PID controller design and tuning in networked control systems

Networked control systems (NCS) are distributed real-time computing and control systems with sensors, actuators and controllers that communicate over a shared medium. The distributed nature of NCS and issues related to the shared communication medium pose significant challenges for control design, as the control system no longer follows the rules of classical control theory. The main problems that are not well covered by the traditional control theory are varying time-delays due to communication and computation, and packet losses. During recent years, the control design of NCS and varying time-delay systems has been extensively researched. This investment has provided us with new results on stability. Often the proposed methods and solutions are far too complex for industrial use, especially if wireless automation applications are considered. The algorithms are computationally heavy, possibly requiring complete information from say, a network of hundreds or thousands of nodes. In the wireless case this is not feasible. The above justifies the use and research of simple controller structures and algorithms for NCS. Despite the growing interest towards more advanced control algorithms, the Proportional-Integral-Derivative (PID) controller still has a dominant status in the industry. Nevertheless, using PID for NCS has not been thoroughly investigated, especially with regard to controller tuning. This thesis proposes several PID tuning methods, which provide robustness against the challenges of NCS, namely varying time-delays (jitter) and packet loss. The doctoral thesis consists of a summary and eight publications that focus on the PID controller design, tuning and experimentation in NCS. The thesis includes a literature review of recent stability and control design results in NCS, a summary of publications and the original publications. The control design methods applied in the publications are also reviewed. In the thesis, several new methods for PID tuning in NCS are proposed. To make the methods usable, a PID tuning tool that implements one of the tuning methods is also developed. In order to verify the results of control design with real processes, the thesis suggests using the MoCoNet platform developed at the Helsinki University of Technology, Finland. The platform provides the tools for remote laboratory experiments in NCS settings. The results of the thesis indicate that the PID controller is well suited for NCS provided that the properties of the integrated communication and control system are taken into account in the tuning phase

Comparison of low-complexity controllers in varying time-delay systems

Abstract : Motivated by the recent developments in networked control systems and control over wireless, this paper presents a comparison of five control algorithms that are based on PID, IMC and fuzzy gain scheduling techniques and discusses their performance in varying time-delay systems. The low complexity of the proposed algorithms makes their use attractive in resource-constrained environments such as wireless sensor and actuator networks. The control system consists of a controller, a simple process and an output delay in the feedback loop. Three different delay models are considered in this framework; constant, random, and correlated random delay. In addition to presenting modifications to the control algorithms to better fit the varying time-delay systems a delay-robust tuning method is proposed, and the performance of various controllers is evaluated using simulation. The results show the benefits of adapting the controller parameters based on delay measurement if its amplitude is significant with respect to the time-constant of the process. Nevertheless, the PID algorithm used in the study also performs well in all scenarios, and this is achieved by its careful tuning

GeneRegionScan: a Bioconductor package for probe-level analysis of specific, small regions of the genome

Summary: Whole-genome microarrays allow us to interrogate the entire transcriptome of a cell. Affymetrix microarrays are constructed using several probes that match to different regions of a gene and a summarization step reduces this complexity into a single value, representing the expression level of the gene or the expression level of an exon in the case of exon arrays. However, this simplification eliminates information that might be useful when focusing on specific genes of interest. To address these limitations, we present a software package for the R platform that allows detailed analysis of expression at the probe level. The package matches the probe sequences against a target gene sequence (either mRNA or DNA) and shows the expression levels of each probe along the gene. It also features functions to fit a linear regression based on several genetic models that enables study of the relationship between gene expression and genotype

AllelicImbalance: An R/ bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing

BACKGROUND: One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases. RESULTS: We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons. CONCLUSIONS: The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0620-2) contains supplementary material, which is available to authorized users

Relationship between CAD Risk Genotype in the Chromosome 9p21 Locus and Gene Expression. Identification of Eight New ANRIL Splice Variants

BACKGROUND: Several genome-wide association studies have recently linked a group of single nucleotide polymorphisms in the 9p21 region with cardiovascular disease. The molecular mechanisms of this link are not fully understood. We investigated five different expression microarray datasets in order to determine if the genotype had effect on expression of any gene transcript in aorta, mammary artery, carotid plaque and lymphoblastoid cells. METHODOLOGY/PRINCIPAL FINDINGS: After multiple testing correction, no genes were found to have relation to the rs2891168 risk genotype, either on a genome-wide scale or on a regional (8 MB) scale. The neighbouring ANRIL gene was found to have eight novel transcript variants not previously known from literature and these varied by tissue type. We therefore performed a detailed probe-level analysis and found small stretches of significant relation to genotype but no consistent associations. In all investigated tissues we found an inverse correlation between ANRIL and the MTAP gene and a positive correlation between ANRIL and CDKN2A and CDKN2B. CONCLUSIONS/SIGNIFICANCE: Investigation of relation of the risk genotype to gene expression is complicated by the transcript complexity of the locus. With our investigation of a range of relevant tissue we wish to underscore the need for careful attention to the complexity of the alternative splicing issues in the region and its implications to the design of future gene expression studies

Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a grid-connected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab)showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid\u27s total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid\u27s peak power cost as shown by its 20.2% increase in comparison to the ideal case