Florin Stinga

Robust model predictive control of an induction motor

The problem of modeling and control of an induction motor is approached by using linear approximation of the nonlinear model at certain equilibrium points and robust predictive control approaches. An advanced control technique for automatic control of the processes under operating constraints and uncertainties, robust model predictive control, was used in this paper. Finally, simulation results are presented, in order to demonstrate the effectiveness of the proposed control algorithm.

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

Predictive control of torsional drillstring vibrations

In this paper, the control problem of torsional vibrations occurring along a rotary drillstring system is approached. This task is performed using a predictive control strategy, based on a discrete-time model of the system, taking into account the constraint on control input. Also, the control scheme uses a compensation friction method in order to use a linear part of the initial model for control purpose. The simulation results validate the effectiveness of the proposed control algorithm.

Online estimation and control of an induction motor

This paper presents the modeling, estimation and control of an induction motor. Starting with the linear approximation of the original nonlinear model of the motor, an on-line parameter estimation algorithm was developed. The algorithm deals with stator windings variations due to temperature oscillations. The resulted linear model was used, in order to control the rotor speed of the induction motor, based on a model predictive algorithm. The predictive control is an advanced control technique of the processes under operating constraints and uncertainties. The simulation results validate the effectiveness of the proposed algorithms.

Multiple predictive control of an anaerobic digestion process of microalgae

This paper proposes a multiple predictive control strategy in order to control a combination of substrate concentrations inside of an anaerobic digestion process of microalgae. Based on the linear approximation of the original nonlinear model, an optimal control algorithm was developed and tested by simulation experiments. The obtained results validate the effectiveness and robustness of the proposed algorithm.

An Embedded System Implementation Of A Predictive Algorithm For A Bioprocess.

Florin Stîngă*, Marius Marian**, Valentin Kese***, Lucian Bărbulescu** and Emil Petre* *Department of Automation and Electronics **Department of Computers and Information Technologies Faculty of Automation, Computers and Electronics University of Craiova ***Softronic Group Craiova, Romania E-mail: [florin, epetre]@automation.ucv.ro, E-mail: [marius.marian, lucian.barbulescu]@cs.ucv.ro, E-mail: kesevalentin@gmail.com

Predictive And Feedback Linearizing Control Of Chlamydomonas ReinhardTII Photoautotrophic Growth Process.

It is well known that water is one of the essential elements of life being an important resource both for industrial applications and domestic usage. Lately, many environmental laws and directives have been enforced in order to decrease the industrial and urban pollution. This situation has lead to an increase in the use of wastewater biological treatment processes using anaerobic digestion. This fermentation bioprocess is very important since it produces valuable energy (methane) besides removing the organic pollution from the liquid influent. It is useful for concentrated wastes such as agricultural and food industry wastewater (F. Angulo et al. 2007). Nevertheless, its main drawback is the production of carbon dioxide (CO2) and its easy destabilization, giving rise to the disappearance of the methanogenic bacteria (G. Bastin and D. Dochain 1990; O. Bernard 2004). Therefore in the last decade the researchers have been looking for solutions to improve the efficiency in the pollution reduction and for CO2 mitigation (O. Bernard 2011; G.A. Ifrim 2012; G.A. Ifrim et al. 2013, S. Tebbani et al. 2014). A recently used solution consist in the growth of some microalgae populations that by using light as source of energy are able to assimilate inorganic forms of carbon (CO2, 3 HCO  ) and to convert them into requisite organic substances for cellular functions, generating at the same time oxygen O2 (G.A. Ifrim 2012; G.A. Ifrim et al. 2013, S. Tebbani et al. 2014; S. Tebbani et al. 2013; S. Tebbani et al. 2015). The control of such processes remains a key issue for the improvment of stability and process efficiency. A difficulty for the design of high-performance control techniques of such living processes lies in the fact that, in many cases, the models contain kinetic parameters and/or yield coefficients that are highly uncertain and time varying (G. Bastin and D. Dochain 1990; O. Bernard 2004; D. Dochain and P. Vanrolleghem 2001; D.J. Batstone et. Al 2002; F. Mairet et al. 2011; F. Mairet et al. 2011a). Another problem in the control of these processes is finding adequate sensors used for measuring all the important state variables (G. Bastin and D. Dochain 1990; O. Bernard 2011). The problem becomes of great importance in complex systems like wastewater treatment plants, where critical instability of the process must be avoided, making the monitoring of the system variables an important issue (F. Angulo et al. 2007; G.A. Ifrim et al. 2013; D. Dochain 2008; E. Petre et al. 2013). To surmount these difficulties, numerous control strategies were developed such as linearizing feedback (F. Angulo et al. 2007; G.A. Ifrim et al. 2013; I. NeriaGonzález et al. 2009), adaptive and robust-adaptive approach (G. Bastin and D. Dochain), predictive an optimal control (F. Logist 2011; S. Tebbani et al. 2014), sliding mode (D. Selişteanu et al. 2007), and so on. This paper presents the design of predictive and feedback linearizing control methods for a complex photoautotrophic growth process that is carried out in a torus photobioreactor numerical simulation are performed in order to validate the proposed control algorithms.