Authors:
Jonathan P. How;
Pages: 4 - 6 Abstract: Presents the introductory editorial for this issue of the publication. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Jonathan P. How;
Pages: 7 - 10 Abstract: Reports on topics and articles presented in this issue of the publication. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Bob Bitmead;
Pages: 11 - 12 Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Pages: 13 - 14 Abstract: The quality of a weld joint is strongly influenced by both the spatial distribution and temporal history of material temperature during the welding process. In order to obtain high quality welds, despite disturbances which would otherwise alter welding temperatures, we have successfully designed a temperature control system for consumable electrode gas metal-arc welding. This article describes the development of an inexpensive optical system for measuring weld temperatures and a pseudogradient adaptive algorithm for self-tuning a proportional-integral (PI) bead temperature controller. The performance of the closed-system is illustrated with experimental data. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Pages: 14 - 14 Abstract: Presents corrections to information from the article, "“Bijnan Bandyopadhyay [People in Control],” (How, J.P.), IEEE Control Syst. Mag., vol. 39, no. 3, pp. 25–30, June 2019. doi: 10.1109/MCS.2019.2900817. PubDate:
Aug. 2019
DOI: 10.1109/mcs.2019.2900817. Issue No:Vol. 39, No. 4 (2019)
Authors:
Daniel E. Rivera;
Pages: 15 - 83 Abstract: Presents information on the CS society OutReach Fund program. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
J.A. Rossiter;B. Pasik-Duncan;A. Visioli;A. Serbezov;K. Zakova;M. Huba;S. Dormido;
Pages: 20 - 22 Abstract: Presents information of the CS society Presents information of the CS society Technical Committee on Control Education. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Alexander Medvedev;
Pages: 23 - 25 Abstract: Presents information of the CS society Technical Committee on Healthcare and Medical Systems. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Chul-Goo Kang;
Pages: 40 - 55 Abstract: Input shaping is an open-loop control technique for reducing residual vibrations in computer-controlled machines that is implemented by convolving a sequence of impulses (an input shaper) with any desired command [1]. The timing instants and amplitudes of the impulses are determined from the natural frequency and damping ratio of a vibratory system by solving a set of constraint equations [2]. Because the input shaper is generally located outside the feedback loop (Figure 1), input-shaping control does not affect the stability of the overall control system, which is a benefit compared to conventional feedback control. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Syed Aseem Ul Islam;Elizabeth L. Ratman;Ankit Goel;Dennis S. Bernstein;
Pages: 56 - 74 Abstract: Control in the power distribution system is needed to match the generation of electricity (that is, supply) with the demand for power (that is, load) at all times while maintaining a high level of power quality [1] (see "Summary"). The complexity of controlling the electric distribution system increases when distributed intermittent energy sources, such as solar photovoltaics (PVs), displace large-scale dispatchable generation [2]-[10]. If the supply from distributed resources exceeds the time-varying demand across a feeder, then the feeder-level voltage will increase. In extreme cases, large voltage increases will trigger the disconnection of distributed generation to protect electrical equipment and ensure customer safety. Consequently, coordinating renewable energy sources that are distributed throughout the distribution system-to match real-time demand across an entire power network-brings the additional challenge of maintaining a high level of supply reliability. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Bo Wahlberg;
Pages: 76 - 79 Abstract: As stated in the book’s preface, the author aimed to write a textbook that gives an accessible description of POMDPs, with emphasis on filtering and controlled sensing. The four parts of this book certainly achieve this objective. A nice feature of the text is that it goes into substantial depth in several areas that have not appeared previously in other works. These include structural results for POMDPs, controlled sensing and weak derivatives for reinforcement learning of MDPs, and quickest detection with social learning that involves the interaction of local and global decision makers. The book also has a wealth of examples that motivate POMDPs, including social networks, behavioral economics (risk averse social learning), adaptive radars, and optimal search. The supplementary material contains numerous problem sets and case studies, making this book an excellent text for a graduate-level course. The book is pitched at a similar mathematical level as the classic [10]. Since the text focuses only on discrete-time filtering and stochastic control (POMDPs) where the state space is the unit simplex (therefore, compact), measure-theoretic aspects [11] are not required. Partially Observed Markov Decision Processes: From Filtering To Controlled Sensing is a valuable contribution to the literature on stochastic decision-making/control and stochastic optimization, and I highly recommend it to a diverse audience, including students and researchers in electrical engineering, applied mathematics, computer science, and operations research. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Clara M. Ionescu;
Pages: 80 - 80 Abstract: As the demand for individualized products increases, there is a need for advanced control at the supervisory level, where model predictive control (MPC) becomes an established solution in industry. This is an excellent text that provides numerous MPC algorithms supported by highly relevant, illustrative industry examples. The book opens with a comprehensive chapter about industry’s need for predictive control, reminding readers that MPC originated in business and expanded into academia, where it captured the attention of several seminal researchers. Addresses some of the following topics: major concepts of PID; properties of model-based control and requirements of MPCs; predictive function control (PFC); basic predictive control algorithms; principle components of dual-mode MPC; and constraint handling mechanisms. The book has a set of Matlab code (from MathWorks) and sources to help readers reproduce the illustrations in each chapter. A First Course in Predictive Control can be considered as a textbook, since it includes introductory subchapters to guide lecturers and help filter the sections for teaching from areas outside the course. In summary, this book provides an excellent course in MPC, presented in the form of a textbook, and great support for lecturers. It also offers a substantial starting point for MPC-related research for industrial applications. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Pages: 84 - 96 Abstract: Presents information on the Second IFAC Conference on Cyber-Physical and Human Systems. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Pages: 86 - 86 Abstract: Provides a listing of upcoming conference events of interest to practitioners and researchers. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Authors:
Francesco Bullo;
Pages: 87 - 94 Abstract: Reports on changes to the CS Society Constitution and Bylaws. PubDate:
Aug. 2019
Issue No:Vol. 39, No. 4 (2019)
Hybrid journal (It can contain Open Access articles)
