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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.

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).

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.

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 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 depollution 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.

This paper presents the design and analysis of a multivariable robust-adaptive control strategy for a continuous aerobic wastewater treatment bioprocess. The design procedure is developed under the realistic assumption that the bacterial growth rates are uncertain and the influent flow rates are disturbed, but some lower and upper bounds of these uncertainties (possibly time varying) are known. The control structure is achieved by combining a linearizing control law with an interval observer able to estimate lower and upper bounds of unmeasurable states. The performance of the control algorithms are illustrated by simulations performed for a recycled wastewater treatment bioprocess for which kinetic dynamics are highly nonlinear, time-varying and incompletely known.

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.

The paper presents a different approach on biomass combustion process modelling based on Bond Graph methodology. The main thermal-chemical processes used for biomass to energy conversion are oxidative. This study focused on oxidation process with heat generation using solid fuels. The important number of biomass categories and different conversion technologies influence the model input parameters in terms of temperatures, mass flows, heat exchange, requiring different models for combustion processes. The large range of biomass based products can be represented by carbon, hydrogen, oxygen, chlorine, sulphur, and nitrogen composition. In order to highlight the modelling method advantages, the research focused on wood biomass combustion. The product was represented by carbon, hydrogen, and oxygen composition given through their molar fraction. The paper presents the results on combustion process kinetics with respect to reactant and reactor input data, especially for the transitory regimes like ignition. The model provides information on time variation of the heat of reaction, reaction products concentration, and reactants concentration / accumulation, based on global mass and energy balance of the process.

This paper presents tuning relations for controllers in time delay systems based on shape coefficients method. The time scale factor is determined to fit the desired shape of the closed loop response to the time delay controlled plant by the so-called closing equation. The method can be applied to processes with both input-output delays and internal delays. Explicit tuning relations are obtained for PID controllers. Some comparative results are presented to point out the advantages and limits of the method.

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Pateikti realaus laiko nuotekų valymo proceso, vykstancio bioreaktoriuje, vertinimas ir adaptyvusis valdymas. Sis procesas yra netiesinis, naudojami būsenų matavimai turi trūkumų ir reakcijos kinetika nėra tiksliai žinoma. Kinetiniai parametrai įvertinami naudojant didelio stiprinimo netiesinius stebiklius, o nesamos būsenos yra rekonstruojamos naudojant asimptotinį būsenų stebiklį. Valdymo tikslas – palaikyti žemą tersalų koncentracijos lygį. Adaptyviojo valdymo taisyklė yra sudaryta kombinuojant tiesinimo valdiklį su netiesiniais stebikliais. Siūlomų algoritmų veiksmingumui nustatyti buvo atliktos skaitmeninės imitacijos. Il. 8, bibl. 17 (anglų kalba; santraukos anglų ir lietuvių k.). DOI: http://dx.doi.org/10.5755/j01.eee.117.1.1047

This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production, which is carried out in continuous stirred tank bioreactors. Firstly, an indirect adaptive control structure based on the nonlinear process model is derived by combining a linearizing control law with a new parameter estimator. This estimator is used for on-line estimation of the bioprocess unknown kinetics, avoiding the introduction of a state observer. Secondly, a tuning procedure of estimator design parameters is achieved by stability analysis of the control scheme. The effectiveness and performance of estimation and 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, and not all the state variables are measurable.

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 presents several issues concerning the modelling, kinetic estimation and nonlinear control of a baker’s yeast process that takes place inside a fed-batch bioreactor (FBB). First, a nonlinear model of the bioprocess is presented taking into consideration two approaches: the classical modelling scheme and the bond graph method. Second, to reconstruct the missing on-line kinetic information, estimation strategies are proposed. Finally, by using the process dynamical model and the on-line estimation strategies, an adaptive control scheme is designed in order to maintain a certain level of ethanol concentration, regardless of uncertainties and disturbances. This objective is attained by designing an adaptive controller as a combination between a linearizing control law and a high-gain observer. Numerical simulations are provided to illustrate the behaviour of the proposed estimation and control techniques.

It is well known that the biotechnology is one of the fields that over the last three decades has a very high and quick development. Therefore, due to their advantages, the control of industrial bioprocesses has been an important practical problem attracting wide attention. The main motivation in applying control methods to such living systems is to improve their operational stability and production efficiency. The operation in Stirred Tank Reactors (STR) has been and it is still a widely used technology in fermentation bioprocesses. But, other new technologies such as fixed bed, fluidized bed or air lift reactors, are considered for bioprocesses operation. These reactors present several advantages over the “classical” STRs. For instance, the fixed bed and fluidized bed reactors are characterized by higher production performance, i.e. larger production capacity and higher productivity (Bastin & Dochain, 1990; Bastin, 1991; Bouaziz & Dochain, 1993). From mathematical point of view, the dynamics of these processes are characterized by partial differential equations and therefore are classified as distributed parameter systems (Bastin & Dochain, 1990; Bouaziz & Dochain, 1993; Christofides, 2001; Dochain et al., 1992). For instance, the concentrations of the reactants and products are not anymore homogeneous in the whole reactor, like in STRs, but are characterized by a spatial profile along the reactor. It is clear that the distributed parameter feature of these systems makes the control problem even more difficult (Bouaziz & Dochain, 1993; Christofides, 2001; Dochain et al., 1992; Petre & Selisteanu, 2007a; Slotine & Li, 1991). Therefore, the modelling and simulation of them, which are the objectives of this chapter, are associated with the formulation of the process model using partial differential equations (PDEs), in general nonlinear, and with the computation of the solution. Since in the most of cases for this kind of systems, with the exception of a few simple cases, there are no analytical methods for finding the solution of the involved equations, the following alternatives are commonly employed (Christofides, 2001; Petre & Selisteanu, 2007a; Slotine & Li, 1991; Vilas, 2008): To assume that these processes behave like lumped parameter systems (the states are only time dependent). To use classical numerical methods like finite differences, finite elements or finite volumes. These methods are based on discretization techniques which allow us to approximate the infinite set of numbers that represent a continuous function by means of a finite set of

