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This paper explores new methods to increase the level of safety of data transfer between sensors and electronic control units (ECUs) in automotive communication. A new model of basic sensors to be used in automotive electronics is proposed. This model contains hardware modules that implement the end-to-end communication protection (E2E) mechanism, as defined by the Automotive Open System Architecture (AUTOSAR) standard. By adding this feature inside the sensors, it is possible that, in addition to increasing the safety level, these sensors can be directly connected to the network ECUs via standard communication buses (e.g., Local Interconnect Network (LIN), Controller Area Network (CAN), Flexray, etc.). This paper describes the model, design, and mapping (in a Field Programmable Gate Array device (FPGA)) of the hardware E2E module capable of generating the Cyclic Redundancy Code (CRC) and counter signal for a customized message. This message represents the output of the new sensor E2E module used in a safety communication as requested by the automotive E2E standard. The model is validated also by comparing the data output of the E2E hardware with the data output of the AUTOSAR software E2E library. Finally, future needs and directions are suggested in this area.

The vehicle suspension system is represented by a complex group of components which connect the wheels to the frame or body. Its primary function is to reduce or absorb various vehicle vibrations generated by road disturbances, providing comfort and safety for passengers. Most modern vehicles have independent, active, or semiactive front and rear suspensions which allow the use of electronic actuation. For this reason, automotive engineers conduct research on the active suspension model to determine the most suitable control algorithm. Three active suspension models are intensely used within simulations: the quarter-car, the half-car, and the full-car models. This paper proposes an adaptive harmonic control for a half-car active suspension system. The mathematical model of the suspension system is implemented in the MATLAB/Simulink environment. The control approach is tested via simulation, and several comparisons with the classical proportional-integral controller are provided. The simulation results show that the controller behaves quite similarly on the half-car model as it did on a quarter-car model. Additionally, an improvement to the harmonic control algorithm has been accomplished.

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition monitoring assurance, and assessments of the need for maintenance activities) require the design of reliable and efficient devices or systems. The present work describes the design and implementation procedure of an Industrial Internet of Things (IIoT) system configured for a basic micro hydropower plant architecture and assuring simple means of customization for plant differences in structure and operation. The designed system features a set of commonly used functions specific to micro hydropower exploitation, providing maximum performance and efficiency.

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.

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price–performance ratio makes it an original and handy solution for electric power infrastructures.

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 article presents the control of a grid connected PV (GC-PV) array system, starting from a benchmark. The control structure used in this article was a cascade-type structure, in which PI or synergetic (SYN) controllers were used for the inner control loop of id and iq currents and PI or sliding mode control (SMC) controllers were used for the outer control loop of the udc voltage from the DC intermediate circuit. This paper presents the mathematical model of the PV array together with the main component blocks: simulated inputs for the PV array; the PV array itself; the MPPT algorithm; the DC-DC boost converter; the voltage and current measurements for the DC intermediate circuit; the load and connection to power grid; the DC-AC converter; and the power grid. It also presents the stages of building and training the reinforcement learning (RL) agent. To improve the performance of the control system for the GC-PV array system without using controllers with a more complicated mathematical description, the advantages provided by the RL agent on process controls could also be used. This technique does not require exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the control system performance for the GC-PV array system, both when using simple PI-type controllers or complex SMC- and SYN-type controllers, was achieved using an RL agent based on the Deep Deterministic Policy Gradient (DDPG). The variant of DDPG used in this study was the Twin-Delayed (TD3). The improvement in performance of the control system were obtained by using the correction command signals provided by the trained RL agent, which were added to the command signals ud, uq and idref. The parametric robustness of the proposed control system based on SMC and SYN controllers for the GC-PV array system was proven in the case of a variation of 30% caused by the three-phase load. Moreover, the results of the numerical simulations are shown comparatively and the validation of the synthesis of the proposed control system was obtained. This was achieved by comparing the proposed system with a software benchmark for the control of a GC-PV array system performed in MATLAB Simulink. The numerical simulations proved the superiority of the performance of control systems that use the RL-TD3 agent.

The field-oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI-type controllers. In addition to their low complexity (an advantage for real-time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of id and iq currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor-load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC-type strategy, both when using simple PI-type controllers or in the case of complex SMC or synergetic-type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC-type strategy, both in the case of simple PI-type controllers and complex SMC or synergetic-type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL-DDPG.

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.

BACKGROUND This work addresses the design of robust adaptive control strategies for a lactic fermentation process carried out inside a continuous stirred tank reactor (CSTR). This bioprocess is widely used in bioindustry for lactic acid production, and it is characterized by strong nonlinearities and uncertainties. Under the realistic assumption that the reaction kinetics and the influent flow rates are unknown and time-varying, but lower and upper bounds of the uncertainties are known, two innovative robust adaptive control structures are proposed. RESULTS The control strategies are designed by combining a linearizing control law with two novel parameter estimators able to estimate the unknown reaction rates. The first strategy uses a modified observer based estimator and the second one utilizes an interval parameter estimator. The control objective is to adjust the plant load so as to convert the glucose into lactic acid via fermentation, which is directly correlated with the economic aspects of lactic acid production. Numerical simulations are performed in order to validate the proposed solution. CONCLUSION By using the proposed control strategies the process is maintained at certain operating points that correspond to a maximum lactic production rate and a minimal residual glucose concentration. The simulations performed by considering various uncertainties and noisy measurement data show that the proposed novel robust adaptive controllers are suitable to control this kind of fermentation processes. © 2017 Society of Chemical Industry

In this work, the nonlinear dynamical model of a lactic fermentation process is widely analysed and control experiments are achieved. More precisely, a production of yogurt by Streptococcus termophilus and Lactobacillus bulgaricus in batch operation is taken into consideration. The process model is expressed by a set of nonlinear differential equations that describes the evolution of concentrations in the fermentation process. To validate the model, several simulations are performed in the Matlab programming and development environment. Furthermore, two experimental setups are used for batch fermentation experiments. From control point of view, the temperature and the pH are the basic dynamical factors that need monitoring and control in order to regulate the microbial growth and the lactic acid production. Different control architectures and tuning procedures are implemented. Specialized data acquisition and control software tools are used to perform the experiments. By using the features of these software tools, the time evolution of various process variables can be plotted and analysed. Several comparisons between the results obtained via simulation and with the two bioreactor setups are achieved.

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.