Academic literature on the topic 'Predictive motor control'

The work is devoted to the study of the possibilities of using the predictive torque control system instead of the currently widely used direct torque control system. Aspects of using the system of direct torque control of an induction motor are considered and it is found that its significant disadvantage is the variable frequency of semiconductor switches. As a further development of the direct torque control system, a predictive torque control system is analyzed, which contains blocks for estimating unmeasured state variables, as well as predicting the state of a dynamic system when applying possible control signals. The systems were compared by mathematical modeling in Matlab / Simulink.

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical–subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

Despite of its remarkable active performances, brushless DC motors, which are widely used in mechanical engineering, have an obvious disadvantage in its high electromagnetic torque ripples. In the paper, a ripple suppression method based on predictive controls of stator currents is proposed according to analysis of causes electromagnetic torque ripples generate in commutation periods of brushless DC motors. First of all, a relative accurate prediction is acquired through DC motor on-line parameter corrections based on generalized predictive control algorithms. Then rolling optimizations make tracking errors and control qualities optimized for best control effects. And finally, minimum electromagnetic torque ripples are achieved. The simulation results show that torque ripples can be suppressed effectively with improved reliabilities by using the method proposed.

Although human sustained control movements are continuous in nature there is still controversy on the mechanisms underlying such physiological systems. A popular topic of debate is whether human motor control mechanisms could be modelled as engineering control systems, and if so, what control algorithm is most appropriate. Since the early years of modelling sustained control tasks in human motor control the servomechanism has been an adequate model to describe human tracking tasks. Another continuous-time system model that is often used to model sustained control tasks is the predictive controller which is based on internal models and includes prediction and optimisation. On the other hand, studies have suggested intermittent behaviour of the ``human controller'' in sustained motor control tasks. This thesis investigated whether intermittent control is a suitable approach to describe sustained human motor control. It was investigated how well an intermittent control system model could approximate both the deterministic and non-deterministic parts of experimental data, from a visual-manual compensatory tracking task. Finally, a preliminary study was conducted to explore issues associated with the practical implementation of the intermittent control model. To fit the deterministic part of experimental data, a frequency domain identification method was used. Identification results obtained with an intermittent controller were compared against the results using continuous-time non-predictive and predictive controllers. The results show that the identified frequency response functions of the intermittent control model not only fit the frequency response functions derived from the experimental data well, but most importantly resulted in identified controller parameters which are similar to those identified using a predictive controller, and whose parameter values appear to be physiologically meaningful. A novel way to explain human variability, as represented by the non-deterministic part of the experimental data (the \emph{remnant}), was developed, based on an intermittent control model with variable intermittent interval. This model was compared against the established paradigm, in which variability is explained by a predictive controller with added noise, either signal dependent control signal noise, or observation noise. The study has shown that the intermittent controller with a variable intermittent interval could model the non-deterministic experimental data as well as the predictive controller model with added noise. This provides a new explanation for the source of remnant in human control as inherent to the controller structure, rather than as a noise signal, and enables a new interpretation for the physiological basis for human variability. Finally, the theoretical intermittent control model was implemented in real-time in the context of the physiological control mechanism of human standing balance. An experimental method was developed to apply automatic artificial balance of an inverted pendulum in the context of human standing, via functions electrical stimulation control of the lower leg muscles of a healthy subject. The significance of this study is, firstly, that frequency domain identification was applied for the first time with intermittent control, and it could be shown that both intermittent and predictive control models can model deterministic experimental data from manual tracking tasks equally well. Secondly, for the first time the inherent variability, which is represented by the remnant signal, in human motor control tasks could be modelled as part of the structure of the intermittent controller rather than as an added noise model. Although, the experimental method to apply automatic artificial balance of an inverted pendulum in the context of human standing was not successful, the intermittent controller was implemented for the first time in real-time and combined with electrical muscle stimulation to control a physiological mechanism.

