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

A novel PID-based control approach for switched-reluctance motors

International audienceWe propose a control strategy for switched-reluctance motors with unknown load, which consists in two separate control loops, for the rotor (mechanical) dynamics and the stator (electrical) dynamics. The novelty of the approach resides in using an alternative rotor model which corresponds to that of an harmonic oscillator hence, it is linear in the rotation coordinates. The control law is of proportional-integral-derivative type and it is implemented through a virtual control input, generated via the mechanical torque of electrical origin. A second control loop is closed around the stator dynamics via a current tracking controller. As far as we know, we establish for the first time global exponential stability considering that the load torque is unknown

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

An Adaptive Periodic-Disturbance Observer for Periodic-Disturbance Suppression

Repetitive operations are widely conducted by automatic machines in industry. Periodic disturbances induced by the repetitive operations must be compensated to achieve precise functioning. In this paper, a periodic-disturbance observer (PDOB) based on the disturbance observer (DOB) structure is proposed. The PDOB compensates a periodic disturbance including the fundamental wave and harmonics by using a time delay element. Furthermore, an adaptive PDOB is proposed for the compensation of frequency-varying periodic disturbances. An adaptive notch filter (ANF) is used in the adaptive PDOB to estimate the fundamental frequency of the periodic disturbance. Simulations compare the proposed methods with a repetitive controller (RC) and the DOB. Practical performances are validated in experiments using a multi-axis manipulator. The proposal provides a new framework based on the DOB structure to design controllers using a time delay element.Comment: 11 pages, 22 figures, journa

Disturbance rejection for nonlinear uncertain systems with output measurement errors: Application to a helicopter model

As a virtual sensor, disturbance observer provides an alternative approach to reconstruct lumped disturbances (including external disturbances and system uncertainties) based upon system states/outputs measured by physical sensors. Not surprisingly, measurement errors bring adverse effects on the control performance and even the stability of the closed-loop system. Toward this end, this paper investigates the problem of disturbance observer based control for a class of disturbed uncertain nonlinear systems in the presence of unknown output measurement errors. Instead of inheriting from the estimation-error-driven structure of Luenberger type observer, the proposed disturbance observer only explicitly uses the control input. It has been proved that the proposed method endows the closed-loop system with strong robustness against output measurement errors and system uncertainties. With rigorous analysis under the semiglobal stability criterion, the guideline of gain choice based upon the proposed structure is provided. To better demonstrate feature and validity of the proposed method, numerical simulation and comparative experiments of a helicopter model are implemented

Adaptive control system of slotless DC linear motor

Slotless DC linear motors (SDCLM) offer several benefits over traditional linear motors, including higher efficiency, smoother operation, and higher power density. These advantages make them a popular choice for a wide range of applications in various industries. One of the main benefits of a slotless DC linear motor is the absence of slot harmonics, which can cause vibration and noise in traditional slotted motors. This makes slotless motors ideal for applications that require precise and smooth motion, such as in medical equipment, robotics, and semiconductor manufacturing. However, one of the challenges of a Slotless DC linear motor is the presence of force ripple, which can limit the motor's performance, precision, and accuracy. Force ripple is caused by the mutual attraction of the translator's magnets and iron cores. It is independent of the motor current and is determined only by the relative position of the motor coils regarding the magnets. To overcome these challenges, motor redesign, magnetic field optimisation and the use of an adaptive control system. This research program focused on and investigated the above possible methods (i.e., motor redesign, magnetic field optimisation field and use of advanced control algorithms such as Sliding Mode Control SMC) to tackle the current challenges and improve the relevant industrial application performance and precision. The inquiry encompasses the analysis, design, and control of the SDCLM by proper modelling, building, and experimental validation of the modelled findings, applying both static and dynamic methodologies. Electrical, mechanical, and magnetic analyses were performed on the SDCLM design. The performance of the SDCLM was investigated using a finite element method (FEM), and the motor parameters were improved. Investigation and analysis are performed about additional difficulties such as force ripple and normal force, where the results indicated that the flux density in the airgap and the thrust force were different between the actual time and the simulation by 7.14% and 8.07%, respectively. Moreover, sliding mode control is designed to achieve desired system performance, such as reducing the power ripple of a slotless DC linear motor. where the proposed control shows experiments that it has stability despite disturbances and uncertainties. To improve the control method and reduce the steady-state error caused by the force ripple, the Bees algorithm has been used to tune the parameters of the controller. Finally, the outcomes indicate that the control method employing the disturbance observer and Bees algorithm has enhanced the performance of both position and speed, while concurrently reducing the force ripple. A comparison between simulation and experiment shows that there is a difference in the tracking performance, where the difference was around 13.6%. This error could have arisen from the omission of certain errors that cannot be accounted for within the simulation. These errors may stem from issues with the position sensor or discrepancies in the manual system design process

Development and applications of new sliding mode control approaches

Ph.DDOCTOR OF PHILOSOPH

Repetitive predictive control and its application to PMSMs

Repetitive Control is a learning control algorithm used to solve the problems of tracking the references and/or rejecting the disturbances that have repetitive nature. One of the challenging problems in repetitive control is to maintain the performance of the controller when the manipulated and/or state variables are hitting the constraints. Meanwhile, it is well known that Model Predictive Control (MPC) has its reputation in dealing with the constrained control problem through the use of optimization algorithms. This thesis incorporates the concept of repetitive control into the design of an MPC controller, resulting a new controller termed Repetitive-Predictive Control (RPC), so that the benefits of both controllers are combined, such as repetitiveness, constraints and multi-variable control. The design of the RPC controller is achieved by incorporating the dominant frequency components identified by the frequency decomposition of the reference signal into the receding horizon control of MPC. To further investigate the strength and weakness of the RPC, the design, tuning and performance of the RPC controller is thoroughly explored by its application to the control of Permanent Magnet Synchronous Motors (PMSMs) that have been broadly adopted for industrial control application due to their low volume and high efficiency. The decision to use PMSMs as the application of RPC is reflected by the increasing trend to apply the Repetitive Control (RC) and Model Predictive Controller (MPC) for the electric drives in recent years. The design of RPC for the position, speed and current regulation of a PMSM has been investigated under two different schemes based on the Field Oriented Control (FOC). The first scheme employs the cascade structure with constrained MPC and RPC replacing the PI controllers for the inner-loop current control and outer-loop speed/position control, respectively. The second scheme is to combine both speed and current controllers into one single multi-variable model predictive controller with operating constraints imposed. The experimental comparisons of the two control schemes with cascade PI controllers demonstrate the superior performance of cascade RPC/MPC in terms of the ability of constrained control, disturbance rejection and position tracking. All results in the thesis have been validated by an experimental test-bed with an industrial-sized PMSM

Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded