Manolis A. Christodoulou's research while affiliated with Technical University of Crete and other places
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Publications (152)
The direct adaptive regulation of affine in the control nonlinear square (system states equals to control inputs) dynamical systems with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High...
A new definition of Adaptive Neuro - Fuzzy Systems is presented in this paper for the identification of unknown nonlinear dynamical systems. The proposed scheme uses the concept of Adaptive Fuzzy Systems (AFS) operating in conjunction with High Order Neural Network Functions. Since the plant is considered unknown, we first propose its approximation...
The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Adaptive Systems (FAS) operating in conjunction with High Order Neural Network Functions (HONNFs). Sin...
In our world, there are two principal objectives in the scientific study of the environment: we want to understand (identification) and to control. These two goals are in continuous interaction with each other, since deeper understanding allows firmer control, while, on the other hand, systematic application of scientific theories inevitably genera...
In this chapter, we present a study for the existence of equilibrium points of FCNs equipped with continuous differentiable sigmoid functions that have contractive or at least nonexpansive properties. The study is done by using an appropriately defined contraction mapping theorem and the nonexpansive mapping theorem. It is proved that, when the wei...
Apart from the simulations carried out throughout this book, demonstrating the various operational aspects of the Neurofuzzy (NF) controllers, in this chapter, we present a number of selected applications. We are focusing in the robotic manipulators, the chemical processes, and the electrical drives. The reason for this selection is due to the comp...
In this chapter, we present the framework of operation of FCN, which is based on the adaptive estimation algorithms developed in the previous chapter and on the proposal of a fuzzy rule-based mechanism for storing acquired knowledge during its operation and training. Moreover, selected applications are presented, which demonstrate the applicability...
In this chapter, adaptive estimation algorithms are proposed, which estimate the FCN parameters based on sampled data that correspond to FCN equilibrium points. First, we assume that the only parameters that have to be estimated are the FCN weights. This requires the development of estimation algorithms that are based on a linear parametric model o...
In this chapter we present the direct adaptive regulation and tracking of affine in control nonlinear MIMO dynamical systems possessing unknown nonlinearities. The method is based on the neurofuzzy modeling presented in Chaps. 3 and 4, which combines the definition of fuzzy dynamical systems with the recurrent high-order neural networks.
When the n...
Fuzzy Cognitive Networks (FCN) stem from Fuzzy Cognitive Maps (FCM), initially introduced by Kosko to model complex behavioral systems in various scientific areas. This chapter presents basic definitions related to FCM and the traditional way of their operation. It starts with a brief bibliographical introduction, presenting various extensions of t...
In this chapter, we analyze the identification problem, which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. As identification m...
In this chapter, we present a HONN-based NF controller used for the direct adaptive control of nonlinear dynamical systems assuming a Brunovsky canonical form. Also, based on the same NF modeling, the adaptive tracking problem is addressed for the same class of affine nonlinear systems, which are exactly input-output linearizable by nonlinear state...
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) using F-RHONNs under the presence of parameter and dynamic uncertainties, is considered in this chapter. The method is based on the new NF dynamical systems definition introduced in Chap. 2, which uses the concept of adaptive fuzzy systems...
Presenting current trends in the development and applications of intelligent systems in engineering , this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varyin...
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant wo...
To cope with the indirect regulation of unknown affine in the control nonlinear systems, this paper proposes a method which is based on a recurrent Neuro-Fuzzy modeling. Initially, the components of the nonlinear plant are approximated by Fuzzy subsystems. Using appropriately defined “indicating functions”, it is shown that the initial dynamical fu...
In this paper, a new methodology is proposed for deterministic learning with neural networks. Using an observer that employs the integral of the sign of the error term, asymptotic estimation of the respective nonlinear vector field is achieved. Patchy Neural Networks (PNNs) are introduced to identify the unknown nonlinearity from the observer's out...
Fuzzy Cognitive Networks (FCNs) have been introduced by the authors as an operational extension of Fuzzy Cognitive Maps (FCMs), initially introduced by Kosko to model complex behavioral systems in various scientific areas. FCNs rely on the admission that the underlying cognitive graph reaches a certain equilibrium point after an initial perturbatio...
