260 research outputs found
Performance enhanced robust iterative learning control with experimental application to PMSM position tracking
No full textThis brief develops an innovative robust iterative learning control law using the repetitive process setting. The new design is experimentally validated through a comprehensive set of experiments highlighting the capabilities for the position tracking control of a permanent magnet synchronous motor subject to load disturbances in the presence of uncertainties in selected parameters
Robust Current Decoupling in a Permanent Magnet Motor Combining a Geometric Method and SMC
No full textIn this work, a new control strategy for a permanent magnet linear motor is conceived. Geometric methods and sliding mode control are combined to reduce the effects of the nonlinearities due to the interaction between the coil currents and to achieve robust positioning. In fact, due to the presence of the induced voltage, the effects of nonlinearities cannot be cancelled without the help of other auxiliary and intrinsically robust techniques. Indeed, the sliding mode controller which is devised makes the whole structure robust with respect to any kind of inaccessible external and internal disturbance, such as induced voltages, loads, and parametric uncertainties. In particular, the paper indicates necessary conditions for the existence of a so-called decoupling sliding mode control scheme. The proposed method has the advantage of providing a controller which has a very simple structure, can be applied to a large variety of actuators and guarantees very good power performance with respect to the non-compensated decoupling controller. Simulation results are reported to validate the proposed methodology
Iterative learning control for a class of multivariable distributed systems with experimental validation
No full textThis article develops an iterative learning control (ILC) design for a class of multiple-input–multiple-output systems where a distributed heating system is used as a particular example to experimentally validate the design. The class of systems considered is described by a parabolic partial differential equation, which, for control design, is approximated by a finite dimensional state-space model obtained by applying the method of integro-differential relations combined with a projection approach. In some cases, including the distributed heating system, this approximation may result in a nonminimum phase system and, hence, pose an additional design challenge. In this work, the ILC law is computed in the frequency domain by solving a convex optimization problem, and its performance is evaluated in both simulation and experiments
Iterative learning control of a distributed heating system described by a non-minimum phase model
No full textThis paper considers iterative learning control design for non-minimum phase dynamics using a model derived from an experimental spatially distributed system, i.e., a heating system. A novel design based on an H∞ setting and convex optimization with validation in both simulation and experiment
Constrained iterative learning control for linear time-varying systems with experimental validation on a high-speed rack feeder
No full textIterative learning control (ILC) applies to systems required to track the desired trajectory of finite duration repeatedly. This article considers constrained ILC design for linear time-varying systems, a problem with limited, in relative terms, results in the literature but not uncommon in practical applications. Different design algorithms are developed, and their convergence properties are established. An extension of these designs to point-to-point tracking tasks is given. A high-speed rack feeder typically used in automated warehouses is considered to verify the designs. It represents a flexible beam structure subject to kinematic constraints, such as a maximum velocity and a maximum acceleration with a vertically moving mass causing the time-varying characteristics. Experimental results demonstrate the effectiveness of the designs
Experimental validation of constrained ILC approaches for a high speed rack feeder
No full textIterative learning control (ILC) is applicable to systems that are required to repeatedly track a desired trajectory of finite duration. Norm-optimal ILC can be characterised as a combined feedforward and feedback learning approach, where the tracking error from the previous trial and the tracking error of the current trial are employed to reduce the tracking error from trial to trial. In this paper, a high speed rack feeder typically used in automated warehouses is considered, which represents a flexible beam structure with a vertically moving mass. Due to kinematic constraints such as a maximum velocity and a maximum acceleration, standard ILC is not applicable if the desired trajectory violates these constraints. One possible solution would be an offline trajectory planning subject to the given kinematic constraints. This paper, however, addresses modifications of the ILC algorithm itself to cope with infeasible trajectories. Two alternative algorithms are given for this purpose and compared with each other in experiments on a test rig that replicates the dynamics of a high speed rack feede
New Approaches in Automation and Robotics
No full textThe book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book
New Approaches in Automation and Robotics
No full textThe book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book
Mengenbasierte Black-Box-Identifikation linearer Systeme
No full textDie wachsende Bedeutung modellbasierter Verfahren bei technischen Geräten und Anlagen führt dazu, dass immer mehr Modelle für unterschiedliche Systeme benötigt werden. Um diesem Umstand gerecht zu werden, kommen in den letzten Jahren verstärkt Verfahren zur datenbasierten Modellbildung zum Einsatz, da diese meist zu genauen Modellen mit einer geringen Modellkomplexität führen. Gleichzeitig steigen jedoch auch die Anforderungen an die Sicherheit der Systeme. Eine genaue Kenntnis der Modellunsicherheit ist erforderlich, um auftretende Fehler sicher zu detektieren. Einen vielversprechenden Ansatz hierzu stellen mengenbasierte Verfahren dar, da an Stelle einzelner Werte mit der Menge aller möglichen Werte gerechnet wird.
In dieser Arbeit wird ein Verfahren zur mengenbasierten Black-Box-Identifikation linearer Systeme vorgestellt. Den Ausgangspunkt des Verfahrens stellen Messdaten mit unbekannten, aber beschränkten Fehlern dar, welche durch Intervalle repräsentiert werden. Das Verfahren wird zunächst für Systeme mit einer Ausgangsgröße vorgestellt und anschließend für Systeme mit mehreren Ausgangsgrößen erweitert. Als Ergebnis wird von dem Verfahren ein zeitdiskretes Gleichungsfehlermodells in ARX-Struktur (Auto Regressive with eXogenous input) mit minimaler Modellordnung bestimmt.
