Adaptive Feedback Linearization Control of SynRM Drives With On-Line Inductance Estimation

This article proposes an adaptive input-output Feedback Linearization Control ( FLC ) techniques for Synchronous Reluctance Motor ( SynRM ) drives, taking into consideration the iron losses. As a main original content, this work proposes a control law based on a new dynamic model of the SynRM including iron losses as well as the on-line estimation of the static inductances. The on-line estimation of the SynRM static inductances permits to inherently take into consideration the magnetic saturation phenomena occuring on both axes. As a major result, it permits a null stator current steady state tracking error even with a proportional derivative controller. The estimation law is obtained thanks to a Lyapunov-based analysis and thus the stability of the entire control system, including the estimation algorithm, is intrinsically guaranteed. The proposed adaptive FLC technique, has been tested experimentally on a suitably developed test set-up, and compared experimentally with its non-adaptive versions in both tuned and detuned working conditions. Moreover, a sensitivity analysis of the performance of the adaptive FLC to the variations of the stator resistance at low speed has been made. Finally, an analysis of the effects of the iron losses on the control performance and stability at high speed in the field weakening region at medium/high loads has been made

Feedback Linearization Based Nonlinear Control of SynRM Drives Accounting for Self- and Cross-Saturation

This article proposes a nonlinear controller based on feedback linearization (FL) for synchronous reluctance motor (SynRM) drives which takes into consideration the magnetic saturation. The proposed nonlinear FL control based control technique has been developed starting from the theoretical definition of an original dynamic model of the SynRM taking into consideration both the self- and the cross-saturation effects. Such a control technique permits the dynamics of both the speed and axis flux loops to be maintained constant independently from the load and the saturation of the iron core in both constant flux and variable direct axis flux operating conditions. Finally, sensitivity of the performance of the proposed FL control versus the variation of the main motor parameters has been verified. The proposed technique has been tested experimentally on a suitably developed test setup. The proposed FL control has been further compared with the classic field-oriented control (FOC) in both constant flux and variable flux working conditions

Nonlinear Time-Frequency Control of Permanent Magnet Electrical Machines

Permanent magnet (PM) electrical machines have been widely adopted in industrial applications due to their advantages such as easy to control, compact in size, low in power loss, and fast in response, to name only a few. Contemporary control methods specifically designed for the control of PM electrical machines only focus on controlling their time-domain behaviors while completely ignored their frequency-domain characteristics. Hence, when a PM electrical machine is highly nonlinear, none of them performs well. To make up for the drawback and hence improve the performance of PM electrical machines under high nonlinearity, the novel nonlinear time-frequency control concept is adopted to develop viable nonlinear control schemes for PM electrical machines. In this research, three nonlinear time-frequency control schemes are developed for the speed and position control of PM brushed DC motors, speed and position control of PM synchronous motors, and chaos suppression of PM synchronous motors, respectively. The most significant feature of the demonstrated control schemes are their ability in generating a proper control effort that controls the system response in both the time and frequency domains. Simulation and experiment results have verified the effectiveness and superiority of the presented control schemes. The nonlinear time-frequency control scheme is therefore believed to be suitable for PM electrical machine control and is expected to have a positive impact on the broader application of PM electrical machines

Robust Control of Synchronous Reluctance Motor Based on Automatic Disturbance Rejection

This article proposes the theoretical development and experimental application of the active disturbance rejection control (ADRC) to synchronous reluctance motor (SynRM) drives. The ADRC is a robust adaptive extension of the input-output feedback linearization control (FLC). It performs the exact linearization of the SynRM model by a suitable nonlinear transformation of the state based on the online estimation of the corrective term by the so-called extended state observers (ESO). Consequently, any unmodeled dynamics or uncertainty of the parameters are properly addressed. The control strategy has been verified successfully both in numerical simulations and experimentally on a suitably developed test set-up that provides the ADRC robustness versus parameters variations which cannot be obtained with other model-based nonlinear control techniques (e.g., FLC). Simulation results show the capability of the ADRC to maintain its dynamic performance, even in the presence of quick variations of the SynRM dynamic inductances. Experimental results confirm the robustness of the ADRC versus any model parameter uncertainty. The proposed ADRC has been experimentally compared with a previously developed FLC, in both a tuned and detuned working configuration, with the classic rotor oriented control, and with a finite state model predictive control (MPC), where speed control is integrated into the MPC. Experimental results show far better robustness versus any parameter variation

Development and Implementation of Some Controllers for Performance Enhancement and Effective Utilization of Induction Motor Drive

The technological development in the field of power electronics and DSP technology is rapidly changing the aspect of drive technology. Implementations of advanced control strategies like field oriented control, linearization control, etc. to AC drives with variable voltage, and variable frequency source is possible because of the advent of high modulating frequency PWM inverters. The modeling complexity in the drive system and the subsequent requirement for modern control algorithms are being easily taken care by high computational power, low-cost DSP controllers. The present work is directed to study, design, development, and implementation of various controllers and their comparative evaluations to identify the proper controller for high-performance induction motor (IM) drives. The dynamic modeling for decoupling control of IM is developed by making the flux and torque decoupled. The simulation is carried out in the stationary reference frame with linearized control based on state-space linearization technique. Further, comprehensive and systematic design procedures are derived to tune the PI controllers for both electrical and mechanical subsystems. However, the PI-controller performance is not satisfactory under various disturbances and system uncertainties. Also, precise mathematical model, gain values, and continuous tuning are required for the controller design to obtain high performance. Thus, to overcome these drawbacks, an adapted control strategy based on Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller is developed and implemented in real-time to validate different control strategies. The superiority of the proposed controller is analyzed and is contrasted with the conventional PI controller-based linearized IM drive. The simplified neuro-fuzzy control (NFC) integrates the concept of fuzzy logic and neural network structure like conventional NFC, but it has the advantages of simplicity and improved computational efficiency over conventional NFC as the single input introduced here is an error instead of two inputs error and change in error as in conventional NFC. This structure makes the proposed NFC robust and simple as compared to conventional NFC and thus, can be easily applied to real-time industrial applications. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using feedback linearization of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using proposed simplified NFC as compared to the conventional NFC, rather it shows superior performance over PI-controller-based drive. A hybrid fuel cell (FC) supply system to deliver the power demanded by the feedback linearization (FBL) based IM drive is designed and implemented. The modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) incorporated with the intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burden over conventional NFSMC and thus, suitable for real-time industrial applications. The parameters of the modified NFC is tuned by an adaptive mechanism based on sliding-mode control (SMC). A FC stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the FC during different operating conditions of the drive system. The effectiveness of the proposed method using FC-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated in real-time. It is evident from the results that the system provides optimal dynamic performance in terms of ripples, overshoot, and settling time responses and is robust in terms of parameters variation and external load

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Torque ripple minimization in non-sinusoidal synchronous reluctance motors based on artificial neural networks

This paper proposes a new method based on Artificial Neural Networks for reducing the torque ripple in a non-sinusoidal Synchronous Reluctance Motor. The Lagrange optimization method is used to solve the problem of calculating optimal currents in the d-q frame. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents giving a constant electromagnetic torque and minimizing the ohmic losses. Thanks to the online learning capacity of neural networks, the optimal currents can be obtained online in real time. With this neural control, each machine’s parameters estimation errors and current controller errors can be compensated. Simulation and experimental results are presented which confirm the validity of the proposed method.Bourse de l'Ambassade de France au Vietna

Control Theory in Engineering

The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation

Dual closed loop controller of bus stepper motor based on back-EMF

Author name used in this publication: K. W. E. ChengAuthor name used in this publication: S. ToRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics