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Today, medical imaging techniques are useful diagnostic tools in every specialty. The images are analyzed for diagnosis and treatment planning. Furthermore, medical imaging analysis is performed by specialized medical staff who, depending on work conditions tend to be subjective. Malignant bone tumors, like osteosarcoma, destroy the cortex of the bone and extend into surrounding soft tissues. So, it is important to detect and classify the bone tumor in an early stage with high accuracy. This work introduces a convolutional neural network approach along with image processing techniques to detect and classify bone magnetic resonance imaging scans into a malignant or benign tumor. Using transfer learning techniques, we compared the performance of two pre-trained CNN models VGG-16 and ResNet-50.

Remote monitoring of electrical networks finds its utility in real-time acquisition of the available data from the existing network transformation points, globally collecting and specifically processing the data at power dispatcher level, as its main beneficiary. This implementation also provides important information for possible low and medium power network failure diagnosis, becoming a useful instrument in network data analysis. The paper exemplifies the utility of this approach through a specifically designed and implemented application. Data acquiring and transfer is assured by a hardware structure controlled through its own software application, particularly implemented for local processing and transfer of the sampled data. The information is provided to the higher hierarchical level using a specific MQTT protocol format. The software application was implemented and runs on power monitoring units installed in a distributed monitoring network for transformation points. Using such structures has improved the efficiency in consumption analysis for electrical networks, bringing easiness in real time identification for various failures in electrical network points.

This work approaches the design of software tools for a Wastewater Treatment Plant (WWTP) located in Facai, Romania, where the Distributed Control System/ Supervisory Control and Data Acquisition (DCS/SCADA) solution consists in four specific levels. More precisely, the paper proposes software solutions for the emulation of the control system from the WWTP. Due to the complexity of a WWTP, the proposed solutions and tools are dedicated to two levels of DCS. The specific bioprocesses (e.g. the activated sludge process) are simulated and the results are compared with data provided by the data acquisition system. Also, a software solution for the monitoring of process data provided by various sensors is designed. The proposed software solutions will be useful in future research for the design of advanced control systems in the WWTP. The software models and tools are designed in Matlab/Simulink, but also in LabWindows CVI and other software environments.

This study addresses an innovative multivariable control strategy for a wastewater treatment bioprocess with activated sludge. This bioprocess takes place in a continuous reactor located at the Wastewater Treatment Plant Facai, Romania. The proposed adaptive control structure is designed under realistic assumptions: several process variables are unavailable, the reaction kinetics is time-varying and unknown, the influent pollutant flow rate varies in large limits and much more its concentration is totally unknown. The novelty of the approach consists in the insertion of a sliding mode observer (SMO) in overall control structure. The SMO provides the estimates of the unknown influent pollutant concentration. The performance of the adaptive multivariable control algorithm is assessed via numerical simulations based on data provided from the plant.

A gesture is a form of non-verbal communication in which body actions communicate some particular messages. Due to the high processing capacity of today’s computers, hand gesture recognition systems can be used to simplify interactions with electronic devices. In this paper, a hand posture recognition system was implemented, which is able to recognize five posture of a hand. The five postures consist of how many fingers are shown, and then, this information is used to control the position of a DC motor. Hand gestures are recognized by an image processing system that uses a neural network implemented on a microcontroller.

Power engineering device testing implies generating a mono-phase or three-phase voltage and current system, with customizable parameters in order to simulate operating regimes resembling those found in electric power plants. These specific electrical signals are generated using specialized equipment of power system simulator type. The generated output electrical quantities are real-time monitored by users through a functional module for measurement and display, included in the simulator’s structure. This paper presents the development of a complete hardware configuration of a signal parameter measurement and display block and the implementation of a software application which controls this unit. The configurable power system simulator including this module finds its utility especially in inspecting and parameter setting for protection relays used in electric power plants.

The present paper focuses on estimation and advanced control structures for an alcoholic fermentation process (AFP) exploited for the ethanol production. Taking into account the inhibition problems, the control aim consists in the preservation of the so-called process inhibitory compounds at a prescribed reduced level in spite of large variations of the influent pollutant concentration and of the load, having as a result a large production of alcohol. Using a strongly nonlinear and uncertain model and realistic operating conditions, novel estimation and advanced control schemes are proposed. More precisely, adaptive control laws designed via novel nonlinear estimation algorithms for unknown inputs and kinetics are developed. To illustrate that the control goal is fulfilled, various tests performed by computer simulations under realistic conditions are included.

The paper addresses the modelling and the design of an adaptive control structure for an aerobic fermentation process under the realistic assumption that some state variables are unmeasurable, the reaction kinetics are incompletely known and time-varying and the influent flow rate has very large variations. More exactly, the studied process is a wastewater biological treatment process, with activated sludge, carried out in a continuous perfectly mixed bioreactor implemented in the Wastewater Treatment Plant (WWTP) Facai, Craiova, Romania. The behaviour and performance of control algorithms are evaluated by several numerical simulations and are compared with the real outputs of the mentioned plant. The aim of this study is to improve the plant performance by implementation of the proposed control algorithms.

In the textile industry, the operation of textile plants is hindered by some defects, derivations or distortions in the weaving process of the fabric. Related to these distortion corrections, one of the critical operations is the so-called recovery operation (orthogonalization) of fabrics resulting from the loom. The orthogonalization plants realize a certain angle between warp threads and weft yarns of fabrics. In this paper we develop a mathematical model of the curvature correction and a nonlinear control structure for the entire orthogonalization process. Adaptation is necessary due to the change in the quality of the fabric and its driving velocity. Thus, a nonlinear adaptive and structure variable control is proposed. Several numerical simulations illustrate the behaviour of the proposed control structure.

Because the models that arise from the bioindustry are highly nonlinear, in order to simulate them we propose an adaptive PID control of step-size, where the variation of the step-size is proportional to the error criterion and a digital filter is used in order to smoothen up the step-size variation. The present paper compares this numerical method with a wide selection of numerical methods that can be used in order to simulate the evolution of the systems. Although the application is designed with the purpose of simulating the evolution of mathematical models that arise from the food processing industry, it can also be used for models that come from other areas of the industry. The proposed numerical method is tested and validated by using some nonlinear models of classical systems such as Lorenz and Van der Pol, but also nonlinear models of bioprocesses.

The paper deals with the automatic control of active force for an active suspension system of railway vehicle. In the first part of the paper, the mathematical model of a simplified active suspension system is derived using the classical Newton laws. The model is represented in the form of a linear first order differential equations system. The control strategy adopted in this paper is Iterative Learning Control (ILC), a control approach intensively used for repetitive processes. In this paper, the repetitive behavior is represented by the body vehicle reaction to track irregularities. From many variants of ILC available in the literature, few simple algorithms were presented: the well-known PD – type algorithms. An heuristic design procedure is implemented for control of a linearized version of the active suspension system. The performances of the proposed control procedure are tested by numerical simulations. The results obtained from the performed simulations show the feasibility of the proposed methods in the case of step-type perturbations.

A novel multivariable adaptive control scheme for a lactic fermentation biotechnological process with unknown inputs taking place inside two sequenced Continuous Stirred Tank Reactors (CSTRs) is developed. The design of the control scheme is achieved under the hypothesis that the influent substrate concentrations and the reaction kinetics are unknown and time-varying, but also a part of the state variables are unmeasurable. The adaptive controller includes a linearizing controller connected with two state observers used for the real-time estimation of the unknown input substrate concentrations and with a parameter estimator which is used for kinetics estimation. The estimation of unknown influent substrate concentrations is realized by using the measurements of the internal substrate and lactic acid concentrations. The proposed parameter estimator employs the information which is provided on-line by two sliding-mode state observers. The performance of the designed control scheme is evaluated via simulation experiments conducted for a lactic acid production process (LAPP) carried out inside two sequenced CSTRs. The dynamics of the considered bioprocess is described by a nonlinear, incompletely known and time varying model.

The paper addresses the design and analysis of Iterative Learning Control (ILC) method for a wastewater biodegradation process. The bioprocess is considered to take place inside a continuous stirred tank bioreactor. The use of this kind of control methods is motivated by its advantages in the case of complex nonlinear systems like biotechnological processes. There were used two ILC algorithms one considered a classical approach in the field (PD-type learning algorithm), and the second one which try to exploit some characteristics of the input signal (exponential learning algorithm). These control methods are implemented for the de pollution control problem in the case of an anaerobic digestion process. This bioprocess is characterized by strongly nonlinear and not exactly known reaction rates. Furthermore, not all the state variables are measurable. The performance and effectiveness of the presented control algorithms are proven by simulation results.

