Eugen Bobasu

Modelling of the rotary inverted pendulum system

This paper presents an approach on the bond graph modelling applied to a Quanser rotary inverted pendulum experiment followed by the computation of the mathematical model. The system was decomposed into subsystems that were modelled separately. The obtained subsystems generated submodels and the overall model was then built up by combining these separate structures. The model was built up and simulated using BondSim environment.

Bond graph modelling of a wastewater biodegradation bioprocess

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

Dynamics properties and control for oilwell drillstrings

It is considered the model with distributed parameters of the torsional vibrations for the drilling system. The basic model is obtained by using the generalized variational principle of Hamilton and it is completed by adding the dynamics of the driving motor in two cases: a d.c. motor and an induction motor. For this setup there is considered the steady state with constant rotating speed, the system in deviations and its stabilization using an energy like control Liapunov functional suggested by the energy identity which is specific to partial differential equations. The paper contains also comments on mathematical issues connected with the basics of the approach and suggestions for future research.

Current Control Of A VSI-FED Induction Machine By Predictive Technique.

The paper deals with a technique which uses the predictive concepts in order to obtain the pulse width modulation strategy of a voltage fed inverter. After the technique is briefly described, it is applied for the case when the inverter supplies an induction motor, the reference values of the currents being obtained from a classical vector control scheme. The described technique is then simulated and the waveforms are compared with ones obtained with preset currents (bangbang) pulse width modulation, as the behaviour of the two strategies are similar. Finally, the results are cross analysed and further actions are proposed for the work continuation.

Predictive Versus Vector Control Of The Induction Motor

The paper deals with the vector control and predictive control of the induction motor. For the vector control, the rotor flux oriented one is pointed out, with highlight on the voltage source inverter type. The influence of the most important parameter variations (e.g. stator resistance) is discussed. A simple (and practical) method for avoiding these influences is presented, based on proper simulation models. Following the basics of the predictive control, a simulation model for this type of command is presented, together with simulations results. Finally, the results are cross analysed and further actions are proposed the work continuation. INTRODUCTION On one hand, since the basic work concerning torque and field control due to Leonhard, Blaschke and their followers in the 1970s, the AC drives became a competitive technology with respect to the traditional one, based on DC drives. In rotating references, solidar with the rotor flux, stator flux or magnetizing flux respectively, there is an obvious decoupling between the two components of the stator current: while the direct component acts on the flux modulus only and produces the reactive component, the quadrature component generates the torque, being the active component. The two components of the stator current may be thus controlled independently and the flux and torque generation are thus decoupled, similarly to the DC motor. Due to results simplicity, the rotor flux orriented control has imposed almost as a standard. From here, two types of control were engineered. On one hand we have the direct control drives, where flux position and modulus are known while the reactive and active components of the stator current are computed in the proper reference frame using the set-point torque and flux. On the other hand we have the indirect control drives, where the slip frequency is computed and imposed without direct knowledge of the flux, while the reference system change from the flux-reference to stator-reference one is performed by integration of the sum of the motor speed and the speed corresponding to the computed slip (Casadei et al. 2002, Vas 1998). A very simple method for the toque control is also the Direct Torque Control (DTC), suited for electrical traction applications (Takahashi and Noguchi 1986, Baader et al. 1992, Ehsani et al. 1997, Faiz et al. 1999, Haddoun et al. 2007, Ivanov 2009, Ivanov 2010). On the other hand, the increased computational capabilities of the existing DSP allow the implementation of the predictive control at the level of the converters which induce the hybrid character of the overall control system of the drive. We infer that predictive control has established itself in the last 5-7 years as a very proficient form of controlling highly nonlinear and uncertain systems; moreover the most recent results show its applicability to fast processes among which drives and their converters have a central position (Seo et al. 2009, Prieur and Tarbouriech 2011, Geyer et al. 2008, Mariethoz et al. 2010, Geyer et al. 2009, Trabelsi et al. 2008, Shi et al. 2007, Rodriguez et al. 2007, Larrinaga et al. 2007, Richter et al. 2010, Almer et al. 2010). The paper will briefly present in the first section the basics of the vector control for the rotor flux oriented control for voltage source inverter, with highlight on the influence of the parameters variations on the drive performance. A simple method for reducing these influences will be discussed based on appropriated models. The basics of the predictive control will be presented in Section 2. Section 3 will analyse the predictive control applied to the induction motor, based also on a Simulink model. Finally, conclusions will be issued and ideas for continuation will be pointed out. VECTOR CONTROL OF INDUCTION MOTOR As stated above, the vector control strategy most often used is the rotor flux oriented one. The reasons reside in the simplicity of the expressions resulted from the rotor voltage equation which mainly gives the rotor flux speed and further, by integration, the rotor flux position, used at its turn for the transformation of the reference currents/voltages from the rotary frame to the stationary one. For the squirrel cage induction motor, the rotor voltages equation in terms of phasors is ( ) 0 r r r mr r r r r r d R i j P dt Ψ Ψ Ψ Ψ = + + ω − ω Ψ , (1) where Rr is the rotor resistance, r r i Ψ is the rotor current, m ω is the rotor flux speed, ω is the mechanical speed of the rotor and P is the number of pairs of poles. The Ψ subscript highlights that (1) is expressed in the rotary frame synchronous with the rotor flux r r Ψ Ψ . By assuming unsaturated operation (realistic hypothesis when the stator currents are precisely controlled), the rotor flux expressed in terms of magnetizing inductance Lm and rotor magnetizing current mr i is m r mr L i Ψ = ⋅ Consequently, (1) becomes ( ) 0 mr r m mr r m r r mr d i R i L j P i L dt Ψ = + + ω − ω ⋅ ⋅ . (2) The rotor current r r i Ψ , being immeasurable for the squirrel cage motor, is expressed in terms of the stator current s r i Ψ and the magnetizing one. By denoting the rotor time constant / r r r T L R = , (2) becomes ( ) mr r mr r r mr s r mr d i T i i j P T i dt Ψ + = − ω − ω , (3) Lr being the total rotor inductance which includes the leakages ( r m r L L Lσ = + ). By identifying the terms on each of the axes d, q, the following two expressions result which are the simplest among all the vector control types mr r sd mr d i T i i dt + = , (4) sq mr r r mr i P T i ω = ω + . (5) We notice from (4) that if the flux is kept constant ( ct. mr i = ), then = ct. sd mr i i = As the electromagnetic torque expressed in the rotor flux oriented frame is 2 3 2 m e sd sq r L t P i i L = ⋅ , (6) from (5) and (6) results that the slip speed (term 2 in (5)) is proportional with the torque and further, the mechanical characteristic of the induction motor are straight lines, quite similar to the DC motor. When the motor is supplied by a voltage source inverter, the necessary voltages are obtained by considering the stator voltages equation expressed in the same rotary frame synchronous with the rotor flux r r Ψ Ψ : s r r r s s m s r s r mr s r m s r r r d i di u R i L L dt dt j L i j L i Ψ Ψ Ψ Ψ

