identification and tuning methods to an open-loop unstable bioreactor with hard input constraints and significant measurement delay is presented.
Automatica | 1994
P.N. Paraskevopoulos; K.G. Arvanitis
A new technique is presented for the solution of the exact model matching problem of linear time-invariant systems using generalized sampled-data hold functions. This technique reduces the problem to that of solving a nonhomogeneous algebraic system of equations. On the basis of this system of equations, the necessary and sufficient conditions are established and the expressions of the controller matrices are derived.
International Journal of Adaptive Control and Signal Processing | 1996
K.G. Arvanitis
SUMMARY The use of sampled-data multirate-output controllers for model reference adaptive control of possibly nonstably invertible linear systems with unknown parameters is investigated. Multirate-output controllers contain a multirate sampling mechanism with different sampling period at each system output. Such a control allows us to assign an arbitrary discrete-time transfer function matrix for the sampled closed-loop system and does not make assumptions on the plant other than controllability, observability and the knowledge of two sets of structural indices, namely the controllability and the observability indices. An indirect adaptive control scheme based on these sampled-data controllers is proposed which estimates the unknown plant parameters (and consequently the controller parameters) on-line from sequential data of the inputs and the outputs of the plant, which are recursively updated within the time limit imposed by a fundamental sampling period T,. Using the proposed adaptive algorithm, the model reference adaptive control problem is reduced to the determination of a fictitious static state feedback controller owing to the merits of multirate-output controllers. Known indirect model reference adaptive control techniques usually resort to the direct computation of dynamic controllers. The controller determination reduces to the simple problem of solving a linear algebraic system of equations, whereas in known indirect model reference adaptive control techniques, matrix polynomial Diophantine equations usually need to be solved. Moreover, persistent excitation of the continuous-time plant is provided without making any special richness assumption on the reference signals.