In this paper, the "wing rock" phenomenon is described for slender delta 80° wing aircrafts on the roll axis. This phenomenon causes the aircraft to undergo a strong oscillatory movement with amplitude dependent on the angle of attack. The objective is to identify "wing rock" using the Patchy Neural Network (PNN), which is a new form of neural nets...
This paper derives a new prediction model of the global discrete‐time input‐output multiple‐delay Takagi–Sugeno (T‐S) fuzzy systems with multiple delays and employs it for adaptive fuzzy control in the presence of system parameter uncertainties. On the basis of a model‐based approach, a new system parametrization and adaptive control scheme are dev...
An editorial to a special issue of the journal ”Int. J. Adapt. Control Signal Process.” about new results on neuro-fuzzy adaptive control systems.
This paper proposes a direct adaptive neuro-fuzzy controller to address the adaptive tracking problem for a class of affine nonlinear Multi-Variable Multi-Input (MVMI) unknown systems that are linearizable by nonlinear state feedback. The proposed control scheme uses a recurrent neuro-fuzzy model to approximate the system, which combines an underly...
An inverse optimal neural controller for discrete‐time unknown nonlinear systems, in the presence of external disturbances and parameter uncertainties, is presented. It is based on a discrete‐time recurrent high‐order neural network trained with an extended Kalman filter‐based algorithm. The applicability of the proposed approach is first tested vi...
In this paper, a discrete‐time inverse optimal trajectory tracking for a class of nonlinear positive systems is proposed. The scheme is developed for MIMO affine discrete‐time positive nonlinear systems. This optimal controller is based on discrete time passivity and positive systems theory. The advantage of this scheme is that it avoids solving th...
In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification mode...
The direct adaptive dynamic regulation of unknown nonlinear multi variable systems is investigated in this chapter in order to address the problem of controlling non-Brunovsky and non-square systems with control inputs less than the number of states. The proposed neuro-fuzzy model acts as a universal approximator. While with the careful selection o...
In this paper a RISE (Robust Integral of the Sign Error) observer approach is used for estimating the concentrations of the proteins that participate in the reactions of a biological system. The system under study is the Epidermal Growth Factor Receptor (EGFR) Signaling Pathway. The EGFR family is directly related with diseases such as multiple scl...
In this paper a neuro fuzzy identification scheme is used in order to identify four different nonlinear biological systems that describe four combination therapy models respectively. Combination therapy is a significant field in modern medicine and it is very important to be able to develop a system-level understanding of such biological systems an...
In this paper, the problem of controlling and stabilizing rapidly time-varying nonlinear unknown systems is being investigated. We propose a new scheme that incorporates Multiple Takagi-Sugeno (T-S) Identification Models into an indirect adaptive fuzzy technique. By using this approach, we increase the possibilities to produce a more accurate estim...
In this paper, an adaptive control method for trajectory tracking of robot manipulators, based on new neuro-fuzzy modelling is presented. The proposed control scheme uses a three-layer neural fuzzy network (NFN) to estimate system uncertainties. The function of robot system dynamics is first modelled by a fuzzy system, which in the sequel is approx...
In this paper, a new methodology is proposed for deterministic learning with neural networks. Using an observer that employs the integral of the sign of the error term, asymptotic estimation of the respective nonlinear vector field is achieved. Patchy Neural Networks (PNNs) are introduced to identify the unknown nonlinearity from the observer's out...
A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper, paying special
attention to the analysis of the model order problem. The method uses a neurofuzzy (NF) modeling of the unknown system, which
combines fuzzy systems (FSs) with high order neural networks (HONNs). We propose the approximat...
The Fuzzy Cognitive Network(FCN) framework is a proposition for the operational extension of fuzzy cognitive maps to support the close interaction with
the system they describe and consequently become appropriate for adaptive decision making and control applications. They constitute
a methodology for data, knowledge, and experience representation b...
The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. Since the plant is considered unknown, we propose its approximation by a special form of a Brunovsky type neuro-fuzzy dynamical system (NFDS) assuming also the existence of disturbance expressed as modeli...