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 presents some aspects regarding modelling and control of some robotics rotational experiments: flexible beam and inverted pendulum experiments. The experiments were realised using WinConTM application that allows running code generated from a Simulink diagram in real-time. For the model describing the flexible beam experiment the control goal was to achieve the flexible beam position control and to damp the arm vibrations. The inverted pendulum experiment objective was to design a feedback control system that positions the arm as well as maintains the inverted pendulum vertical. Both experiments are highly nonlinear and consequently, the real mathematical models of the systems are very complicated, so for control purpose simplified models were used. Using the formulas of the kinetic and potential energies, from the generalized dynamic equations one obtained approximated linear models expressed by ordinary differential equations. Nonlinear systems model imprecision compensation and perturbations rejection were achieved using the robust controllers design. The LQG/LTR method was used in order to obtain feedback controllers for the benchmark robotic experiments. The aim of these controllers is to achieve robust stability margins and good performance in step response of the system. LQG/LTR method is a systematic design approach based on shaping and recovering open-loop singular values. The control strategies required the use of all state variables. Many of the proposed control strategies suppose that the state variables are available; this fact is not always true in practice so, it was necessary to design a state observer. The LQG/LTR control method and the modified Utkin SMO were designed and implemented. Sliding mode observers differ from more traditional observers e.g. Luenberger observers, in that there is a non-linear discontinuous term injected into the observer depending on the output estimation error. These observers are much more robust than Luenberger observers, as the discontinuous term enables the observer to reject disturbances. The Lyapunov based SMO (the so-called Walcott-Zak observer) provides exact estimation for certain class of nonlinear systems under existence of certain class of uncertainties. The difficulty in finding the design and gain matrices is the main drawback of this observer. A negative output feedback term was added to each equation of the Utkin observer and this result in a new error system. The addition of a Luenberger type gain matrix, feeding back the output error, yields the potential to provide robustness against certain classes of uncertainty. The problem considered was that of reconstructing the state variables using only measured output information. For the flexible beam experiment a LQG/LTR controller was developed in order to achieve the flexible link position control and to damp the arm vibrations. The LQG/LTR controller uses the state estimations from a sliding-mode observer. A lot of experiments using the Quanser rotational experiments show that the modified Utkin sliding-mode observer provides better results than the classical Utkin sliding-mode observer. The results show also good angle reference tracking and vibration suppression. For the inverted pendulum experiment a LQG/LTR controller was developed also in order to maintain it upright. The non-measurable state variables are obtained using the modified Utkin SMO. The robustness of the controller is tested to some perturbations. The efficiency of the control-observer

The growth rates of the micro-organisms in bioreactors are described by complex kinetic expressions, and the modelling and estimation of such kinetics are essential for the design of control strategies. This study deals with the online estimation of kinetic rates of a baker's yeast process, taking place inside a fed-batch bioreactor. This bioprocess is widely used in bioindustry; its model is highly non-linear and, furthermore, the available online measurements are missing and the reaction kinetics is not perfectly known. The unknown kinetics is estimated by using non-linear observers, based on high-gain approach. Two high-gain observers are designed and implemented: one for the specific growth rates and the other for the reaction rates of the baker's yeast bioprocess. The multiple estimation schemes do not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. An observer-based estimator is also implemented in order to use it for comparisons. Numerical simulations are included to test the behaviour and the performance of the proposed observers.

This paper presents the design of a nonlinear multivariable adaptive control strategy for a class of wastewater depollutionbioprocesses that are carried out in recycle bioreactors. The adaptive control structure is achieved by combining a linearizing controllaw with a new state observer and a parameter estimator, which play the role of the software sensors for on-line estimation of biologicalstates and parameter variables of interest of the bioprocess. Simulation results are included to illustrate the performance of bothestimation and control algorithms.

This paper presents the control problem of a class of propagation bioprocesses that are carried out in fixed bed reactors. Since the dynamics of these processes are described by partial differential equations, in order to obtain useful models for control purposes, a possible method consists of approximation of their infinitely order associated models by finite order models. A class of nonlinear adaptive controllers are then designed based on these finite order models, which consist of a set of ordinary differential equations obtained here by orthogonal collocation method. Computer simulations conducted in the case of a fixed bed reactor are included to illustrate the performances of the proposed adaptive controllers.