Modeling of culture cells for pharmaceutical industry applications

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

Parameter identification of bacterial growth bioprocesses using heuristics for global optimization

A metaheuristic is a general algorithmic framework which can be applied to different optimization problems with relative few modifications to make them adapted to a specific problem. This work describes a dynamic mathematical model for Bacterial growth bioprocess containing 9 unknown parameters, which were calibrated using particle swarm optimization and genetic algorithms through the minimization of an evaluation function. Two kinetic expressions, the Monod and Haldane equations, commonly employed to describe microbial growth were tested in the model simulations. The identification problem is formulated as a multi-modal numerical optimization problem with high dimension. The performances of the two methods are analyzed by numerical simulations.

Nonlinear Control Strategies for Bioprocesses: Sliding Mode Control versus Vibrational Control

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).

Parameter estimation of bioprocesses via parallel particle swarm optimization

Modeling of biotechnological systems is an important research area. The most challenging approach is to build non-linear state space models for these systems. In this work the parameters of a bacterial growth bioprocess are estimated using prediction error method. Prediction error methods are widely used in parameter estimation both for linear and nonlinear models and consist in minimization of the distance between measured and modeled data in a suitable norm. Because these problems are solved using numerical algorithms that are time consuming, in this paper a parallel particle swarm optimization technique is used in order to numerically solve the minimization problem. The algorithm is implemented on a multicore processor and the performances of this approach are presented by numerical simulations.

Direct torque versus predictive control of the induction motor

The direct torque control (DTC) is the preferred control technique of the induction motor in electric traction applications. It has advantages, as it is a sensorless control technique, but also disadvantages. The main disadvantage is related to the switching frequency which is the same with the sampling frequency of the entire control loop. This determines high requirements for the control system which must be quite performant. The control is simulated and the results are compared with the ones obtained when the predictive concepts are used for obtaining the pulse width modulation strategy of a voltage fed inverter which supplies an induction motor, as the both determine, during each sampling period, the next stator voltage phasor, but considering different criteria. The reference values of the currents are obtained from a classical rotor flux vector control scheme. The described technique is similar with the preset currents (bang-bang) pulse width modulation technique, but it has the advantage of fixed switching frequency.