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and hi...
An adaptive methodology is introduced to identify the weights of a general Fuzzy Cognitive Network (FCN). The proposed scheme consists of two hierarchically structured individual identification tasks that are both implemented on-line. The inner task employs a robust integral of the sign error (RISE) observer to approximate the system's unknown nonl...
The direct adaptive regulation for affine in the control nonlinear dynamical systems possessing unknown nonlinearities, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynam- ical Systems (FDS) operating in conjunction with High Order Neural Network Functions (F-HONN...
The indirect or direct adaptive regulation of unknown nonlinear dynamical systems is considered in this chapter. Since the plant is considered unknown, we first propose its approximation by a special form of a fuzzy dynamical system (FDS) and in the sequel the fuzzy rules are approximated by appropriate high order neural networks (HONN’s). The syst...
The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this chapter. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Adaptive Systems (FAS) operating in conjunction with High Order Neural Network Functions (HONNF’s)....
The indirect adaptive regulation of unknown nonlinear dynamical systems under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical Systems definition named Fuzzy-Recurrent High Order Neural Network (F-RHONN), which however takes into account the fuzzy output partitions...
Fuzzy Cognitive Networks (FCN) have been introduced by the authors recently as an extension of Fuzzy Cognitive Maps (FCM).
One important issue of their operation is the conditions under which they reach a certain equilibrium point after an initial
perturbation. This is equivalent to studying the existence and uniqueness of solutions for their conce...
Fuzzy cognitive maps (FCMs) have been introduced by Kosko to model complex behavioral systems in various scientific areas. One issue that has not been adequately studied so far is the conditions under which they reach a certain equilibrium point after an initial perturbation. This is equivalent to studying the existence and uniqueness of solutions...
The direct adaptive regulation of affine in the control nonlinear dynamical systems with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Sinc...
The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unkn...
Fuzzy cognitive maps (FCM) have been introduced by Kosko to model complex behavioral systems in various scientific areas. One issue that has not been adequately studied so far is the conditions under which they reach a certain equilibrium point after an initial perturbation. This is equivalent to studying the existence and uniqueness of solutions f...
Fuzzy Cognitive Networks (FCN) have been introduced by the authors as an operational extension of Fuzzy Cognitive Maps (FCM),
which were initially introduced by Kosko to model complex behavioral systems in various scientific areas. One important issue
of their operation is the conditions under which they reach a certain equilibrium point after an i...
In recent years there has been a great effort to convert the existing Air Traffic Control system into a novel system known as Free Flight. Free Flight is based on the concept that increasing international airspace capacity will grant more freedom to individual pilots during the enroute flight phase, thereby giving them the opportunity to alter flig...
The regulation of unknown nonlinear dynamical systems using an indirect adaptive control technique is considered in this paper. The proposed scheme uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Since the plant is considered unknown, we first propose its approximation...
In recent years there has been a great effort to convert the existing air traffic control system into a novel system known as Free Flight. Free Flight is based on the concept that increasing international airspace capacity will grant more freedom to individual pilots during the enroute flight phase, thereby giving them the opportunity to alter flig...
In this paper, we present a general computational and operational framework for the Fuzzy Cognitive Network FCN, which is a direct extension of Fuzzy Cognitive Maps FCM. The proposed framework assumes a network operation, which continuously receives feedback from the system it describes and outputs control or decision values. This way, its knowledg...
Geneticists try to find the hidden truth behind the genomes which contain the blueprint for all parts of life's machinery. Next challenge will be corresponding DNA to various types of proteins; thus deriving meaningful knowledge for the understanding of biological systems. Proteins are capable of explaining human evolution while others, such as mem...
In this paper, an Adaptive Backstepping Neural Network control approach is used for a class of affine nonlinear systems which describe the Mitogen Activated Protein Kinase (MAPK) cascade models in the strict feedback form. We consider some of forms of the MAPK cascade [4]. The close loop signals are semiglobally uniformly ultimately bounded and the...
