Roman B. Statnikov

L1 Adaptive Flight Control System: Systematic Design and Verification and Validation of Control Metrics

This paper presents preliminary results of the application of the Parameter Space Investigation method for the design of the L1 flight control system implemented on the two turbine-powered dynamically-scaled GTM AirSTAR aircraft. In particular, the study addresses the construction of the feasible solution set and the improvement of a nominal prototype design, obtained using the systematic design procedures of the L1 adaptive control theory. On the one hand, the results in the paper demonstrate the benefits of L1 adaptive control as a verifiable robust adaptive control architecture by validating the theoretical claims in terms of robustness and performance, as well as illustrating its systematic design procedures. On the other hand, the paper confirms the suitability of the Parameter Space Investigation method for the multicriteria design optimization of a flight control system subject to desired control specifications. Also, in order to facilitate the multicriteria analysis process, this study takes advantage of the Multicriteria Optimization and Vector Identification software package, which was designed to apply the Parameter Space Investigation method to engineering problems. The results and conclusions of this paper have contributed to the improvement of the (predicted) flying qualities and the robustness margins of the all-adaptive L1-augmented GTM AirSTAR aircraft.

Multicriteria Engineering Optimization Problems: Statement, Solution and Applications

The majority of engineering optimization problems (design, identification, design of controlled systems, optimization of large-scale systems, operational development of prototypes, and so on) are essentially multicriteria. The correct determination of the feasible solution set is a major challenge in engineering optimization problems. In order to construct the feasible solution set, a method called PSI (Parameter Space Investigation) has been created and successfully integrated into various fields of industry, science, and technology. Owing to the PSI method, it has become possible to formulate and solve a wide range of multicriteria optimization problems. In addition to giving an overview of the PSI method, this paper also describes the methods for approximation of the feasible and Pareto optimal solution sets, identification, decomposition, and aggregation of the large-scale systems.

Multicriteria analysis tools in real-life problems

Applied optimization problems such as design, identification, design of controlled systems, operational development of prototypes, analysis of large-scale systems, and forecasting from observational data are multicriteria problems in essence. Construction of the feasible solution set is of primary importance in the above problems. The definition of a feasible solution set is usually considered to be the skill of a designer. Even though this skill is essential, it is by no means sufficient for the correct statement of the problem. There are many antagonistic performance criteria and all kinds of constraints in these problems; therefore, it is quite difficult to correctly determine the feasible set. As a result, ill-posed problems are solved, and optimal solutions are searched for far from where they should be. As a consequence, the optimization results have no practical meaning. In this work we propose methods and tools that will assist the designer in defining the feasible solution set correctly.

Multi-Criteria Identification of a Controllable Descending System

This paper introduces an effective computational environment for multi-objective decision-making, optimization and identification. The paper adopts multi-objective vector identification methodology and performance assessment provided by the parameter space investigation method (PSI). The main feature of this methodology is in the fact that various design objectives are taken into consideration in their natural form without reducing dimensionality of the problem and therefore without distorting its nature. Therefore, there is no need for artificial convolution and weighting of multiple criteria. Moreover, the design alternatives are assessed explicitly versus multiple given requirements. The main practical purpose of this work is of twofold. First, we introduce an optimization framework and technique that allows to determine feasible and Pareto sets of the numerous uncertainties inherent for real-world engineering systems. This framework tightly couples principal advantages of MatLab/Simulink simulation engine with the unique properties of the multi-objective PSI method. Second, we show key benefits of the MatLab/PSI bundle on the example of identification of the principal aerodynamic characteristics and apparent masses of the controllable circular parachute

Multi-criteria approach in configuration of energy efficient sensor networks

Problem and Motivation: A designing of energy efficient wireless sensor networks is one of the most trendy research topics. In this paper the authors developed a new multi-criteria approach which allows filtering out non-efficient topologies during a phase of designing of a sensor network and also running a self-adjustment process during its functioning. It is shown that the problem of designing of an energy efficient wireless sensor network belongs to the class D of optimization combinatorial problems for which the equivalent recognition problems are NP-complete or open. The D-class (Peltsverger B., Khavronin O. (1999)) is such that according to a decomposition approach there exist particular criteria compatible with the main one, and the recognition task corresponding to a particular criterion always belongs to the P-class problem.

Multicriteria design of composite pressure vessels

Design of composite pressure vessels is characterised by multiple contradictory performance criteria, such as winding on the heads, dimension, volume, and weight. Statement and solution of such problems requires search for compromise solutions and consideration of multiple criteria simultaneously. Therefore, single-criterion approaches are generally not applicable. The paper presents a multicriteria methodology for search for feasible and Pareto optimal solutions for the design of composite pressure vessels. The methodology is based on the parameter space investigation (PSI) method. The PSI method is implemented in the multicriteria optimisation and vector identification (MOVI) software system. Using the above tools, this work provides a numerical example of statement and solution of the problem of multicriteria design of composite pressure vessels.

Visualization Tools for Multicriteria Analysis of the Prototype Improvement Problem

One of the basic engineering optimization problems is the problem of improving a prototype. This problem is constantly encountered by industrial and academic organizations that produce and design various objects (e.g., motor vehicles, machine tools, ships, and aircraft). This paper presents an approach for improving a prototype by construction of the feasible and Pareto sets while performing multicriteria analysis. We introduce visualization methods that facilitate constructing the feasible and Pareto sets. Using these techniques, an expert can correctly state and solve the problem under consideration in a series of dialogs with the computer. Finally, we present a case study of applying these methods to a problem of improving a prototype of the ship

DBS-PSI: a new paradigm of database search

The advent of the World Wide Web made search engines the most essential component of our everyday life. However, the analysis of information provided by current search engines often presents a significant challenge to the client. This is to a large extent because the client has to deal with many alternatives (solutions) described by contradictory criteria, when selecting the most preferable (optimal) solutions. Furthermore, criteria constraints cannot be defined a priori and have to be defined interactively in the process of a dialog of the client with computer. In such situations, construction of the feasible solution set has a fundamental value. In this paper, we propose a new methodology for systematically constructing the feasible solution set for database search. This allows to significantly improving the quality of search results.

MODELING AND SIMULATION FRAMEWORK FOR MULTI-OBJECTIVE IDENTIFICATION OF A GUIDED DESCENDING SYSTEM

The paper addresses the task of multi-objective identification of a controllable descending system. The main goal of this work is to develop an optimization framework and technique that can determine feasible and Pareto sets of the numerous uncertainties typical of a parachute-based descending system. The work employs the unique multi-objective optimization method called, the Parameter Space Investigation method. This paper illustrates an advantage of such a technique based on an example of identifying the unknown parameters of the controllable descending system that were discovered during our previous work. Furthermore, it provides a step-by-step implementation of the multi-objective identification of a mathematical model of a controllable circular parachute. The work presents the equations of motion, which are followed by the parameterization of acting aerodynamic forces and moments. It then introduces an application of a twostep parameters identification technique that employs two types of airdrop data (the controlled and uncontrolled set). The paper ends with a summary of the obtained results.