The paper deals with this problem of automatic control of the orthogonalization plants for textile industry by using a process computer. To do this an original mathematical theory of the textile threads correction phenomenon, particularly orthogonalization phenomenon is developed, as a ground for obtaining the mathematical model of the controlled plant. It is proven that the mathematical model corresponds to a nonlinear time-varying system with several time-delay variables with 2 inputs and 2p outputs, where 2p is the number of the slope transductors. It is pointed out the nonlinearity is a nondynamical one, which can be separated from the linear dynamical part. The control law algorithm which can be implemented on a numerical process computer is obtained. Several practical results obtained by simulation are presented.

This paper is devoted to the comparison of numerical integration methods for complex dynamical biosystems such as mammalian cell culture process used for monoclonal antibodies production and the baker's yeast process. These bioprocesses are widely used in bio-industry, biochemistry, biology, and medicine. Their mathematical models are highly non-linear and, furthermore, the reaction kinetics is not perfectly known and some of the parameters are not experimentally accessible. Another part of the complexity of these models arises from the high number of state variables that describe their evolution in time. Because some of the parameters of these models need to be estimated on-line, fast and viable numerical integration algorithms need to be employed.

The paper addresses the development and implementation of a distributed and hierarchized control system for processes in food industry. More precisely, the wheat grinding and bread production processes that take place at the Mill and bread factory Calafat, Romania, are envisaged. Two solutions based on the DCS / SCADA (Distributed Control System / Supervisory Control and Data Acquisition) architecture are proposed. The first DCS-SCADA structure is a hierarchical monitoring and control system based on digital controllers, data acquisition and computer system. The second structure is a DCS-SCADA hierarchical monitoring and control system based on compact data acquisition and control systems. The configuration and the functionality of the DCS levels are described. Also, the communication interface between the primary control loops and the superior hierarchic levels is presented. The proposed control structures allow the improvement of the overall system currently implemented at the mill and bread factory.

The paper addresses the developing method of an adaptive control structure for an anaerobic digestion process under the realistic assumption that some state variables are unmeasurable, the reaction kinetics are incompletely known and time-varying and moreover the influent pollutant concentration is completely unknown. More exactly, the process that is taken into account is a wastewater biological treatment process operated in a continuous perfectly mixed bioreactor. The control solution proposed in the paper is based on an innovative adaptive technique. The controller design is achieved by combining an exact linearizing controller with a sequence of two cascaded state observers and with a parameter estimator. The proposed adaptive control structure is able to cope with several problems such as uncertainty of inputs and of kinetics, unavailability of on-line measurements, disturbances and noisy measurements. The behaviour and efficacy of estimation and control structures are evaluated by several digital simulations.

This work presents simplified numerical methods for chaotic attractors. The proposed simplification consists in a rescaling technique that uses integrating factors. Because some factors are exponentials and the numerical values can grow very fast, further simplifications are introduced in the paper. These simplified numerical methods are implemented for Rossler and Lorenz systems. Numerical simulations are achieved using Matlab/Simulink programming and development environment.

It is well known that in the case of the wastewater treatment bioprocesses, including anaerobic digestion process, usually, the complete knowledge of inputs is not available and therefore the implementation of the control laws becomes a difficult problem. Therefore, in the sequel, for the above mentioned process two observer structures, able to estimate the unknown concentration of the input will be presented and analyzed. The paper deals with a comparative analysis of two types of observers: a stochastic one — Extended Kalman Filter and a deterministic one — Sliding Mode Observer. The estimation methods are analyzed in realistic frame taking into consideration the presence of the measurement noise but especially the usual variations in the case of an anaerobic digestion process, such as the variation of the maximum growth rate of the microorganisms. The effectiveness of the proposed observers has been validated by numerical simulations.

The paper presents a finite-memory adaptive-optimal control of a Continuous Stirred Tank Bioreactor (CSTB). In the proposed algorithm, the concentration of the inlet limiting substrate and the dilution rate are chosen such that the operating point evolves towards the optimal steady state which maximizes the CSTB productivity. This optimization process is subject to the constraint that the biomass concentration to be constant at a desired value. The optimal point is obtained making a local approximation of the steady state behaviour by a second degree hyper-surface, whose parameters are estimated with a finite-memory optimal estimator. The plant control in the proximity of each operating point is provided by a linear multivariable law whose parameters are adapted such that the eigenvalues of the closed loop linearized system belong to an invariant spectrum. The behaviour of the proposed algorithm is analysed by numerical simulation.