Induction machine drives with various configurations are getting a lot of attention in several industrial applications. Due to this increasing demand in industrial applications, the significance of developing effective control approaches for obtaining a high dynamic performance from the induction machine drives became essential. Up to the present time, the control of induction machine drives using power converters has been based on the principle of mean value, using pulse width modulation with linear controllers in a cascaded structure. Recent research works have demonstrated that it is possible to use Predictive Control to control induction machine drives with the use of power converters, without using modulators and linear controllers. This new approach will have a strong impact on control in power electronics in coming decades. The advantages of Predictive Control are noticed through the ability to consider a multi-objective case within the model, easy inclusion of non-linearities within the model, simple treatment of system constraints, easy of digital implementation, and flexibility of including modifications and extension of control horizons according to the required applications. Upon this, the research presented in this thesis concerns with developing different control topologies for various configurations of induction machine drives based on finite control set model predictive control (FCS-MPC) principle, which actuates directly the switch states of the voltage source inverter (VSI). In addition, for enhancing the robustness of the induction machine drives, different sensorless approaches are utilized and tested for validations. The first topology of induction machine drives that has been studied is the induction motor (IM) drive. An effective model predictive direct torque control (MP DTC) approach is used to control the torque and stator flux of the motor through the utilization of an effective cost function, through which the understanding and comparing implementation variants and studying convergence and stability issues can be easily investigated. The speed sample effect on the control variants and overall performance of the proposed MP DTC is analyzed, which enables the understanding of the real base principle of DTC, as well as why and when it works well. Two different sensorless procedures for estimating the speed and rotor position are used by the proposed MP DTC approach; the first utilizes a model reference adaptive system (MRAS) observer, while the other exploits the prediction step during the implementation of proposed MP DTC to get the speed information through performing a linear extrapolation of the speed values starting from the last two estimated samples. Extensive simulation and experimental tests have been carried out to validate the effectiveness of both sensorless approaches in achieving precise tracking of speed commands for a wide range of variations. For enhancing the robustness of proposed MP DTC, the stator flux as a control variable is replaced with controlling the flow of the reactive power through the induction motor drive. As the reactive power is a measured quantity compared with the estimated value of stator flux, thus, the sensitivity of the control against parameters variation is limited, and this confirmed through the obtained results from both simulation and experimental tests. In addition, an effective alternative approach to the MP DTC is presented, which based on controlling the instantaneous values of the active and reactive powers of the IM drive based on model predictive principle, instead of controlling the torque and flux as in MP DTC. This technique has the advantage that all controlled variables are became measured quantities (active and reactive powers), thus the estimation problems that commonly present in classic DTC schemes are effectively limited. For the last two control approaches (MP DTC reactive power control, and MP IPCactive and reactive power control), the sensorless that utilizes the predictive feature is also adopted. Obtained results via simulation and experiments confirm the feasibility of the two alternatives control procedures in obtaining a robust dynamic response of IM drive. To limit the accompanied ripple contents in the controlled values of electromagnetic torque and stator flux of induction motor, an effective ripple reduction technique has been presented. The technique is based on the derivation of the optimal value for the weighting factor (w_f) used in the cost function. A detailed mathematical derivation of the optimal value of w_f is introduced based on the analysis of torque and flux ripples behaviors. The proposed ripple reduction technique has been validated via simulation utilizing Matlab/Simulink software, and experimentally tested using a fast control prototyping dSpace 1104 board. In addition, the prediction step based sensorless approach is adopted during implementation. The performance of the IM drive using the proposed approach is compared with the results obtained from MP DTC approach that uses an arbitrary value of w_f. The comparison confirms the validity of the proposed ripple reduction procedure in reducing the ripple contents in the controlled variables while preserving the permissible computation burdens during the implementation. The FCS-MPC principle is also utilized to control the current of induction motor as an alternative to classic field oriented control (FOC), the proposed model predictive current control (MPCC) approach belongs to the class of the hysteresis predictive control (for limiting the switching frequency) as the MPCC is triggered by the exceeding of the error of a given threshold. In addition, a sensorless drive is achieved by including an effective Luenberger observer (LO) for precise estimation of rotor flux vector together with stator current, speed and load torque. The stator currents are estimated to eliminate the accompanied noise in their values when they are directly measured, thus the currents noise during prediction is limited. An effective pole placement procedure for the selection of observer gains has been adopted. The procedure is based on shifting the poles of the observer to the left of the motor poles in the complex (s-plane) with low imaginary part, so that the stability of the observer is enhanced for wide speed range. The feasibility of the sensorless MPCC for IM drive is confirmed through the obtained simulation and experimental results. The second topology of induction machine drives that has been studied is the doubly fed induction motor (DFIM) drive. An effective model predictive direct torque control (MP DTC) algorithm is developed for controlling the torque and rotor flux of DFIM drive. In addition, an effective sensorless approach is presented, which estimates the speed and rotor position in an explicit way without the need for involving the flux in the estimation process, thus the effect of parameters variation on the overall performance of the sensorless observer is effectively limited, this has been approved through the obtained results that are performed for a wide speed range from sub-synchronous to super-synchronous speed operation. During the operation, the stator resistance and magnetizing inductance values are changed from their original values to study the variation effect on the observer performance. Matlab/Simulink software and a prototyping dSpace 1104 control board are used to validate the effectiveness of proposed sensorless MP DTC approach through simulation and experiments, respectively. The results proof the robustness of the proposed sensorless approach and its ability to achieve precise estimation of the speed and rotor position. The third topology of induction machine drives that has been studied is the doubly fed induction generator (DFIG). A detailed analytical derivation for the proposed model predictive direct power control (MP DPC) approach for DFIG is presented, which as a sequence considered as a transposed control approach from the MP DTC used before for doubly fed induction motor (DFIM). A sensorless approach based on model reference adaptive system (MRAS) observer is adopted for estimating the speed and rotor position. Both simulation using Matlab/Simulink software and experimental test using a prototyping dSpace 1104 control board have tested the dynamic performance of the drive. Obtained results affirm the feasibility of the proposed MP DPC approach in achieving a decoupled control of active and reactive powers for DFIG. In summary, it can be said that the proposed model predictive control approaches have proved their ability in achieving high dynamic performance for different topologies of induction machine drives. In addition, the proposed sensorless techniques have confirmed their effectiveness for a wide range of speed variations. All of this are approved and validated through extensive simulation and experimental tests.
