Time-and event-driven communication process for networked control systems: A survey

Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Integrated simulation of ground motion mitigation, techniques for the future compact linear collider (CLIC)

CLIC is a proposal of CERN for a future high-energy particle collider. CLIC will collide electron and positron beams at a centre of mass energy of 3TeV with a desired peak luminosity of 2x10^3^4cm^-^2s^-^1. The luminosity performance of CLIC is sensitive to ground motion. Ground motion misaligns accelerator components, most importantly quadrupole magnets, which leads to emittance growth and beam-beam offset at the interaction point. This paper discusses the beam based feedback strategies currently used together with mechanical stabilization systems to address the above mentioned issues. These strategies consist of an Interaction Point Feedback (IPFB) and an Orbit Feedback (OFB). The two feedbacks have been designed independently and the main objective of this paper is to show how they interact. A simulation program is used in order to simulate the whole collider, it includes the behaviour of the beams, magnets, supports, ground attenuators, sensors, and actuators. Beam-offset feedback optimization and integrated simulations have been performed and results show that despite a detrimental coupling of both feedbacks at high frequency, it is possible to decrease the beam-beam offset and maintain the desired luminosity

Adaptation, Mitigation and Innovation: A Comprehensive Approach to Climate Policy

The ultimate question that most interests policy makers is how to reduce the climate change vulnerability of socio-economic systems in the most cost-effective manner. Extended literature has investigated the different dimensions of mitigation strategies, whereas much less can be found on adaptation. Even less can be found on the interactions between adaptation and mitigation. The increasing emphasis on adaptation raises a set of still unanswered questions concerning the design of an optimal mix of mitigation and adaptation measures. This paper presents an Integrated Assessment Model (IAM) that explicitly models the connections between mitigation, climate change impacts and adaptation. Compared to the few existing studies in the field, our framework provides a more detailed characterisation of adaptation processes. Adaptation activities have been distinguished from adaptive capacity building. We also provide an updated quantitative support for the calibration of adaptation costs and benefits. Using this framework, we explore issues such as the optimal timing of mitigation and adaptation, the trade-off between mitigation and adaptation, and the regional distribution of investments and residual damage.Climate change impacts, mitigation, adaptation, integrated assessment model

Robust fractional order PI control for cardiac output stabilisation

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved