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Intelligent Autonomous Adaptive Control (AAC) Method and AAC systems

Prof. Alexander ZHDANOV Head of Adaptive control methods Departmentalexander.zhdanov@ispras.ruhttp://www.aac-lab.comhttp://www.ispras.ru /~zhdanov

AAC system is the self-learning neuron-like adaptive control system based on empirical knowledge

ANN - artificial neural networks

FLS - fuzzy logic systems

ES - expert systems

RLS - reinforcement learning systems

. . . AAC - autonomous adaptive control systems

. . .

Institute for System Programming, Russian Academy of Sciences, MoscowInstitute for System Programming, Russian Academy of Sciences, Moscow

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The AAC system structure and functions - depart from logical simulation of the biologic nervous system of natural organisms

• Automatic classification

• Pattern recognition

• Research of the functional properties of given controlled object and environment

• Acquisition of “knowledge” about possibilities for control of the given object

• Saving of empirical knowledge in the “knowledge base”

• Inference of new knowledge from old one

• Qualitative appraisal of knowledge (“emotions” modeling)

• Qualitative appraisal of the object states

• Decision making

• and some others tasks.

AAC system automatically solves the following tasks in one control process framework:

The AAC system goal functions: Survival of the System Knowledge accumulation

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• “Control paradigm” (not “recognition paradigm” only). The AAC system automatically finds the way to control of given object

• The AAC system is a complex of subsystems solving a few “intelligent” tasks

• Self-learning and control in one process

• A precise mathematical model of controlled object is not used

• Multi-criteria and many-purposes control

• The control system is applicable for control of objects of different types

• The AAC system can be used additionally to a standard controller and/or as the system for decision making support

Main features of the AAC control system

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AAC method could be ranked among the “intellectual” methods but it has some useful advantages

ANN artificial neural

networks

ANN artificial neural

networks

FLS fuzzy logic systems

FLS fuzzy logic systems

ESexpert systems

ESexpert systems RLS

reinforcement learning systems

RLS reinforcement

learning systems

AACautonomous

adaptive control systems

AACautonomous

adaptive control systems

Previous learning.Recognition or approximation

paradigm

Previous forming of the

fuzzy rules

Previous forming of the expert control

rules

Previous learning

• Adaptive control• Learning and control in one

process• A mathematical model of

controlled object is not used• Multi-criteria and many-purposes

control

. . .. . .

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The AAC system useful features

• In comparison with ANN the AAC system gives adaptive control, not only recognition as ANN, has more rapid learning and learns directly in control process. It has no the “catastrophic forgetting” problem.

• In comparison with ES and FLS the AAC system gives automatic adaptive control, accumulates and uses its empirical knowledge. But if it is necessary the AAC could be previously trained by expert or by means of archive data.

• In comparison with RLS the AAC system is more complex system, it adapts and relearns directly during control process. RLS maps a set of patterns to a set of qualitative appraisals, AAC maps the set of patterns to the set of patterns with relation

of a set of appraisals.

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When and where we can use the AAC system?

If we would like to have automatic control of an object but: • we have neither a “control law” for it nor a mathematical model of

the object and environment (using of traditional control methods is difficult), and

• we have no experience of control of given object (using of expert system is difficult), and

• we know that the object has some regularities, which can be used for control and you do not know the regularities a priori or they change in the time (using of traditional artificial neural networks is difficult), and

• there are “sensors”, “actuators” and qualitative criteria for

estimations of the controlled process,

then we can try to use the AAC system.

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Some examples of application adaptive controlled systems on basis of the

AAC method

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“AdCAS” System – Adaptive Control of Active Car Suspension

The car suspension has to have an active actuator. Then the AAC accumulates empirical knowledge about properties of given car and controls the system by means “clever pushes”.

Active high pressure shock absorber

forcepressure

or shock absorber with magneto-reological fluid (MRF)

AdCAS system increases the comfort, stability and controllability of the car

Empirical Knowledge Base

Obstacle on the road

Smooth motion of the car body under control

Control pulses to actuator

ISP RASISP RASATS APSATS APS

Without control

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The control quality increases as the Knowledge Base accumulates the empirical knowledge

Russian Russian Space Space AgencyAgency

“ PILOT ” System - the adaptive systemof angular motion stabilization of space satellite

Empirical Knowledge Base

Controlled Process

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The goal function is the automatic creation of behavior stereotypes when the robot runs into obstacles

Learning and control in one process

Adaptive Neuron-like Control System for Mobile Robot (for example as an nurse)

Visual and tactile sensors

Actuators

“Gnome # 8”

Obstacles

Mobile robot

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The control quality of increases (the quantity of smashes decreases) as the Knowledge Base fulfils

The frequency of running into the obstacles decreases in the robot life time.

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The Tactician system tries to control the social object

The traditional artificial neural network can only predict some situations

In the case the controlled object is a social object

Analytical Analytical Center of Center of PresidentPresident

“TACTICIAN” System – the Adaptive System Prototype for Decision Making Support

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We start an investigation of the possibilities of the AAC method using for adaptive control of prosthesis.

We want the ААС system should adapt to a human body and to kinematics of the prosthesis.

AAC system for adaptive control of prosthesis

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The modern soft- and hardware around us have one common property – the brilliant absence of their adaptability

There are two reasons for the situation:

designers do not declare the aim to create the objects as adaptive objects

there are not appropriate methods to do the objects adaptive

Adaptive Soft- and Hardware – why not?

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people adapt themselves under communication

people and animals adapt themselves in communication

when a person interacts with a device the person adapts to the device but the device does it never

We guess each device can automatically adapt to user in many respects !

In the nature all objects are adaptive

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We are convinced that the AAC can be used in a lot of devices and software

1. Car production

2. Space industry

3. Medical equipment

4. Telecommunication systems

5. Software

6. Machine tools production

7. etc.

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Thank you for attention

Prof. Alexander Zhdanov alexander.zhdanov@ispras.ruhttp://www.aac-lab.comhttp://www.ispras.ru /~zhdanov

Institute for System Programming, Russian Academy of Sciences, MoscowInstitute for System Programming, Russian Academy of Sciences, Moscow