The paper presents an original method for Finite Time Response (FTR) control of the affine systems. The FTR property is specific to linear systems only, known in the literature as dead-beat algorithms. In this work, it is developed as a new procedure for the affine systems FTR synthesis, called the Equivalent Input Method (EIM). For this purpose it calculates an equivalent input which will determine, according to a quadratic criterion, the best approximation of the affine component. This way the system is approximated by an affine system with an input variable equal to the sum of the original input and the equivalent input, but having only a residual affine component. This residual affine component has a smaller norm than the initial affine component. Considering zero the residual affine component, a FTR linear system synthesis procedure is applied. In the real system, controlled by a FTR control law, the residual affine component creates at each step a disturbance that FTR algorithm seeks to cancel. This approach is justified by the fact that the disturbance residual affine component is much smaller in norm than the original affine component. Under certain circumstances, this residual affine component can be zero. The controllability and algorithm convergence is analyzed. The proposed EIM method can be applied also for nonlinear systems approximated by Piecewise Affine Subsystems (PWAS). An experimental platform has been designed in Matlab environment allowing implementation of various affine systems and their control algorithms. Simulation results are included to support the method presented in the paper.

This paper is concerned with the parameter estimation of a bacterial growth bioprocess using heuristic optimization. The identification algorithms used a dynamic mathematical model containing nine unknown parameters. These parameters were determinate using simulated annealing and genetic algorithms through the minimization of an evaluation function. The reaction kinetics used to simulate microbial growth are the Monod and Haldane equations. The parameter estimation problem is formulated as a multi-modal numerical optimization problem The performances of the two methods are analyzed by numerical simulations.

This paper deals with the design of a predictive control law for microalgae culture process to regulate the biomass concentration at a chosen setpoint. However, the performances of the Nonlinear Model Predictive Controller usually decrease when the true plant evolution deviates significantly from that predicted by the model. Thus, a robust criterion under model's parameter uncertainties is considered, implying solving a min-max optimization problem. In order to reduce the computational burden and complexity induced by this formulation, a sensitivity functions analysis is carried out to determine the most influential parameters which will be considered in the optimization step. The proposed approach is validated in simulation and numerical results are given to illustrate its efficiency for setpoint tracking in the presence of parameter uncertainties.

The paper presents the results of mathematical modeling using bond graph methodology applied to cells growing process in pharmaceutical industry. Largely used at industrial scale, the mammalian cell culture represents an important sector with intensive dynamic that requires continuous optimization due to evolution and transformations of medical applications. The research is focused on monoclonal antibodies (mAbs), proteins that are usually produced from mammalian cell cultures and used in biochemistry, biology and medicine. A particular model of mammalian cell culture, generic for this class of bioprocesses, was used. Usually the operation of these processes is mostly based on empirical knowledge and repeated experimental procedures, continuous adjusted using the operators experience. This paper proposes a comprehensible nonlinear dynamic modeling as a tool for this type of bioprocesses characterization.

This paper presents the design of adaptive and robust-adaptive control strategies for an anaerobic bioprocess with biogas production. The design schemes are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with a state asymptotic observer or an interval observer, and with a parameter estimator used for on-line estimation of unknown kinetics of the process. Numerical simulations are performed to test the proposed algorithms.

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a nonlinear propagation bioprocesses carried out inside a fixed bed reactor. The design schemes are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately state asymptotic observer or an interval observer and with a parameter estimator used for on-line estimation of unknown kinetics. The effectiveness of the proposed algorithms is validated by numerical simulations applied in the case of a prototype fixed bed bioprocess.

Considering the importance of practices in electropneumatic and electrohydraulic systems education and training, the goal of this work is to present an interactive teaching system useful in simulation and control of electropneumatic and electrohydraulic systems. The interactive system is based on applications developed for Windows operating system using LabVIEW and FluidSIM software. The teaching tools are organized in experiments with a real electropneumatic and electrohydraulic structure and in experiments with simulators, all with friendly graphical user interface. The presented interactive teaching system allows an improvement in electropneumatic and electrohydraulic education and training. The system has didactic properties such as modularity of the package, friendly graphical user interfaces and so on, helping students to understand and compare different modeling and control methods. The package can be easily extended with new experiments. Some applications of this interactive system for simulation and control of a Festo MPS (Modular Production System) Compact Workstation for Automation are presented.