Den Schwerpunkt der Arbeit bildet dabei die Ordnungsbestimmung des Systems. Die so bestimmte minimale Ordnung wird mit verschiedenen mengenbasierten Parameteridentifikationsverfahren zur mengenbasierten Black-Box-Identifikation verwendet. Als mögliche Anwendung der so identifizierten Modelle wird in dieser Arbeit die Fehlerdetetektion betrachtet. Dazu wird eine Möglichkeit vorgestellt, direkt die bei der Identifikation gewonnenen ARX-Modelle zu nutzen. Die Vorgehensweise wird anhand verschiedener simulativer Beispielsysteme sowie an unterschiedlichen Laborsystemen demonstriert.The increasing importance of model-based methods for technical equipment and installations leads to a growing demand for models of various systems. To meet these demands, data-based modeling approaches have been applied often in the past few years, as they usually lead to precise models with a low model complexity. The demands on the safety of the systems increased at the same time. A detailed knowledge on the model uncertainty is necessary to reliably detect faults. Therefore, promising approaches are set-membership based methods, as they compute the whole set of possible values instead of individual values.
In this work, a set-membership method for black-box identification of linear systems is presented. Starting point of the method is measurement data with unknown but bounded errors, which are represented by intervals. The method is initially presented for systems with a single output and afterwards extended to multiple output systems. The result is an equation-error model in ARX (Auto Regressive with eXogenous inputs) structure with the minimal model order.
The main focus of this work is the order determination of the system. The thereby determined minimal order is used with various set-membership parameter identification methods to obtain a complete black-box system identification method. In this work, fault detection is considered as possible application of the identified models. For that purpose, a fault detection method directly using the identified ARX-model is presented. The procedure is demonstrated by means of several simulative examples as well as various laboratory systems
Nichtlineare modellprädiktive Drehmomentregelung elektrischer Antriebssysteme für hochdynamische Anwendungen
No full textNonlinear model predictive control ((N)MPC) techniques are used more and more frequently for high-performance control of dynamic nonlinear multiple-input and multiple-output systems. A main reason for this is the systematic consideration of (nonlinear) constraints, as they frequently occur in mechatronic systems. Typically, the sampling times of such systems are in the (sub-)millisecond range and the control strategy must be implemented in an embedded system with limited resources. Especially in time-critical applications with small computing power, the real-time implementation of MPC algorithms still poses a challenge.
Torque control of electrical machines is such a challenging problem with nonlinear dynamics, sampling times in the microsecond range and nonlinear constraints on the phase voltage as well as on the phase and the DC link current. The number of electric servo drives is constantly increasing due to the flexible application in both automotive and industrial environments. The type of machine plays an important role for the application. Traditionally, permanent magnet synchronous machines (PMSMs) are used for precision applications due to their high dynamics, high energy density and good efficiency. Because of improving control strategies, however, induction machines, which are characterized by their robust and inexpensive design, are also increasingly used in more demanding applications. In general, the requirements on the drives with regard to speed and accuracy of torque generation are very high, especially in critical applications such as electric power steering systems or machine tools. Due to increasing environmental awareness and economic considerations, there is also a growing interest in operating the machines as efficiently as possible and to fully exploit their physical limits.
Motivated by the above-mentioned challenges, an MPC approach for fast and energy-efficient torque control of electrical machines is developed in this thesis. A particular focus is on the development of suitable methods to take into account the constraints mentioned at the beginning. On the basis of an augmented Lagrangian approach, the nonlinear constrained optimal control problem is transformed into an unconstrained problem. This allows one to apply a tailored gradient method for solving the unconstrained problem in a time efficient manner. In addition, a fixed point iteration strategy, which further reduces the algorithmic complexity for a certain class of applications. For example, the DC link current constraints, which play an important role in automotive need not always be taken into account.
By seamlessly integrating the solution methods into the augmented Lagrangian framework, an MPC algorithm tailored exactly to the problem at hand is developed. Runtime analyses prove that the torque control for PMSMs with a sampling time of 500µs is real-time feasible even on an electronic control unit (ECU) level. Due to the more complex model structure, the computing time of the induction machines' control is somewhat higher in direct comparison. Nevertheless, real-time feasibility is still achieved on a dSPACE rapid prototyping platform. Another limiting factor for embedded implementation is the memory footprint of the algorithm. For this reason, the developed algorithms are kept streamlined and have an almost negligible memory requirement of less than 10kB.
The basis of the control design is a mathematical model of the machines in the flux-oriented dq-coordinate system. Current-dependent model parameters take saturation effects into account and thus ensure the validity of the model even in case of a significant overload of the machines. At the same time, the complexity of the models is low enough in order to be parameterized using the standard inverter without additional sensors.
In order to increase energy efficiency with regard to copper losses, a new setpoint computation is developed for both the PMSM and the IM. The calculation of the setpoints is based on a hierarchical, static optimization problem and ensures that the setpoints meet all constraints in the stationary case.
Experimental results illustrate the performance of the controls in numerous scenarios. In order to better classify the results, a state--of--the--art field-oriented controller (FOC) is implemented for both machines. The setpoints for these controls are calculated with the methods developed in this thesis, which ensures that the FOC complies with the constraints in the best possible way in order to provide a fair comparison with the MPC methods.Die Dissertation wurde 2020 im Shaker-Verlag unter der ISBN 978-3-8440-7305-8 veröffentlicht
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