Shape-independent model predictive control for Takagi-Sugeno fuzzy systems

[EN] Predictive control of TS fuzzy systems has been addressed in prior literature with some simplifying assumptions or heuristic approaches. This paper presents a rigorous formulation of the model predictive control of TS systems, so that results are valid for any membership value (shape-independent) with a suitable account of causality (control can depend on current and past memberships and state). As in most fuzzy control results, a family of progressively better controllers can be obtained by increasing Polya-related complexity parameters, generalising over prior proposals. (C) 2017 Elsevier Ltd. All rights reserved.The authors are grateful to the financial support of Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE), and grant P11B2015-36 (Universitat Jaume I).Ariño-Latorre, CV.; Querol-Ferrer, A.; Sala, A. (2017). Shape-independent model predictive control for Takagi-Sugeno fuzzy systems. Engineering Applications of Artificial Intelligence. 65:493-505. https://doi.org/10.1016/j.engappai.2017.07.011S4935056

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Relaxed LMI conditions for control of nonlinear Takagi-Sugeno models

Los problemas de optimización de desigualdades matriciales lineales en control borroso se han convertido en la herramienta más utilizada en dicha área desde los años 90. Muchos sistemas no lineales pueden ser modelados como sistemas borrosos de modo que el control borroso puede considerarse como una técnica de control no lineal. Aunque se han obtenido muchos y buenos resultados, quedan algunas fuentes de conservadurismo cuando se comparan con otros enfoques de control no lineal. Esta tesis discute dichas cuestiones de conservadurismo y plantea nuevos enfoques para resolverlas. La principal ventaja de la formulación mediante desigualdades matriciales lineales es la posibilidad de asegurar estabilidad y prestaciones de un sistema no lineal modelado como un sistema borroso Takagi-Sugeno. Estos modelos están formados por un conjunto de modelos lineales eligiendo el sistema a aplicar mediante el uso de unas reglas borrosas. Estas reglas se traducen en funciones de interpolación o de pertenecía que nos indican el grado de validez de un modelo lineal respecto del resto. El mayor problema que presentan estas técnicas basadas en desigualdades matriciales lineales es que las funciones de pertenencia no están incluidas en las condiciones de estabilidad del sistema, lo que significa que se prueba la estabilidad y prestaciones para cualquier forma de interpolación entre los diferentes modelos lineales. Esto genera una fuente de conservadurismo que sería conveniente limitar. En la tesis doctoral se presentan varias metodologías capaces de trasladar la información de las funciones de pertenencia del sistema al problema basado en desigualdades matriciales lineales de estabilidad y prestaciones. Las dos principales aportaciones propuestas se basan, respectivamente, en introducir una serie de matrices de relajación que permitan incorporar esta información y en aprovechar la descripción de una amplia clase de sistemas borrosos en productos tensoriales de...Ariño Latorre, CV. (2008). Relaxed LMI conditions for control of nonlinear Takagi-Sugeno models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8301Palanci