This paper deals with a specific category of systems called State Blocking Systems (SBS). Time evolution of such processes is characterized by time intervals where some state components are kept constant, so that classical models are not generally available. The state components can be found in two statuses, namely: active or blocked. Such systems are a particular case of Variable Structure Systems (VSS). The main idea in this paper is to model SBS by using so called blocking functions and to realize a parameterization based on time intervals. Different cathegoruy of SBS are presented. A time variable decomposition is proposed. Examples obtained by numerical simulation point out the advantages of this approach.

This paper continues the results obtained by the authors on State Blocking Systems (SBS). The approach proposed by the authors is based on the so-called blocking function which may be considered as a new input variable that continuously changes the system structure. The paper deals with an original method for Finite Time Response (FTR) control of the state blocking systems. Because of the blocked components and of the active subsystem uncontrollable components, an SBS system appears as an affine system with FTR control procedures unknown in literature. It is developed a new procedure for the affine systems FTR synthesis, called the equivalent input method. For this purpose it calculates an equivalent input which will determine, according to a quadratic criterion, the best approximation of the affine component. Experimental results are presented to justify the method approached in the paper.

This paper addresses the design of an adaptive control strategy for an alcoholic fermentation bioprocess, which takes place inside of a Fed-batch Bioreactor (FBB). This bioprocess is highly nonlinear, some state variables are not measurable and moreover the reaction kinetics is not perfectly known. The kinetic parameters are estimated by using a high-gain nonlinear estimator. An asymptotic state observer is derived so as to provide the unavailable states necessary to the kinetics estimator. Finally, the indirect adaptive control law is designed by combining a linearizing controller with the nonlinear observers. Several numerical simulations are performed, in order to test the proposed nonlinear adaptive control strategy.

In the context of environmental protection, modeling of wastewater treatment bioprocesses, especially of biotechnological processes used for wastewater and organic waste treatment becomes a necessity. In this paper, modeling of a process used for wastewater treatment is presented. The so-called pseudo Bond Graph method is proposed as a modeling technique for an aerobic process of biological wastewater treatment, the activated sludge bioprocess, which is carried out inside two interconnected tanks. The obtained Bond Graph model and several simulations are conducted using 20sim modeling and simulation environment.

This paper deals with the problem of on-line estimation and nonlinear control of a wastewater treatment process, which takes place inside a biological Sequencing Batch Reactor. This bioprocess is highly nonlinear, the on-line measurements are lack and the reaction kinetics is not perfectly known. The unknown kinetic parameters are estimated by using nonlinear observers, based on high-gain approach. Also, the unavailable states are reconstructed by using an asymptotic state observer. The control objective is to maintain a low level of pollutant concentration. This goal is achieved by using a linearizing control law combined with the proposed nonlinear observers, which results in an adaptive control law. Simulations are included in order to test the behavior of the proposed estimation and control algorithms.

This paper deals with the on-line estimation of unknown kinetics for the enzymatic synthesis of ampicillin, that is carried out inside a fed-batch bioreactor. A nonlinear dynamical model of this bioprocess is obtained by using the reaction scheme and the mass balance. The dynamical kinetics is strongly nonlinear and not exactly known; therefore an online estimation strategy is developed, based on high gain approach. The tuning of the proposed high gain observers is reduced to the calibration of a single parameter. Numerical simulations are included in order to test the behavior and the performances of the proposed estimation strategy.

This paper deals with the design of a nonlinear model predictive control (NMPC) scheme for the regulation of the substrate concentration in a lipase production bioprocess that takes place inside a Fed-batch Bioreactor. The NMPC control structure is based on the nonlinear model of the process whose parameters are known and all the states are measurable. Minimization of the cost function is realized using the Levenberg-Marquardt numerical optimization method. Some simulation results are given to illustrate the efficiency of the proposed control strategy.

This paper deals with the Bond Graph modeling and the design of estimation strategies for a nonlinear fed-batch prototype bioprocess. The proposed strategies are developed for an aerobic microbial growth process coupled with an enzyme-catalyzed reaction, which is a usual bioprocess that takes place in fed-batch bioreactors. The rules for the design of pseudo Bond Graph model are obtained by using the reaction schemes and the analysis of biochemical phenomena. Two kinds of on-line estimation strategies are approached. First, a general state observer is analyzed and the exponential observability of the bioprocess is tested; two state estimation algorithms are designed: an extended Luenberger observer and an asymptotic observer. Second, an observer-based estimator is derived for the estimation of unknown kinetics. In order to test the behavior of proposed strategies, numerical simulations are included.

