Intelligent Autonomous Adaptive Control ( AAC) Method and AAC systems

1. Institute for System Programming, Russian Academy of Sciences, Moscow Intelligent Autonomous Adaptive Control (AAC) Method and AAC systems Prof. Alexander ZHDANOVHead of Adaptive control methods Department alexander.zhdanov@ispras.ru http://www.aac-lab.com http://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. . .

2. The AAC systemstructure and functions - depart from logical simulation of the biologic nervous system of natural organisms AAC system automatically solves the following tasks in one control process framework: • 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. • The AAC system goal functions: • Survival of the System • Knowledge accumulation

3. Main features of the AAC control system • “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

4. AAC method could be ranked among the “intellectual” methods but it has some useful advantages Previous learning. Recognition or approximation paradigm • Adaptive control • Learning and control in one process • A mathematical model of controlled object is not used • Multi-criteria and many-purposes control ANN artificial neural networks AAC autonomous adaptive control systems FLS fuzzy logic systems Previous forming of the fuzzy rules . . . ES expert systems RLS reinforcement learning systems Previous forming of the expert control rules Previous learning

5. 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.

6. 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.

7. Some examples of application adaptive controlled systems on basis of the AAC method

8. force pressure ATS APS “AdCAS” System – Adaptive Control of Active Car Suspension ISP RAS Without control Obstacle on the road Smooth motion of the car body under control Control pulses to actuator Active high pressure shock absorber Empirical Knowledge Base The car suspension has to have an active actuator. Then the AAC accumulates empirical knowledgeabout properties of given car and controls the system by means “clever pushes”. or shock absorber with magneto-reological fluid (MRF) AdCAS system increases the comfort, stability and controllability of the car

9. Controlled Process Empirical Knowledge Base Russian Space Agency “ PILOT ” System - the adaptive systemof angular motion stabilization of space satellite The control quality increases as the Knowledge Base accumulates the empirical knowledge

10. Adaptive Neuron-like Control System for Mobile Robot (for example as an nurse) “Gnome # 8” Obstacles Mobile robot Visual and tactile sensors Actuators The goal function is the automatic creation of behavior stereotypes when the robot runs into obstacles Learning and control in one process

11. 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.

12. Analytical Center of President “TACTICIAN” System – the Adaptive System Prototype for Decision Making Support In the case the controlled object is a social object The Tactician system tries to control the social object The traditional artificial neural network can only predict some situations

13. AAC system for adaptive control of prosthesis We start an investigation of the possibilities of the AAC method using for adaptive control of prosthesis. Wewantthe ААСsystemshould adapttoa humanbodyandtokinematicsof the prosthesis.

14. Adaptive Soft- and Hardware – why not? • 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

15. In the nature all objects are adaptive • 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 Why ?! We guess each device can automatically adapt to user in many respects !

16. We are convinced that the AAC can be used in a lot of devices and software • Car production • Space industry • Medical equipment • Telecommunication systems • Software • Machine tools production • etc.

17. Institute for System Programming, Russian Academy of Sciences, Moscow Thank you for attention Prof. Alexander Zhdanov alexander.zhdanov@ispras.ru http://www.aac-lab.com http://www.ispras.ru /~zhdanov