The present work deals with the MAPK (mitogen-activated protein kinase), a three molecule module present in all eucaryotes, which has a wide range of functions in signal transduction, such as stress-response, cell-cycle-control, cell-wall-construction, osmosensing, growth and differentiation. This biological system is in fact an autonomous system a...
In this paper, optimal resolution of air-traffic (AT) conflicts were considered. Aircraft are assumed to cruise within a free altitude layer and are modeled in three dimensions with variable velocity and proximity bounds. Aircraft cannot get closer to each other than a predefined safety distance. The problem of solving conflicts arising among sever...
In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated by comparing its performance with conventional schedulers. Initially, after a brief reference to the context of existing solutions, the evaluated controller is thoroughly described. Namely, the employed...
In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated by comparing its performance with conventional schedulers. Initially, after a brief reference to the context of existing solutions, the evaluated controller is thoroughly described. Namely, the employed...
In this paper a simulation language is developed, useful in ground handling operations at hub airports. Model blocks are made, for various operations, and then the SSLA is used to simulate airport operations dynamics. Under different scenarios, quantitative estimates for time operations are obtained, which help us in improving their efficient sched...
In this paper a simulation language is developed, useful in ground handling operations at hub airports. Model blocks are made, for various operations, and then the SLAGOM is used to simulate airport operations dynamics. Under different scenarios, quantitative estimates for time operations are obtained, which help us in improving their efficient sch...
Present multimedia services are provided through a heterogeneous set of networks. Because of the heterogeneity of the networks, long-term resource availability guarantees are difficult to obtain. Consequently, even if the resource requirements throughout the service could be accurately mapped, it would not be feasible to provide overall performance...
This paper presents the implementation of a portable ECG unit, connected to a PC via the parallel port (LPT1). The ECG unit is divided into an analog and a digital part. In the analog part, the ECG signal is acquired from the patient, with the use of the proper electrodes, it is amplified, then cleared from most of its noise and, finally, it is sui...
This paper addresses the problem of conflict avoidance in Air Traffic Management (ATM) systems. Aircraft are assumed to travel in a shared airspace and collaborate for solving the conflicts by sharing information. Aircraft can not get closer to each other than a given safety distance and each aircraft's goal is to reach its final destination in min...
In this paper a neural adaptive scheduling methodology approached
machine scheduling as a control regulation problem is evaluated by
comparing its performance with conventional schedulers, through
simulation studies. The case study chosen constitutes an existing
manufacturing cell which can be viewed as a deterministic job shop with
extremely heter...
This chapter is devoted to the development of direct adaptive neurocon-trollers for afflne in the control nonlinear dynamical systems possessing unknown nonlinearities. The recurrent high-order neural networks are used as models of the unknown plant, practically transforming the original unknown system into a RHONN model which is of known structure...
In Chapter 5, the non-acyclic FMS scheduling problem was considered to be a control regulation problem, where system states (buffer levels), have to reach some prespecified production requirements, by means of control input commands. Based on a recurrent high-order neural-network model of the buffer states, an adaptive continuous-time neural-networ...
Efficient management of the production process, in manufacturing systems is a vast subject with decisive impact on major economic entities, like productivity, competitiveness and viability. Production scheduling (i.e., organization and control of production operations in manufacturing systems), is essential for the efficient operation of a producti...
The use of multilayer neural networks for pattern recognition and for modeling of “static” systems is currently well-known (see, for example, [1]). Given pairs of input-output data (which may be related by an unknown algebraic relation, a so-called “static” function) the network is trained to learn the particular input-output map. Theoretical work...
This chapter is devoted to the development of indirect adaptive control techniques (based on RHONNs), for controlling nonlinear dynamical systems, with highly uncertain and possibly unknown nonlinearities.
In this paper a dynamic neural network (DNN)-based controller is constructed to provide the basis upon which a scheduler is developed to guarantee that system production will reach the required demand while satisfying buffer capacity constraints. Lyapunov stability theory is used to prove boundedness of all signals in the closed loop.
In this paper, a control aspect of the non-acyclic FMS scheduling problem is considered. Based on a dynamic neural network model derived herein, an adaptive, continuous time neural network controller is constructed. The actual dispatching times are determined from the continuous control input discretization. The controller is capable of driving sys...
