Search

This work addresses the analysis and design of various Proportional-Integral-Derivative (PID) control techniques for a three-level inverter. Multilevel power converters are modern and basic elements of high-voltage electric drive and power supply systems. By using simulations and specific computer-aided design tools, the overall functional characteristics of multilevel converters, as well as the electrical demands of the components, can be accurately assessed to obtain an appropriate control solution. An innovative and detailed software model of a three-level inverter is developed and then used for the implementation of control techniques. Several tuning methods are used to tune PID controllers for two specific cases: the multilevel inverter with a linear load and with an asynchronous motor load, respectively. A detailed analysis and comparisons of the quality criteria and control performance are achieved. This analysis shows that the choice of controller type depends on the inverter load. For the linear load, proper results are obtained with a PI Nichols-tuned controller, and for the asynchronous load, with a PI controller tuned via a modified Hokushin method. The computer-aided design tools can be further used for the simulation of the equipment in various operating conditions, normal and fault, following all functional parameters.

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.

The paper addresses the modeling and control of the orthogonalization plants for textile industry. An innovative mathematical theory of the textile yarns correction phenomenon, particularly of the 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 2 ns outputs, where 2 ns is the number of the slope sensors. The process analysis shows that we have a non-dynamical nonlinearity, which can be separated from the linear dynamical part. The multivariable closed-loop control system and the equivalent closed-loop control structure are developed. Also, a control algorithm which can be implemented on a numerical process computer is obtained.

Nowadays, the domain of biotechnology is characterized by rapid changes in terms of novelty and by highly complex processes that require advanced procedures for design, operation and control. From the engineering point of view, the control of bioprocesses poses a number of challenging problems. These problems arise from the presence of living organisms, the high complexity of the interactions between the micro-organisms, as well as the high complexity of the metabolic reactions. Moreover, for monitoring and control applications, only a few measurements are available, either because the measuring devices do not exist or are too expensive, or because the available devices do not give reliable measurements. Therefore, we can deduce that the main difficulties arising in the control of bioprocesses arrive from two main sources: the process complexity and the difficulty to have reliable measurements of bioprocess variables (Bastin & Dochain, 1990; Selisteanu et al., 2007a). In order to overcome these difficulties several strategies for the control of bioprocesses were developed, such as adaptive approach (Bastin & Dochain, 1990; Mailleret et al., 2004), vibrational control (Selisteanu & Petre, 2001; Selisteanu et al., 2007a), sliding mode control (Selisteanu et al., 2007a; Selisteanu et al., 2007b), fuzzy and neural strategies etc. Sliding mode control (SMC) has been widely accepted as an efficient method for control of uncertain nonlinear systems (Utkin, 1992; Slotine & Li, 1991; Edwards & Spurgeon, 1998). The classical applications of SMC (such as robotics, electrical machines etc.) were extended to SMC of chemical processes (Sira-Ramirez & Llanes-Santiago, 1994) and to SMC of bioprocesses (Selisteanu et al., 2007a; Selisteanu et al., 2007b). The well-known advantages of the SMC are the robustness, controller order reduction, disturbance rejection, and insensitivity to parameter variations. The main disadvantage of the SMC strategies used in real applications remains the chattering phenomenon, even if some techniques of chattering reduction were developed (Slotine & Li, 1991; Edwards & Spurgeon, 1998). Vibrational control (VC) is a non-classical open-loop control method proposed by Bellman, Bentsman and Meerkov (Meerkov, 1980; Bellman et al., 1986a; Bellman et al., 1986b). Applications of the vibrational control theory can be found for: stabilization of plasma, lasers, chemical reactors, biotechnological processes (Selisteanu et al., 2007a) etc. The VC technique is applied by oscillating an accessible system component at low amplitude and high frequency. Therefore, this technique can be considered, like the SMC, a form of highfrequency control (obviously high-frequency relative to the natural frequency of the system).