This paper studies the design and the analysis of a nonlinear and neural adaptive control strategy for a complex nonlinear and time varying wastewater treatment bioprocess. In fact a direct adaptive controller based on a radial basis function neural network used as an on-line approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controller design is achieved by using an input-output feedback linearization technique. A realistic case study, which consists of a complex bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor, is fully analyzed. Computer simulations are included to demonstrate the behaviour and the performance of the proposed controllers.

In this paper the Bond Graph methodology is applied on a class of fed-batch biotechnological processes. First, Bond Graph model of a fed-batch prototype bioprocess is obtained by developing a set of rules, starting from the reactions schemes and taking into account the biochemical phenomena. Then, the rules used for the design of this model are applied on an alcoholic fermentation bioprocess that takes place also inside a fed-batch bio-reactor. This modeling procedure represents a valuable illustration of the power of Bond Graph methodology, and can be used as a base for the development of the models of bioprocesses with high level of complexity. Several simulations are conducted using 20sim modeling and simulation environment.

The paper presents a unified procedure for construction of Real Generalized Fock Space (RGFS) oriented for continuous time nonlinear systems identification whose inputs belong to a separable Hilbert space. The basic characteristic of this approach is the construction of a tensor space generated by an n-linear natural embedding map. This natural embedding map can be defined according to the practical problem of identification. The inner product of the built tensor space is dependent on the scalar product of the input variables Hilbert space. The properties of the defined tensor product space are transferred to some linear space, in particular the Hilbert-Schmidt space of n-degree polynomials. The nonlinear identification problem can be formulated as a minimum norm problem. Finally, the formula for the nonlinear functionals identification is obtained, solved by dual approximation in Hilbert spaces. Some numerical examples are presented.

This paper addresses the problem of Bond Graph modelling of nonlinear bioprocesses. The rules for the design of pseudo Bond Graph models of some prototype bioprocesses — one batch and one continuous process — are obtained using the reaction schemes and the analysis of biochemical phenomena. These rules are applied in order to design the Bond Graph model of a complex wastewater treatment process, which is a biomethanation process — bio-degradation with production of methane gas. This bioprocess takes place into a Continuous Stirred Tank Bioreactor. The obtained Bond Graph models and several simulations are conducted using 20sim modelling and simulation environment. This modelling procedure represents a valuable illustration of the power of Bond Graph technique, and can be used as a base for the development of the models of bioprocesses with high level of complexity.

This paper deals with the problem of on-line estimation of kinetic rates inside biological Sequencing Batch Reactors (SBRs). Two wastewater treatment bioprocesses that are carried out inside SBRs are taken into consideration. These biotechnological processes are highly nonlinear and, furthermore, the available on-line measurements are lack and the reaction kinetics is not perfectly known. The unknown kinetic parameters are estimated by using nonlinear observers, based on high-gain approach. The estimation scheme does not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. Numerical simulations are included in order to test the behaviour and the performance of the proposed observers.

This paper presents real, simulated and remote experiments for the process stations, part of a Flexible Manufacturing System, and the benefits of the application of Virtual Reality and Telematics in Electrical Engineering education. An introduction to the basic concepts of Virtual Reality and description of process stations are provided. The real and simulated experiments on a process station are presented. A telematic structure was created for remote experiments with process stations. Finally, the “Virtual Repair” is discussed. All these methods improved the educational process.

This paper deals with estimation and adaptive control strategies for a biotechnological process, which is in fact a lipase production process that takes place inside a fed-batch bioreactor. The lipase production process is highly nonlinear and, furthermore, the available on-line measurements are lack and the reaction kinetics is not perfectly known. On-line state estimation strategies based on extended Luenberger observer and asymptotic observer approach are derived. The unknown kinetic parameters of the bioprocess are estimated by using nonlinear techniques, such as regressive parameter estimator and high-gain observer. The control goal is to maximize the lipase production by controlling the substrate feeding rate. A nonlinear feedback control law is obtained by means of exact linearization technique. By coupling this controller with the parameter estimation algorithms, a nonlinear adaptive controller is obtained. Numerical simulations are included in order to test the behavior and the performance of the proposed estimation and control strategies.

The paper presents the design and analysis of some nonlinear and neural adaptive control strategies for a class of time-varying and nonlinear processes. In fact, a direct adaptive controller based on a radial basis function neural network used as online approximator to learn the time-varying characteristics of process parameters is developed and then is compared with a classical linearizing controller. The controllers design is achieved by using an input-output feedback linearization technique. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics fermentation process, are included to illustrate the behaviour and the performance of the presented control laws.