We employ recurrent high order neural networks (RHONNs) to
determine the unknown values of media characteristics that lead to user
satisfaction without violating network limitations. Based on a priori
knowledge-measurements, we assume given a nonlinear function that
relates media characteristics with user satisfaction, which we further
exploit to c...
Classical adaptive and robust adaptive schemes, are unable to ensure convergence of the identification error to zero, in the case of modeling errors. Therefore, the usage of such schemes to ''black-box'' identification of nonlinear systems ensures--in the best case--bounded identification error. In this paper, new learning (adaptive) laws are propo...
In the article by [Kosmatopoulos et al. (1997)] (Neural Networks 10(2) 299-314) the Theorem 4.1 was incorrect. In this note we present the correct version of Theorem 4.1.
This paper presents a neural network approach in determining the appropriate manufacturing cell configuration that meets the required performance measures. Simulation experiments were conducted with many possible combinations of design changes to calculate cell performance measures, and thus generate training pairs for a neural network. Three diffe...
In this paper the neuro adaptive scheduling methodology is
evaluated by comparing its performance with conventional schedulers,
through simulation studies. The case study chosen constitutes an
existing manufacturing cell, which can be viewed as a highly complex
nonacyclic FMS, with extremely heterogenous part processing times. The
results reveal su...
A control aspect of the non-acyclic FMS scheduling problem is
considered. Based on the dynamic neural network model derived herein, an
adaptive, continuous time neural network controller is constructed. The
actual dispatching times are determined from the continuous control
input discretization. The controller is capable of driving the system
produ...
It is known that the problem of learning the shape parameters of
unknown surfaces that are in contact with a robot end-effector can be
formulated as a nonlinear parameter estimation problem and an extended
Kalman filter can be applied in order to estimate the surface shape
parameters. In this paper, we show that the problem of learning the
shape pa...
With this paper we extend our previous work on the subject, by including the case where the number of control inputs is different from the number of states which is frequently faced in control engineering problems. Uniform ultimate boundedness of the state and uniform boundedness of all other signals in the closed loop is guaranteed. Robustness of...
A neural network approach to the manufacturing cell modelling
problem is discussed. A recurrent high-order neural network structure
(RHONN) is employed to identify cell dynamics, which is supposed to be
unknown. The model is constructed in such a way that enables the design
of a controller which will force the model and thus the original cell to
di...
The application of neural network technology to multichannel image processing is presented in this paper. Topics such as image restoration, segmentation, transformation and compression are discussed, covering a wide range of image processing and analysis areas. The problems are converted to optimisation or interpolation problems through the appropr...
A direct nonlinear adaptive state regulator is derived, based on dynamic neural networks, and it is successfully applied to control the speed of a nonlinearized dc motor. One interesting feature of the proposed control algorithm is that it covers the situation where the magnetic flux continuously varies, as it is the case in the loss minimization p...
A mathematical analysis of a class of learning vector quantization
(LVQ) algorithms is presented. Using an appropriate time-coordinate
transformation, we show that the LVQ algorithms under consideration can
be transformed into linear time-varying stochastic difference equations.
Using this fact, we apply stochastic Lyapunov stability arguments, and...
A direct nonlinear adaptive state regulator, for unknown dynamical systems that are modeled by recurrent neural networks is discussed. In an ideal case of complete model matching, the convergence of the state to zero plus boundedness of all signals in the closed loop is ensured. Moreover, the behavior of the closed loop system is analyzed for cases...
A direct nonlinear adaptive state regulator, for unknown dynamical
systems that are modeled by dynamic neural networks is discussed. In the
ideal case of complete model matching, convergence of the state to zero
plus boundedness of all signals in the closed loop is ensured. Moreover,
the behavior of the closed loop system is analyzed for cases in w...
A direct nonlinear adaptive controller, to solve the regulation problem for unknown dynamical systems that are modeled by recurrent neural networks is discussed. The behavior of the closed-loop system is analyzed for the case in which the true system differs from the recurrent neural network due to the presence of a modeling error term. Generally,...