The biorefineries, with their recognized capacity to convert in a sustainable manner the biomass into a wide range of bio-based products and bioenergy, are used on an increasing scale in bioindustry. One of the aims of biorefineries is to contribute to the conservation of resources and to the reduction of pollutants. The biorefineries are complex plants, with many challenging problems related to their operation and control. The present chapter addresses innovative issues related to the control of a modern type of biorefinery, which includes two interconnected reactors: an anaerobic digester and a photobioreactor. The anaerobic digestion is used as a main process in wastewater treatment plants. In the photobioreactor, by using the solar energy and a specific microalgae population, the carbon dioxide produced by anaerobic fermentation is converted into organic components. To gradually present the control concepts for the biorefinery, in the first part of the chapter, a description of basic modeling, estimation and control problems, and techniques for bioprocesses is approached. The second part investigates control schemes that are based on special types of software sensors, resulting thus advanced adaptive and robust-adaptive control methods, able to cope with nonlinear, complex, and uncertain plants. Several numerical simulations illustrate the behavior of the estimation and control schemes.

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.

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.

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.

This study addresses the design of advanced control schemes implemented for a continuous fermentation process used to produce ethanol. Due to the inaccuracy of the models that express this complex process, a feasible controller is required to maximize the production of ethanol and to minimize its environmental impact, despite the existence of some significant uncertainties. Therefore, novel estimation and control schemes are designed and tested. These schemes are adaptive control laws including nonlinear estimation algorithms: a sliding mode observer to estimate the unknown influent concentration, but also state observers and parameter estimators used to estimate the unknown states and kinetics. Since the temperature is an important factor for an efficient operation of the process, an algorithm for temperature control in the bioreactor is also developed. To verify the control algorithms effectiveness, several tests performed via numerical simulations under realistic conditions are presented.

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.

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.

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.

This work addresses estimation and advanced control schemes for a complex microalgae anaerobic digestion process, used for wastewater treatment as well as for converting organic waste and biomass into biogas. The control goal is to keep the inhibitory compounds at imposed low levels despite the influent pollutant concentration and load variations. The proposed innovative control schemes work in accurate operating conditions and can cope with harsh assumptions: unknown kinetic rates, unknown feed-in pollutant concentration, unmeasurable state variables. The adaptive and robust-adaptive control laws are based on nonlinear algorithms: a sliding mode observer for the unknown inputs, kinetics parameter estimators, asymptotic and interval state observers. The tests performed under realistic conditions showed that the closed-loop bioprocess is preserved at specific operating points such that the control goal is fulfilled. The state and parameter estimators provide enough on-line information such that the control schemes can handle all the input and kinetic uncertainties.

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a class of anaerobic wastewater treatment processes. The design procedures 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. To achieve these control strategies, firstly, a state asymptotic observer and a robust interval observer for a class of bioprocesses are presented. Secondly, by using these observers, adaptive, robust and robust-adaptive control schemes are developed and analyzed. The adaptive control structure is achieved by combining a linearizing control law with a state asymptotic observer and with a parameter estimator used for on-line estimation of unknown kinetics. The robust control structure is designed by combining a linearizing control law with an interval observer able to estimates a lower and an upper bound of unmeasurable states. Also, the uncertain process parameters are replaced by their lower and upper bounds assumed known. The robust-adaptive controller uses a linearizing control law coupled with an interval observer for the unmeasured states, and with a parameter estimator. These approaches are applied to a particular wastewater treatment bioprocess based on anaerobic fermentation. The effectiveness of the proposed algorithms is validated by several numerical simulations.

This paper deals with the design of a system for decisional support dedicated to an anti-hail system. It is proposed a specific system for Romania anti-hail network, which completes the existing systems with specific information in order to identify the best solutions for both coordination and launch. The main topics refer to the system structure, information handled in the system and reconstruction of a signal based on Bayes criterion. The system comprises two main components: a subsystem of launch decision and a subsystem for assisting the launch decision. In order to achieve this system, the following important issues are considered: the use of communication via GPRS, monitoring the parameters throughout all operating period, log-values, status and alarms, operator actions logs, friendly graphical interface and the generation of tabular and graphical reports for any period. The system enables an increased efficiency by shortening the time for action, a good organization and a high degree of security.

