Rational Adaptation of Control Systems for the Autonomous Aircraft Motion

Rational Adaptation of Control Systems for the Autonomous Aircraft Motion

Kostiyantin Dergachov (Kharkiv Aviation Institute, Ukraine) and Anatolii Kulik (National Aerospace University, Ukraine)
DOI: 10.4018/978-1-7998-1415-3.ch002

Abstract

The possibilities of using an adaptation principle in an application for organizing the close-loop life circuit of autonomous fly vehicles (FV) are discussed in the chapter. The uncertainties arising at each stage of the life cycle of an autonomous FV are considered. To solve a problem the approach with using intelligent, rational objects and using knowledge database tool is proposed. The main theses of rational adaptation control system (CS) are represented. The preliminary designing tools for constructing rational adaptation algorithms for the motion CS are considered. The practical applications of the proposed approach at the stage of preliminary design of control systems for autonomous fly vehicles are presented.
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Introduction

A Person can live safely only in balance with a Nature. Breaking this balance creates global problems. Global problems are the result of different principles of Nature and human society development. Inartificial Nature functions according to the principle of negative feedback, while human society operates according to the principle of positive feedback. Disrespectful attitude of Man to Nature leads to the extensive use of biosphere resources, which causes a significant deterioration in living conditions on Earth.

The Nobel Prize in Physics Pyotr Kapitsa in 1976 gave a lecture at the University of Stockholm on the topic: «Global Problems and Energy». In this lecture the main cause of the imbalance was formulated. «The cause of global problems is well known: a person differs from an animal mainly in that the animal adapts to nature and the person reworks and adapts it to his needs». How to eliminate this cause and recover the balance, it was suggested that P.L. Kapitsa in the report «Global scientific problems of the near future». He expressed the following idea: «The global crisis associated with the depletion of raw materials, science can prevent by transferring industrial production to so-called «closed processes» as is the case in nature, where nothing is thrown away, since everything is consumed again» (Kapitza, 1987).

Let’s consider the possibility of using a device – an adaptation to the organization of a «closed process» in relation to the life cycle of autonomous aircraft. A generalized life cycle diagram is shown in figure 1, reflecting typical features of the desired spiral-like development process of autonomous aircraft. Nine stages of the life cycle together pass – the hull structure bearing the airframe of the aircraft, the power plant and the control system. The control system differs from other components of the airframe and power plant in that it has such a functional element as an on-board computer or a complex. The functionality of modern computing tools allows for all components of autonomous aircraft to have an intelligent interface that provides high-quality adaptation to objectively changing both external and internal operating conditions during the life cycle.

Figure 1.

Generalized life cycle pattern of typical autonomous aircraft

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At all stages of the control systems life cycle, there are objectively reasons that destabilize operability. Operability is the ability of the control system to perform specified functions in accordance with the terms of reference. Thus, at the stage of conception, a not well-formulated concept of the control system creating generates problems and intractable tasks for the subsequent stages. At the design stage, the use of inadequate people, design errors and a number of other factors lead to poor-quality design solutions. At the third stage – the manufacture and testing of prototypes of the control system affects both errors and violations accumulated at the previous stages, and deviations from the design documentation, poor quality components and a number of other reasons that complicate the process of testing and refining the control system to ensure its operability. At each of the subsequent stages of the control systems life cycle, both errors and shortcomings of the previous stages as well as their own due to the specificity of production activities and operating conditions are manifested.

There is a tradition of using different types of models in the practice of designing manufacturing and operating control systems for autonomous aircraft. As a rule at the design stage these are verbal models using quantitative characteristics of the future control system. At the design stage, along with verbal models, graphic in the form of characteristics, functional and structural diagrams, mathematical and machine models, and static graphs, temporal and frequency characteristics are used. At the subsequent stages of the life cycle these are graphical models in the form of principal, assembly, flowcharts of algorithms and other schemes, tabular setup and debugging models and a number of other graphical and text documents corresponding to the life cycle stage.

Key Terms in this Chapter

Autonomous Fly Vehicles: An autonomous aircraft that is capable of performing its basic functions under destabilizing effects and has a vitality.

Diagnosability: A property of automatic control object that allows to unambiguously establish the causes of destabilizing effects by indirectly features that available for measurements in a finite time.

Operating State of the Automatic Control System: Such a state in which the values of all parameters characterizing the ability to perform specified functions correspond to the requirements of the technical specification.

Recoverability: A property of automatic control object that allows to use excess funds in its composition to recover an operability in a finite time.

Destabilizing Effects: Impacts that interfere with a performance of automatic control system.

Rational Control Object: An automatic control object that has properties of controllability and observability, as well as diagnosability and recoverability.

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