Novel fuzzy techniques for modelling human decision making

Standard (type-1) fuzzy sets were introduced to resemble human reasoning in its use of approximate information and uncertainty to generate decisions. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision problems can be greatly simplified. However, standard type-1 fuzzy sets have limitations when it comes to modelling human decision making. In many applications involving the modelling of human decision making (expert systems) the more traditional membership functions do not provide a wide enough choice for the system developer. They are therefore missing an opportunity to produce simpler or better systems. The use of complex non-convex membership functions in the context of human decision making systems were investigated. It was demonstrated that non-convex membership functions are plausible, reasonable membership functions in the sense originally intended by Zadeh. All humans, including ‘experts’, exhibit variation in their decision making. To date, it has been an implicit assumption that expert systems, including fuzzy expert systems, should not exhibit such variation. Type-2 fuzzy sets feature membership functions that are themselves fuzzy sets. While type-2 fuzzy sets capture uncertainty by introducing a range of membership values associated with each value of the base variable, but they do not capture the notion of variability. To overcome this limitation of type-2 fuzzy sets, Garibaldi previously proposed the term ‘non-deterministic fuzzy reasoning’ in which variability is introduced into the membership functions of a fuzzy system through the use of random alterations to the parameters. In this thesis, this notion is extended and formalised through the introduction of a notion termed a non-stationary fuzzy set. The concept of random perturbations that can be used for generating these non-stationary fuzzy sets is proposed. The footprint of variation (FOV) is introduced to describe the area covering the range from the minimum to the maximum fuzzy sets which comprise the non-stationary fuzzy sets (this is similar to the footprint of uncertainty of type-2 sets). Basic operators, i.e. union, intersection and complement, for non-stationary fuzzy sets are also proposed. Proofs of properties of non-stationary fuzzy sets to satisfy the set theoretic laws are also given in this thesis. It can be observed that, firstly, a non-stationary fuzzy set is a collection of type-1 fuzzy sets in which there is an explicit, defined, relationship between the fuzzy sets. Specifically, each of the instantiations (individual type-1 sets) is derived by a perturbation of (making a small change to) a single underlying membership function. Secondly, a non-stationary fuzzy set does not have secondary membership functions, and secondary membership grades. Hence, there is no ‘direct’ equivalent to the embedded type-2 sets of a type-2 fuzzy sets. Lastly, the non-stationary inference process is quite different from type-2 inference, in that non-stationary inference is just a repeated type-1 inference. Several case studies have been carried out in this research. Experiments have been carried out to investigate the use of non-stationary fuzzy sets, and the relationship between non-stationary and interval type-2 fuzzy sets. The results from these experiments are compared with results produced by type-2 fuzzy systems. As an aside, experiments were carried out to investigate the effect of the number of tunable parameters on performance in type-1 and type-2 fuzzy systems. It was demonstrated that more tunable parameters can improve the performance of a non-singleton type-1 fuzzy system to be as good as or better than the equivalent type-2 fuzzy system. Taken as a whole, the techniques presented in this thesis represent a valuable addition to the tools available to a model designer for constructing fuzzy models of human decision making

Development of new methodologies for the weight estimation of aircraft structures

The problem of weight estimation in the aerospace industry has been acquiring considerably greater importance in recent years, due to the numerous challenges frequently encountered in the preliminary phases of the design of a new aircraft. This is the stage where it is possible to make design changes without incurring into excessive cost penalties. On the other hand, the knowledge of the design, of the relationships existing between the different variables and their subsequent impact on the final weight of the structure is very limited. As a result, the designer is unable to understand the true effect that individual design decisions will produce on the weight of the structure. In addition to this, new aircraft concepts end up being too conservative, due to the high dependency of current weight estimation methods to historical data and off-the-shelf design solutions. This thesis aims at providing an alternative framework for the weight estimation of aircraft structures at preliminary design stages. By conducting a thorough assessment of current state-of-the-art approaches and tools used in the field, fuzzy logic is presented as an appropriate foundation on which to build an innovative approach to the problem. Different adaptive fuzzy approaches have been used in the development of a methodology which is able to combine an analytical base to the structural design of selected trailing edge components, with substantial knowledge acquisition capabilities for the computation of robust and reliable weight estimates. The final framework allows considerable flexibility in the level of detail of the estimate consistent with the granularity of the input data used. This, combined with an extensive uncertainty analysis through the use of Interval Type-2 fuzzy logic, will provide the designer with the capabilities to understand the impact of error propagation within the model and increase the confidence in the final estimat

Quality of Service for Multimedia and Control System Applications in Mobile Ad-hoc Network

A Mobile Ad-Hoc Network (MANET) is a collection of randomly distributed infrastructure-less mobile nodes that form a wireless network. These Mobile nodes have the capability to act as a host or relay. As a host, the mobile nodes can be the source and/or destination of traffic, and when acting as a relay, they can be an intermediate node that forwards the traffic to its destination. Some of the challenges of a MANET include the dynamic network topology, device discovery, power constraints, wireless channel conditions and limited network resources. These challenges degrade the network performance and thus affect the network stability and robustness. Therefore, it is difficult for a MANET to attain the Quality of Service (QoS) of a wired network. This thesis aims to address the problem of the limited wireless network resources by proposing two adaptive scheduling algorithms that can adapt in real-time to the changes in the network. To achieve the aim; this thesis first analyses the behaviour of various application profiles in a queue. It models Voice, Email, and Internet Browsing traffic (by specifying packet sizes, and inter-arrival rates based on various distributions) separately and then simultaneously in a common network for uncongested and congested conditions, after which scheduling is applied in order to improve the overall network performance. The Voice traffic profile is then added to the UDP/IP protocol stack and the network performance is compared to a simple node without the UDP/IP protocol stack. A realistic wireless propagation model for the simulation is developed from a point-to-point open-field outdoor experiment. This thesis proposes two adaptive priority fuzzy based scheduler for a MANET, the priority of packets in the queue are determined based on the real-time available network resources. The methodology for transmitting a live-feed video stream over OPNET to validate the scheduler is also presented. An interface between the simulation and hardware is created to send real-time video traffic through the simulation network. This thesis concludes by showing that the performance of a MANET network can be improved by applying an adaptive scheduler