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

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 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.

Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies.

This work deals with the design and analysis of a nonlinear model predictive control (NMPC) strategy for a lactic acid production that is carried out in two continuous stirred bioreactors sequentially connected. The adaptive NMPC control structure is based on a dynamical neural network used as on-line approximator to learn the time-varying characteristics of process parameters. Minimization of a cost function depending on control inputs is realised using the Levenberg-Marquardt numerical optimisation method. The effectiveness and performance of the proposed control strategy is illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying and completely unknown.

This work deals with the design and analysis of some nonlinear and neural adaptive control strategy for a lactic acid production that is carried out in continuous stirred tank bioreactors. An indirect adaptive controller based on a dynamical neural network used as 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. The effectiveness and performance of both control algorithms are illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying and completely unknown.

In this chapter, some classical, adaptive and neural control strategies for a simple planar robotic manipulator with two revolute joints were designed and implemented. First, the conventional computed-torque method was discussed. This control method solves the precision tracking problem, by using an exactly linearization of the nonlinearities of the manipulator model. The main disadvantage is the assumption of an exactly known dynamic model. If the model is imprecise known, it is necessary to design adaptive and/or neural control strategies. Direct and indirect adaptive controllers have been studied and implemented in order to preserve the tracking performances when parameter uncertainties occur. From the simulation point of view, it can be noticed that the evolution of tracking errors remains good, even if the estimated parameters are used in the control law. Also, three neural based control strategies were developed: a feedforward neural controller, a feedback neural control scheme, and a feedback error based neural controller. The simulations showed that the proposed neural controllers obtain results comparable to those achieved using adaptive control strategies. If a classical (PD or computed-torque) controller already controls a manipulator the advantage of proposed neural structures is that extension to a neural controller for performances improvement is easy.

This work deals with the design and analysis of a nonlinear and neural adaptive control strategy for an anaerobic depollution bioprocess. A direct adaptive controller based on a radial basis function neural network used as 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. Numerical simulations, conducted in the case of a strongly nonlinear, time varying and not exactly known dynamical kinetics wastewater biodegradation process, are included to illustrate the behaviour and the performance of the presented controller.

The paper studies the design and analysis of some nonlinear and neural adaptive control strategies for a wastewater treatment process, which is an activated sludge process with nonlinear, time varying and not exactly known kinetics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed and then is compared with a classical linearizing controller. The neural controller design is achieved by using an input-output feedback linearization technique.

The paper presents the work for development of a telematics application for robotics field. First, it was developed a client-server application and then a Web based application. A range of remote experiments with a mobile robot with particular emphasis on motor control, obstacle avoidance, image processing and their application to trajectory control can be done by the users. The users learn the microcontroller programming, sensor use, path planning, trajectory following. The idea is for the users to be able to perform real experiments, in real time, on real equipment, but over the Internet. The remote user is connected by Internet to a dedicated computer used for controlling and monitoring the mobile robot.

This paper presents a fuzzy logic controller for a shape memory alloy (SMA) tentacle robotic structure. A tentacle manipulator is a manipulator with a great flexibility and a distributed mass and torque that can take any arbitrary shape. Technologically, such systems can be obtained by using a cellular structure for each element of the arm. In the first part of the paper, the mathematical model of single unit SMA actuator and conventional PI controller are presented. Secondly, a mathematical model for the proposed SMA tentacle module is described. In the final part, a fuzzy controller is developed and applied to the SMA tentacle structure. The fuzzy control strategy is based on the so-called Direct Sliding Mode Control (DSMC). A fuzzy controller is proposed and the fuzzy rules are established by using the DSMC procedures. Conventional control structures offer better performance in term of time response, but they don't guarantee repeatability without ambient temperature control.