Gli azionamenti con machine ad induzione (macchine asincrone nelle loro varie configurazioni), stanno riacquistando molta attenzione in diverse applicazioni industriali. A causa di questo crescente interesse applicativo, è diventato di essenziale importanza lo sviluppo di efficaci tecniche di controllo per ottenere dagli azionamenti in questione elevate prestazioni dinamiche. Fino ad oggi, il controllo degli azionamenti con macchina a induzione alimentati da convertitori di potenza è basato sul “principio del valore medio” delle grandezze in commutazione, utilizzando la modulazione di larghezza di impulsi con controllori lineari in una struttura a cascata. Recenti ricerche hanno dimostrato che è possibile utilizzare il Controllo Predittivo per controllare gli azionamenti con macchina a induzione, con l'utilizzo di convertitori di potenza senza utilizzare modulatori e controllori lineari. Questo nuovo approccio avrà un forte impatto sul controllo dell'elettronica di potenza nei prossimi decenni. I vantaggi del Controllo Predittivo derivano dalla possibilità di perseguire problemi multi-obiettivo, di includere facile le non linearità all'interno del modello, di trattare in modo semplice i vincoli di sistema, nonché dalla facilità di implementazione digitale e dalla flessibilità di includere modifiche ed estensioni al controllo secondo le applicazioni richieste. Inlinea con tutto ciò, la ricerca presentata in questa tesi riguarda lo sviluppo di diverse topologie di controllo per varie configurazioni di azionamenti con macchine a induzione, basate sul principio di Controllo Predittivo a modello con insieme finito degli stati di controllo (Finite Control Set Model Predictive Control - FCS-MPC), che definisce direttamente l’assetto dell'inverter di tensione (VSI). Inoltre, per aumentare la robustezza degli azionamenti, vengono proposti e sperimentati diversi approcci senza sensori elettromeccanici (sensorless). La prima topologia studiata di azionamenti con macchina a induzione (IM) è l'azionamento con motore a gabbia. Il controllo diretto di coppia (DTC) è aggiornato in termini di controllo predittivo a modello (MP DTC) e usato per controllare la coppia e il flusso statorico attraverso l'utilizzo di una efficace funzione di costo attraverso la quale è anche possibile facilmente comprendere e confrontare le varianti di implementazione e studiare i problemi di convergenza e di stabilità. Viene analizzato l'effetto della velocità sulle diverse versioni di controllo e sulle prestazioni complessive del MP DTC proposto; ciò consente di comprendere appieno il principio del DTC, nonché perché e quando esso funzioni bene. Vengono utilizzate due diverse procedure di stima della posizione e della velocità del rotore nel MP DTC proposto; il primo utilizza uno stimatore adattivo con modello di riferimento (MRAS), mentre l'altro sfrutta la stessa fase di predizione del MP DTC proposto per ottenere le informazioni sulla velocità effettuando infine un'estrapolazione lineare dei valori di velocità a partire dagli ultimi due campioni stimati. Sono state eseguite numerose prove in simulazione e sperimentali per convalidare l'efficacia di entrambi gli approcci sensorless nell’ottenere un preciso inseguimento del comando di velocità per una vasta gamma di situazioni. Per migliorare la robustezza del MP DTC proposto rispetto alle variazioni parametriche, il controllo del flusso dello statore viene sostituito con quello della potenza reattiva assorbita dal motore ad induzione; di conseguenza la sensibilità del controllo alle variazioni dei parametri è limitata e ciò è confermato attraverso i risultati ottenuti sia dalla simulazione che dalle prove sperimentali. Inoltre, viene presentato un ulteriore efficace approccio alternativo per il MP DTC, basato sul principio del controllo predittivo a modello dei valori istantanei delle potenze attive e reattive dell'azionamento, invece di controllare la coppia e il flusso come nell’usuale MP DTC. Questa variante ha il vantaggio che tutte le variabili controllate sono divenute quantità misurate (potenze attive e reattive) e quindi i problemi di stima comunemente presenti nei classici schemi DTC sono efficacemente limitati. Per gli ultimi due approcci di controllo (controllo di coppia e di potenza reattiva e controllo di potenza attiva e reattiva) viene anche adottato la stima della velocità rotorica che sfrutta la funzione predittiva del controllo. I risultati ottenuti attraverso la simulazione e la sperimentazione confermano la fattibilità delle due procedure alternative di controllo per ottenere una risposta dinamica robusta dell’azionamento con IM. Per limitare il ripple che accompagna gli andamenti controllati della coppia e del flusso statorico del motore, è stata presentata una tecnica efficace di riduzione della sua ampiezza. La tecnica è basata sull’impiego di un valore ottimale per il fattore di ponderazione w_f utilizzato nella funzione di costo per sommare i due contributi che la definiscono. Viene introdotta una derivazione matematica dettagliata del valore ottimale di w_f attraverso l'analisi dei comportamenti dell’ondulazione di coppia e del flusso. La tecnica di riduzione del ripple proposta è stata verificata tramite la simulazione usando il software Matlab/Simulink e sperimentalmente utilizzando la scheda di rapida prototipazione del controllo dSpace 1104. Ancora, l'implementazione adotta l'approccio sensorless basato sulla fase di predizione. Le prestazioni dell’azionamento con IM utilizzando quest’ultimo approccio proposto sono confrontate con i risultati ottenuti con l'approccio MP DTC che utilizza invece un valore arbitrario di w_f. Il confronto conferma la validità della procedura di riduzione del ripple nelle variabili controllate mantenendo nel contempo gli oneri di calcolo entro i limiti consentiti per l'implementazione. Il principio FCS-MPC è anche utilizzato per controllare la corrente del motore di induzione come alternativa al controllo classico ad orientamento di campo (Field Oriented Control -FOC). L'approccio proposto di controllo di corrente di tipo predittivo (Model Predictive Current Control - MPCC) appartiene alla classe del controllo predittivo ad isteresi (per limitare il frequenza di commutazione) in quanto il MPCC viene attivato dal raggiungimento dell’errore di corrente di una determinata soglia. In questo caso, la caratteristica sensorless dell’azionamento è ottenuta includendo un efficace osservatore Luenberger (LO) per una precisa stima del vettore del flusso del rotore insieme alla coppia di carico e alla velocità. È stata adottata una efficace procedura di allocazione dei poli per la selezione dei guadagni dell'osservatore; la procedura si basa sul posizionamento dei poli dell'osservatore a sinistra di quelli del motore nel complesso (piano di s) con una ridotta parte immaginaria, in modo che la stabilità dell'osservatore sia migliorata in un'ampia gamma di velocità. La fattibilità dell'azionamento sensorless con MPCC è ancora confermata attraverso la simulazione e i risultati sperimentali. La seconda topologia degli azionamenti con macchina a induzione che è stata studiata è l'azionamento con motore ad anelli con rotore alimentato da invertitore e statore da rete (Doubly Fed Induction Motor DFIM). È stato sviluppato un efficace algoritmo predittivo a modello (MP DTC) per il controllo dinamico della coppia e del flusso di rotore dell'azionamento DFIM. Inoltre, viene presentato un approccio efficace di soluzione sensorless che valuta la velocità e la posizione del rotore in modo esplicito senza la necessità di coinvolgere la stima del flusso nel processo di predizione; di conseguenza l'effetto delle variazioni dei parametri sulle prestazioni complessive dell'osservatore di posizione e velocità è sensibilmente limitato. Questo è stato provato attraverso i risultati ottenuti con test eseguiti in un'ampia gamma di velocità, dal sub-sincronismo a velocità super-sincrona. Durante l'operazione, la resistenza dello statore e i valori di induttanza di magnetizzazione sono stati modificati rispetto ai valori reali per studiare l'effetto di variazioni parametriche sulle prestazioni dell'osservatore. Anche in questo caso, il software Matlab/Simulink e una scheda di controllo dSpace 1104 sono stati utilizzati per convalidare l'efficacia dell'approccio sensorless del MP DTC per l’azionamento. I risultati dimostrano la robustezza del controllo sensorless proposto e la sua capacità di ottenere una precisa stima della posizione e della velocità del rotore. La terza topologia di azionamenti con macchina a induzione che è stata studiata è quella del generatore ad induzione con rotore avvolto (DFIG) e invertitore sul rotore. Viene presentata una derivazione analitica dettagliata del controllo predittivo diretto di potenza (MP DPC) per DFIG, che trasferisce ed estende l’approccio di controllo del MP DTC citato prima per il motore a induzione a doppia alimentazione (DFIM). Una soluzione sensorless ancora basata sull'osservatore adattivo a modello di riferimento (MRAS) è adottato per stimare la velocità e la posizione del rotore. Sia le simulazioni usando il software Matlab/Simulink che i test sperimentali utilizzando la scheda dSpace 1104 hanno mostrato le elevate prestazioni dinamiche dell'azionamento. I risultati ottenuti confermano la fattibilità del metodo MP DPC proposto per ottenere un controllo disaccoppiato di potenze attive e reattive per DFIG. In sintesi, si può dire che l'utilizzo proposto del controllo predittivo a modello ha dimostrato la sua capacità di ottenere elevate prestazioni dinamiche per le diverse topologie degli azionamenti con macchina ad induzione considerati. Inoltre, le tecniche sensorless proposte hanno confermato la loro efficacia per una vasta gamma di velocità. Tutto questo è stato verificato e validato attraverso una vasta attività analisi simulativa e di sperimentazione in laboratorio.

A causa delle alte frequenze di campionamento e delle ridotte risorse computazionali, la certificazione di complessità ha un ruolo chiave nella determinazione del successo del Model Predictive Control (MPC) nelle applicazioni embedded. Questa tesi propone un algoritmo di certificazione per metodi active-set duali, che permette di calcolare esattamente il tempo massimo di risoluzione di un problema di Quadratic Programming (QP) parametrico, risultante ad esempio da formulazioni MPC lineari. Dato un problema MPC e una piattaforma di calcolo è quindi possibile certificare se il problema di ottimizzazione sarà sempre risolto nel limite di tempo. La mancanza di una certificazione è anche una minaccia per la validità dei metodi di accelerazione, dato che il miglioramento del tempo massimo di soluzione è molto più importante di quello medio per embedded MPC. Due nuovi metodi sono presentati per i quali il miglioramento nel caso peggiore è certificabile esattamente. Il primo è un MPC semi-esplicito che combina un risolutore online con la legge multiparametrica delle partizioni poliedrali che incidono maggiormente sul caso peggiore. Il secondo consiste in una selezione alternativa dei vincoli violati per metodi active-set duali, la quale diminuisce sia il numero massimo di iterazioni, sia la complessità della singola iterazione. Infine, la tesi propone applicazioni sperimentali di embedded MPC a motori elettrici e convertitori di potenza. Il controllo di coppia di un motore brushless tramite MPC è validato su un’unità di controllo economica, risultando più veloce della corrispondente soluzione multiparametrica. Viene poi presentato un controllo MPC per convertitori DC-DC pre-compensati per aggirare il problema dei controllori primali non modificabili. Inoltre, è affrontato il problema della stima dello stato per diversi convertitori nella stessa unità di alimentazione, sviluppando un osservatore robusto e non lineare unificato per sei diverse tipologie di convertitori.
Due to the fast sampling frequency and the scarce computational resources, the complexity certification of optimization algorithms plays a key role in determining the success of embedded Model Predictive Control (MPC). This thesis proposes a certification algorithm for dual active-set methods, able to compute exactly the worst-case number of iterations and the amount of time needed to solve a parametric Quadratic Programming (QP) problem, like those that arise in linear MPC. Therefore, given an MPC problem and a computational unit, it can be certified if the optimization problem will be always solved in the prescribed amount of time. The lack of a complexity certification is a threat for accelerating methods as well, as speeding up the worst-case time is much more important than improving the average case in embedded MPC. The thesis presents two novel accelerating methodologies, for which the worst-case improvement can be exactly certified. The first is a semi-explicit MPC, combining an online solver with the multiparametric solution of those polyhedral regions that most affect the worst-case time. The second method consists of an alternative selection for violated constraints in dual active-set solvers, which lowers the worst-case number of iterations and the complexity of the single iteration. Finally, embedded MPC for electrical drives and power converters is experimentally investigated. MPC for the torque control of a brushless motor is demonstrated to be feasible on a cheap control board, and even faster than the corresponding multiparametric solution. Embedded MPC for pre-compensated DC-DC converters is developed, in order to overcome the obstacle of a non-modifiable primal controller, very common in power converters. The issue of estimating the state for multiple DC-DC converters on the same power supply is also addressed, by presenting a unified nonlinear robust observer for six different converter topologies.

Orientador: Edson Bim
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Uma abordagem preditiva global-local para o controle de um motor de indução é apresentado nesta tese. O conceito de controle preditivo diz respeito a uma classe de controladores que tem se desenvolvido muito no âmbito do controle de sistemas de conversão de energia nos últimos anos, acompanhando o desenvolvimento da capacidade computacional dos sistemas microprocessados ao longo da última década. A técnica proposta é fundamentada numa lei de controle baseada em uin modelo identificado de um motor de indução. A identificação se dá de maneira experimental a partir da simulação de um motor de indução indiretamente orientado em função do fluxo do rotor, através de um modelo fuzzy do tipo Takagi-Sugeno TS com Funções de Base Ortonormal no consequente das regras.As ações de controle locais são combinadas são combinadas pela ativação das regras do modelo local devido, e a ação de controle global resultante é aplicada ao controle de velocidade do motor de indução. Este método permite ao controlador a inclusão nos parâmetros de controle das não-linearidades e restrições inerentes ao controle do máquinas elétricas
Abstract: A predictive global-local approach technic for induction motor control is presented in this thesis. Predictive control is a very wide class of controllers that have found rather recent applications in the control of electrical machines. Research on this topic has been increased in the last years due to the possibilities of today s microprocessors used for control. The proposed technique is founded on a identified model based predictive control. The identification technique applied in this method is based on the black box modeling of a Indirect Rotor Flux Oriented Induction Motor in stationary reference system through Takagi-Sugeno-TS fuzzy models with orthonormal base functions - OBF - on the rules consequents. Control actions are matched by local activation of the model rule because local and global control action is applied to speed control of an induction motor. This method allows the inclusion in the control parameters of non-linearities and constraints inherent the control of electrical machines
Mestrado
Energia Eletrica
Mestre em Engenharia Elétrica

Le problème de commande motrice de systèmes exécutant des activités multi-objectifs et fortement contraintes est à résoudre pour permettre l’émergence de comportements performants et robustes ; l’élaboration de stratégies complexes de coordination motrice est critique pour en assurer les performances, faisabilité et sécurité.Bien que les approches de commande prédictive multi-objectifs permettent la définition de stratégies complexes et sous contraintes coordonnant l’activité motrice du système, leur coût de calcul est un inconvénient critique à leur application.Le travail présenté dans ce manuscrit vise à considérer des techniques de commande prédictive multi-objectifs pour des applications pratiques à la robotique humanoïde.Une architecture de commande est alors proposée sous la forme d’un contrôleur multi-objectif à deux niveaux, exploitant les avantages respectifs des formulations prédictive et instantanée.La contribution de ce travail prend la forme de la validation des avantages d’une telle approche dans son développement pour des défis pratiques, en simulation et implémentation temps-réel, sur les robots iCub et TORO ainsi que sur des modèles d’humain.Le coût de calcul du niveau prédictif est contenu par l’introduction de problèmes réduits, permettant la formulation avantageuse de problèmes de commande au travers de programmes en nombres entiers mixtes et de distributions séquentielles et parallèles.Malgré les approximations sur la dynamique du système au niveau prédictif, des comportements complexes émergent, exploitant des stratégies de coordination entre objectifs et contraintes conflictuels pour augmenter les performances et robustesse face à des perturbations
Rising to the challenge of motor control for systems involved in multi-objective and highly-constrained activities is a requirement to enable the emergence of efficient and robust behaviors; the elaboration of complex motor coordination strategies is critical in ensuring performance, feasibility and safety.Although multi-objective predictive approaches enable the definition of complex and constrained strategies coordinating the motor activity of the system, their computational cost is a critical drawback from practical applications.The work presented in this dissertation aims at considering multi-objective predictive control for feasible and practical applications to humanoid robotics.A control architecture is proposed to this purpose as a multi-objective, two-layered controller exploiting the respective advantages of predictive and instantaneous formulations.The contribution of this work takes the form of the validation of the benefits from such an approach in its development for practical challenges and applications, in simulation and real-time implementation, on the iCub and TORO robots and virtual human models.Computational demand of the predictive level is contained with the introduction of reduced multi-objective predictive problems, enabling computationally-favorable formulations of the control problem using mixed-integer programming and sequential and parallel distributions.Despite the resulting approximations on the dynamics of the system at the predictive level, complex behaviors are emerging, exploiting elaborate coordination strategies between conflicting objectives and constraints to increase performance and robustness against disturbances

This thesis focuses on the possibilities of application of nonlinear model predictive control for electric drives. Specifically, for drives with a permanent magnet synchronous motor. The thesis briefly describes the properties of this type of drive and presents its mathematical model. After that, a nonlinear model of predictive control and methods of nonlinear optimization, which form the basis for the controller output calculation, are described. As it is used in the proposed algorithm, the Active set method is described in more detail. The thesis also includes simulation experiments focusing on the choice of the objective function on the ability to control the drive. The same effect is examined for the different choices of the length of the prediction horizon. The end of the thesis is dedicated to the comparison between the proposed algorithm and commonly used field oriented control. The computational demands of the proposed algorithm are also measured and compared to the used sampling time.

Thesis (M.S.)--University of Nevada, Reno, 2007.
"August, 2007." Includes foldout illustrations. Includes bibliographical references (leaves 79-84). Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2008]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.

The monograph discusses the application of telematics in dispatch control systems in urban passenger transport. The role of telematics as a technological basis in automating the solution of control tasks, accounting and analysis of the volume and quality of transport work in modern dispatch control systems on urban passenger transport is shown. Analytical models have been developed to estimate the capacity of a high-speed bus transportation system on a dedicated line. Mathematical models and algorithms for predicting passenger vehicle interior filling at critical stages of urban passenger transport routes are presented. The issues of application of the concept of the phase space of states introduced by the authors to assess the quality of the passenger transportation process on the route of urban passenger transport are described. The developed classification of service levels and their application in order to inform passengers at stopping points about the degree of filling of the passenger compartment of the arriving vehicle is described. The material is based on the results of theoretical research and practical work on the creation and implementation of automated control systems for urban passenger transport in Russian cities. The material of M.H. Duque Sarango's dissertation submitted for the degree of Candidate of Technical Sciences in the specialty 05.22.10 "Operation of motor transport" was used. It will be useful to specialists in the field of telematics on urban passenger transport.