Occupancy driven supervisory control of indoor environment systems to minimise energy consumption of airport terminal building

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns

Inspection and Monitoring of Structural Damage Using Vibration Signatures and Smart Techniques

The structural damage detection plays an important role in the evaluation of structural systems and to ensure their safety. Structures like large bridges should be continuously monitored for detection of damage. The cracks usually change the physical parameters like stiffness and flexibility which in turn changes the dynamic properties such as natural frequencies and mode shapes. Crack detection of a beam element comprises of two aspects: the first one is the forward problem which is achieved from the Eigen parameters and the second one is the process to locate and quantify the effect of damage and is termed as ‘inverse process of damage detection’. In the present investigation the analytical and numerical methods are known as the forward problem includes determination of natural frequencies from the knowledge of beam geometry and crack dimension. The vibration signals are derived from the forward problem is exploited in the inverse problem. The natural frequency changes occur due to the various reasons such as boundary condition changes, temperature variations etc. Among all the changes boundary condition changes are the most important factors in structural elements. Many major structures like bridges are made up of uniform beams of unknown boundary conditions. So in the present investigation two of the boundary conditions i.e. fixed -free and fixed- fixed are considered. Using the forward solution method, the natural frequencies are determined. In the inverse solution method various Artificial Intelligence (AI) techniques with their hybrid methods are proposed and implemented. Damage detection problems using Artificial Intelligence techniques require a number of training data sets that represent the uncracked and cracked scenarios of practical structural elements. In the second part of the work different AI techniques like Fuzzy Logic, Genetic Algorithm, Clonal Selection Algorithm, Differential Evolution Algorithm and their hybrid methods are designed and developed. In summary this investigation is a step towards to forecast the position of the damage using the Artificial Intelligence techniques and compare their results. Finally, the results from the Artificial Intelligence techniques and their hybridized algorithms are validated by doing experimental analysis

The sequence stratigraphy of the East China Sea continental margin (late Pleistocene to present)

The East China Sea continental margin is characterized by abundant sediment supply, high energy, and a unique shelf physiography - a fairly shallow shelf with a low gradient, extreme width, and deep shelf-slope break. Three sequences are chronologically constrained by core data in the shallow strata from the late Pleistocene to present. Regional seismic surveys, collected as part of a broader investigation, provide unique observations unavailable to previous studies. A revised stratigraphy is presented that provides an alternate stratigraphic framework and, in the process, clarifies existing ECS stratigraphic nomenclature. In these strata, the lowstand fluvial systems, prior to oxygen isotope stage 4 do not incise the East China Sea continental margin. Instead, they comprise laterally (>400 km) and vertically (>40 m) extensive sand sheets interpreted as low-energy and low-sinuosity fluvial braidplains dominated by bed load. It is suggested in this paper that the rare nature of lowstand unincised fluvial systems (LUFS) are related to the rare nature of the extreme depositional boundary conditions forming the LUFS on continental margins (i.e., low gradient, deep shelf-slope break, abundant sediment supply). Extensive (>100 km), unincised fluvial sheet sands resembling the LUFS from the ECS margin are preserved in foreland basins throughout the Phanerozoic. Because this stratal architecture is similar to the LUFS preserved on the ECS margin, it may have been created under similar depositional conditions. In order to better understand stratal sensitivity under these conditions, a three-dimensional, forward simulator (fuzzyPEACH) was developed. In this investigation, numerous scenarios of eustasy, tectonic subsidence, and sediment influx were simulated. The various rates and magnitudes of eustatic fluctuations may also be used as a proxy for the effect of margin physiography on stratal geometry, although fuzzyPEACH allows the geometry to be user defined. FuzzyPEACH simulations collectively uses only five FISs containing a total of 21 separate rules. These rules incorporate 15 variables and are defined by 47 fuzzy sets. Fuzzy logic was chosen for its ability to quantify subjectivity, by capturing the vagueness of linguistic terms, and assemble a robust fuzzy logic inference system, in relatively short order, that can describe complex, nonlinear relationships