A Nonlinear Sliding Mode Controller for IPMSM Drives with an Adaptive Gain Tuning Rule

This paper presents a nonlinear sliding mode control (SMC) scheme with a variable damping ratio for interior permanent magnet synchronous motors (IPMSMs). First, a nonlinear sliding surface whose parameters change continuously with time is designed. Actually, the proposed SMC has the ability to reduce the settling time without an overshoot by giving a low damping ratio at the initial time and a high damping ratio as the output reaches the desired setpoint. At the same time, it enables a fast convergence in finite time and eliminates the singularity problem with the upper bound of an uncertain term, which cannot be measured in practice, by using a simple adaptation law. To improve the efficiency of a system in the constant torque region, the control system incorporates the maximum torque per ampere (MTPA) algorithm. The stability of the nonlinear sliding surface is guaranteed by Lyapunov stability theory. Moreover, a simple sliding mode observer is used to estimate the load torque and system uncertainties. The effectiveness of the proposed nonlinear SMC scheme is verified using comparative experimental results of the linear SMC scheme when the speed reference and load torque change under system uncertainties. From these experimental results, the proposed nonlinear SMC method reveals a faster transient response, smaller steady-state speed error, and less sensitivity to system uncertainties than the linear SMC metho

A Nonlinear Sliding Mode Controller for IPMSM Drives with an Adaptive Gain Tuning Rule

This paper presents a nonlinear sliding mode control (SMC) scheme with a variable damping ratio for interior permanent magnet synchronous motors (IPMSMs). First, a nonlinear sliding surface whose parameters change continuously with time is designed. Actually, the proposed SMC has the ability to reduce the settling time without an overshoot by giving a low damping ratio at the initial time and a high damping ratio as the output reaches the desired setpoint. At the same time, it enables a fast convergence in finite time and eliminates the singularity problem with the upper bound of an uncertain term, which cannot be measured in practice, by using a simple adaptation law. To improve the efficiency of a system in the constant torque region, the control system incorporates the maximum torque per ampere (MTPA) algorithm. The stability of the nonlinear sliding surface is guaranteed by Lyapunov stability theory. Moreover, a simple sliding mode observer is used to estimate the load torque and system uncertainties. The effectiveness of the proposed nonlinear SMC scheme is verified using comparative experimental results of the linear SMC scheme when the speed reference and load torque change under system uncertainties. From these experimental results, the proposed nonlinear SMC method reveals a faster transient response, smaller steady-state speed error, and less sensitivity to system uncertainties than the linear SMC metho

Identification and Adaptive Control for High-performance AC Drive Systems.

High-performance AC machinery and drive systems can be found in a variety of applications ranging from motion control to vehicle propulsion. However, machine parameters can vary significantly with electrical frequency, flux levels, and temperature, degrading the performance of the drive system. While adaptive control techniques can be used to estimate machine parameters online, it is sometimes desirable to estimate certain parameters offline. Additionally, parameter identification and control are typically conflicting objectives with identification requiring plant inputs which are rich in harmonics, and control objectives often consisting of regulation to a constant set-point. In this dissertation, we present research which seeks to address these issues for high-performance AC machinery and drive systems. The first part of this dissertation concerns the offline identification of induction machine parameters. Specifically, we have developed a new technique for induction machine parameter identification which can easily be implemented using a voltage-source inverter. The proposed technique is based on fitting steady-state experimental data to the circular stator current locus in the stator flux linkage reference-frame for varying steady-state slip frequencies, and provides accurate estimates of the magnetic parameters, as well as the rotor resistance and core loss conductance. Experimental results for a 43 kW induction machine are provided which demonstrate the utility of the proposed technique by characterizing the machine over a wide range of flux levels, including magnetic saturation. The remainder of this dissertation concerns the development of generalizable design methodologies for Simultaneous Identification and Control (SIC) of overactuated systems via case studies with Permanent Magnet Synchronous Machines (PMSMs). Specifically, we present different approaches to the design of adaptive controllers for PMSMs which exploit overactuation to achieve identification and control objectives simultaneously. The first approach utilizes a disturbance decoupling control law to prevent the excitation input from perturbing the regulated output. The second approach uses a Lyapunov-based adaptive controller to constrain the states to the output error-zeroing manifold on which they are varied to provide excitation for parameter identification. Finally, a receding-horizon control allocation approach is presented which includes a metric for generating persistently exciting reference trajectories.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120862/1/davereed_1.pd

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

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

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

High performance disturbance observer based control system design for permanent magnet synchronous AC machine applications