In this chapter, three control methods recently developed for or applied to electric motors in general and to permanent magnet synchronous (PMS) motors, in particular, are presented. The methods include model predictive control (MPC), deadbeat control (DBC), and combined vector and direct torque control (CC). The fundamental principles of the methods are explained, the machine models appropriate to the methods are derived, and the control systems are explained. The PMS motor performances under the control systems are also investigated. It is elaborated that MPC is capable of controlling the motor under an optimal performance according to a defined objective function. DBC, on the other hand, provides a very fast response in a single operating cycle. Finally, combined control produces motor dynamics faster than one under VC, with a smoother performance than the one under DTC.

This is the first comprehensive, coherent, and up-to-date book devoted solely to the control of permanent magnet synchronous (PMS) motors, as the fastest growing AC motor. It covers a deep and detailed presentation of major PMS motor modeling and control methods. The readers can find rich materials on the fundamentals of PMS motor control in addition to new motor control methods, which have mainly been developed in the last two decades, including recent advancements in the field in a systematic manner. These include extensive modeling of PMS motors and a full range of vector control and direct torque control schemes, in addition to predictive control, deadbeat control, and combined control methods. All major sensorless control and parameter estimation methods are also studied. The book covers about 10 machine models in various reference frames and 70 control and estimation schemes with sufficient analytical and implementation details including about 200 original figures. A great emphasis is placed on energy-saving control schemes. PMS motor performances under different control systems are presented by providing simulation and experimental results. The past, present, and future of the PMS motor market are also discussed. Each chapter concludes with end-chapter problems and focussed bibliographies. It is an essential source for anyone working on PMS motors in academic and industry sectors. The book can be used as a textbook with the first four chapters for a primary graduate course and the final three chapters for an advanced course. It is also a crucial reading for researchers, design engineers, and experts in the field.

An overview of permanent magnet synchronous (PMS) motors and the related control system are presented in this chapter as introductory materials for the rest of the book. The interconnections of the control system to the power electronic inverter and the motor are emphasized. In addition, the major parts of the system are overviewed. Pulse width-modulated voltage source inverter, as the most commonly used power converter in PMS motor drives, is briefly discussed. PMS motors configurations and operating principles are also presented after considering characteristics of permanent magnet materials. Major PMS motor control methods including vector control, direct torque control, predictive control, deadbeat control, and combined vector and direct torque control are briefly reviewed. Finally, several rotor position and speed estimation schemes, and offline and online parameter estimation methods are overviewed.

Over the last thirty years the comparator hypothesis has emerged as a prominent account of inner speech pathology. This chapter discusses a number of cognitive accounts broadly derived from this approach, highlighting the existence of two importantly distinct notions of inner speech in the literature; one as a prediction in the absence of sensory input, the other as an act with sensory consequences that are themselves predicted. Under earlier frameworks in which inner speech is described in the context of classic models of motor control, I argue that these two notions may be compatible, providing two routes to inner speech pathology. Under more recent accounts grounded in the architecture of Bayesian predictive processing, I argue that “active inference” approaches to action generation pose serious challenges to the plausibility of the latter notion of inner speech, while providing the former notion with rich explanatory possibilities for inner speech pathology.

The nature of inner language has long been under the scrutiny of humanities, through the practice of introspection. The use of experimental methods in cognitive neurosciences provides complementary insights. This chapter focuses on wilful expanded inner language, bearing in mind that other forms coexist. It first considers the abstract vs. concrete (or embodied) dimensions of inner language. In a second section, it argues that inner language should be considered as an action-perception phenomenon. In a third section, it proposes a revision of the “predictive control” account, fitting with our sensory-motor view. Inner language is considered as deriving from multisensory goals, generating multimodal acts (inner phonation, articulation, sign) with multisensory percepts (in the mind’s ear, tact, and eye). In the final section, it presents a landscape of the cerebral substrates of wilful inner verbalization, including multisensory and motor cortices as well as cognitive control networks.

Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).