Gernot Herbst

A Simulative Study on Active Disturbance Rejection Control (ADRC) as a Control Tool for Practitioners

As an alternative to both classical PID-type and modern model-based approaches to solving control problems, active disturbance rejection control (ADRC) has gained significant traction in recent years. With its simple tuning method and robustness against process parameter variations, it puts itself forward as a valuable addition to the toolbox of control engineering practitioners. This article aims at providing a single-source introduction and reference to linear ADRC with this audience in mind. A simulative study is carried out using generic first- and second-order plants to enable a quick visual assessment of the abilities of ADRC. Finally, a modified form of the discrete-time case is introduced to speed up real-time implementations as necessary in applications with high dynamic requirements.

Practical Active Disturbance Rejection Control: Bumpless Transfer, Rate Limitation, and Incremental Algorithm

Practical applications of controllers often impose further requirements on the implementation beyond the actual control performance, such as the ability to switch between manual and automatic control or between different control laws or controller parameter settings, known as bumpless transfer. Another common requirement is to limit the control signal in magnitude and/or rate. This paper examines and extends several discrete-time variants of active disturbance rejection control (ADRC), which is increasingly being applied especially in the field of power electronics and drives, in this regard. Detailed guidelines for practical ADRC implementations with these abilities are presented, and all features are being demonstrated with the help of simulation examples.

Learning non-convex fuzzy classifiers using single-class SVMs

In this paper, we propose an approach for building tree-like structured fuzzy classifiers. In order to learn classes for non-convex shapes of data, basic building blocks modelling convex classes are composed. For this purpose, an idea for the integration of single-class support vector machines (SVMs) into fuzzy class learning is sketched. The leading thought of this hybrid approach is the creation of a robust and most notably well interpretable parametric fuzzy classification model. Its feasibility is demonstrated in the context of a machine diagnosis task and compared to standard soft-margin SVMs.

Building Hybrid Fuzzy Classifier Trees by Additive/Subtractive Composition of Sets

Especially for one-class classification problems, an accurate model of the class is necessary. Since the shape of a class might be arbitrarily complex, it is hard to choose an approach that is generic enough to cope with the variety of shapes, while delivering an interpretable model that remains as simple as possible and thus applicable in practice. In this article, this problem is tackled by combining convex building blocks both additively and subtractively in a tree-like structure. The convex building blocks are represented by multivariate membership functions that aggregate the respective parts of the learning data. During the learning process, proven methods from support vector machines and cluster analysis are employed in order to optimally find the structure of the tree. Several academic examples demonstrate the viability of the approach.

Comment on “A Unified State-Space Model of Constant-Frequency Current-Mode Controlled Power Converters in Continuous Conduction Mode”

In their recent paper [IEEE Trans. Ind. Electron., vol. 62, no. 7, pp. 4514–4524, Jul. 2015], Smithson and Williamson propose a unified state-space model for power converters in peak current-mode control. As a part of their experimental section, the control-to-output frequency response is compared against the models of Ridley and Kondrath. The comparison reveals a significant difference between Ridley’s model and the proposed model as well as the measured frequency response. If this were true, controller designs relying on Ridley’s model would possibly have to be called into question, presumably affecting many practical designs. For this reason, the model comparison will be repeated here, showing that the results of Ridley’s model are in fact very close to the proposed model of Smithson and Williamson if the correct model formulation is being used.