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAn electrical machine is one of the main workforces in different industries and serves them in various applications. Machine drive control design involves many technical issues for efficient and robust exploitation. Over several decades, Permanent Magnet Synchronous Motor (PMSM) is getting preferred for industrial applications over its counterpart Squirrel Cage Induction Motor (SCIM) drive, because of their higher efficiency, power density, and higher torque to inertia ratio. In the prospective that PMSM drives are considered the drives of the future, there are still technical challenges and issues related to PMSM control. Many studies have been devoted to PMSM control in the past, but there are still some open research areas that bring worldwide researchers’ interests back to PMSM drive control. One of the approaches that may facilitate better performance, higher efficiency, and robust and reliable work of the control system is the disturbance observer-based control (DOBC) with linear and nonlinear output feedback control for PM synchronous machine applications. DOBC is adopted due to its ability to reject external and internal disturbances with improving tracking performance in the variable speed wind energy conversion system (WECS) to maximize power extraction. The high order disturbance observer (HODO) is utilized to estimate the aerodynamic torque-based wind speed without the use of a traditional anemometer, which reduces the overall cost and improves the reliability of the whole system. Also, this method has been designed to improve the angular shaft speed tracking of the PMSM system under load torque disturbance and speed variations. The model-based linear and nonlinear feedback control are used in the proposed control systems. The sliding mode control (SMC) with switching output feedback control law and integral SMC with linear feedback and state-dependent Riccati equation (SDRE) based approaches have been designed for the systems. The SDRE control accounts for the nonlinear multivariable structure of the WECS and is approximated with Taylor series expansion terms. The chattering inherited from SMC is eliminated by the continuous approximation technique. The sliding mode is guaranteed by eliminating the reaching mode in the proposed integral SMC. The model-free cascaded linear feedback control system based on the proportional-integral (PI) controllers use a back-calculation algorithm anti-windup scheme. The proposed speed controllers are synthesized with HODO to compensate for the external disturbance, model uncertainty, noise, and modelling errors. Moreover, servomechanism-based SDRE control, a near-optimal control system is designed to suppress the model uncertainty and noise without the use of disturbance observers. The proposed control systems for PMSM speed regulation have demonstrated a significant improvement in the angular shaft speed-tracking performance at the transients. Their performances have been tested under speed, load torque variations, and model uncertainty. For example, HODO-based SMC with switching output feedback control law (SOFCL) has demonstrated improvement by more than 78% than the PI-PI control system of the PMSM. The performance of the HODOs-based Integral SMC with SDRE nonlinear feedback is improved by 80.5% under external disturbance, model uncertainty, and noise than Integral SMC with linear feedback in the WECS. The HODO-based SDRE control with servomechanism has shown an 80.2% improvement of mean absolute percentage error under disturbances than Integral SMC with linear feedback in the WECS. The PMSM speed tracking performance of the proposed HODO-based discrete-time PI-PI control system with back-calculation algorithm anti-windup scheme is improved by 87.29% and 90.2% in the speed commands and load torque disturbance variations scenarios respectively. The simulations for testing the proposed control system of the PMSM system and WECS have been implemented in Matlab/Simulink environment. The PMSM speed control experimental results have been obtained with Lucas-Nuelle DSP-based rapid control prototyping kit.Center for International Program “Bolashak” of the Ministry of Education and Science Republic of Kazakhsta

Nonlinear optimal control of interior permanent magnet synchronous motors for electric vehicles

At present time, research in the field of Electric Vehicles (EV) is significantly intensifying around the world due to the ambitious goals of many countries, including the UK, to prohibit the sale of new gasoline and diesel vehicles, as well as hybrid vehicles, in the near future around 2030-35. The primary goal of this Ph.D. research is to improve the propulsion system of electric vehicles' powertrains through improvements in the control of Interior Permanent Magnet Synchronous Motors (IPMSM), which are commonly used in EV applications. The proposed approaches are supported by simulations in Matlab, Matlab-Simulink and laboratory-based experiments. The research initially proposes an analytical solution in implicit view for a combined Maximum Torque per Ampere (MTPA) and Maximum Efficiency (ME) control, allowing to determine the optimal d-axis current, based on the concept of minimisation of the fictitious electric power loss. With the exception of two parameters, the equation is identical to that of the ME control. Therefore, upgrading the ME control to the combined MTPA/ME control is relatively easy and doesn't require any change in hardware beyond a few minors of controller code in the software. The presented research demonstrates an easy-to-apply combined MTPA/ME control leading to the ‘Transients Optimal and Energy-Efficient IPMSM Drive’ providing smooth transitions to the MTPA control during transients and to the ME control during steady states. A concept of ‘Nonlinear Optimal Control of IPMSM Drives’ is also introduced in this Ph.D. research. The velocity control loop develops nonlinearities when energy consumption optimisation methods like MTPA, ME, or combined MTPA/ME are added. In addition, the control system's parameters can be inaccurate and fluctuate depending on the operating point or possible uncertainties in real-time operation. In the proposed method, the control structure is the same as in the Field Oriented II Control (FOC), with the close velocity and two current loops, but the Proportional-Integral (PI) controllers are replaced by Nonlinear Optimal (NO) Controllers. The linear part of the controller is designed as a Linear Quadratic Regulator (LQR) with integral action for each loop separately. This is, in fact, a PI controller with optimal gain parameters for a specific operating point. The nonlinear part takes the required fluctuations of the control system’s optimal gain parameters in real-time operation as new control actions to improve a robust control structure. The design procedure for the nonlinear part is similar to that of the LQR, but the criterion of A. Krasovsky's generalised work is used, and the analytical derivations lead to an explicit control solution for the nonlinear optimal part. The nonlinear part emulates the adjustments for updating the linear part’s optimal LQR gains based on operating conditions, instead of employing extensive look-up tables or complicated estimation algorithms. The proposed control is robust in the allowed range of the system’s parameters. In conclusion, upgrading existing industrial IPMSM drives into a robust and optimal energy-efficient version that can be used for electric vehicle applications is the main advantage of the novel control concept described in this Ph.D. research. For this upgrade, only a small portion of the software that is related to the PI controllers needs to be changed; no new hardware is needed. Therefore, it is cost-effective and simple to transform existing industrial IPMSM drives into a better version with the proposed method. This feature also leads to the design of more adequate IPMSM drives to meet the demands of Electric Vehicle (EV) operating cycles