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ONR N00014-97-1-0946

Exploration of Hybrid and Intelligent Control Architectures in Conjunction with Probabilistic Verification (ONR N00014-97-1-0946)

S. Shankar Sastry

ONR Project Review, July 21, 1998

Electronics Research Laboratory

University of California, Berkeley

Overview of ONR UCAV Project

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ONR N00014-97-1-0946

Research Objective and Research Thrusts

Research Objective: Design and evaluation of intelligent control architectures for UCAV’s

Research Thrusts:

Intelligent control architectures for coordinating UCAV’s

Verification and design tools for intelligent control architectures

Perception and action hierarchies for vision-based control and navigation

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ONR N00014-97-1-0946

Project Concept

Central control paradigm (optimization of system-wide mission objectives) breaks down when dealing with distributed multi-agent systems

Real-world environments are complex, spatially extended, dynamic, stochastic and largely unknown

Autonomous intelligent systems in real-world environments require

– sensory and control functions• based on system decomposition based on hierarchical hybrid, and multi-

agent designs using multiple levels of abstraction

– structural and parametric learning methods• to adapt to initially unknown environments

– generalized estimation methods, uncertainty management and robust control techniques

• to deal with residual uncertainty in stochastic, partially observable environments

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ONR N00014-97-1-0946

Project Concept

Real-time decision making is achieved by– parallelism– reflexive control– compilation– anytime approximation

Hierarchical perception and control paradigm– architectural fusion of the central control paradigm with

autonomous intelligent systems Distributed intelligent systems

– hierarchical or modular to control complexity– globally organized emergent behavior– robust, adaptive and fault tolerant and degraded modes of

operation– architectural organization involving the use of compositionality

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Technology Drivers

Intelligent Multi-Agent Systems

The need for a theoretical framework for an integrative approach arises from advances in computation, communication, intelligent materials, visualization and other technologies which make it possible to expect more from a multi-agent system, than a centralized control framework.

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Intelligent Multi-Agent Systems

Unmmaned Autonomous Vehicles Distributed Command and Control Simulated Battlefield Environment Decision Support Aids for Human Centered Systems Automatic Target Recognition Robust and Fault tolerant Systems Distributed Communication Systems Distributed Power systems Intelligent Vehicle Highway systems Air Traffic Management Systems Intelligent Telemedical Systems

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ONR N00014-97-1-0946

Berkeley Team

SASTRY: Specializes in decentralized control of distributed systems and hybrid design and verification techniques, with applications to automated highway systems, air traffic management systems and robotics.

MALIK: A leader in low-level and intermediate vision, with recent work in crucial aspects of image segmentation, association, grouping and attribute evaluation.

SENGUPTA: Experience in observation and control for distributed systems. Extensive background in discrete event and hybrid systems. Application to transportation and communication problems.

GODBOLE: Hybrid control of multi-agent systems. Extensive background in applications to automated highway and air traffic management systems. Inter-agent coordination problems. Design of fault management systems.

LYGEROS. Hybrid control synthesis. Background in automated highway and air traffic management systems. Formal methods for verification of large-scale systems. Fault tolerant control systems.

SHAKERNIA. First-year graduate student in EECS. B.S. (1997) EECS, UCB.

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ONR N00014-97-1-0946

Theoretical Underpinnings

Architectural design for multi-agent systems– centralization for optimality– decentralization for safety, reliability and speed of response

Perception systems sharing many representations– hierarchical aggregation– wide-area surveillance– low-level perception

Frameworks for representing and reasoning with uncertainty Incorporation of learning, adaptation and fault toperance: parametric

uncertainty with update and adaptation at the continuous levels, learning of new “logical entities”, reinforcement learning at the logical levels and meta-learning for redefining architecture

Soft-computing approaches to intelligence augmentation for human-centered systems: reconciliation of human decision making schemes with machine performance, intelligent agents, keeping the human in the loop, sufficing rather than optimizing

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ONR N00014-97-1-0946

Research Thrust 1: Intelligent Control Architectures

An architecture design problem is concerned with design of both the observation and control

An architecture design problem for a distributed system begins with specified safety and efficiency objectives and aims to characterize communication, observation and control

Our investigation of the intelligent control architecture design problem is concerned with three formalisms– intrinsic model– supervisory control of discrete event systems– hybrid system formalism

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ONR N00014-97-1-0946

Research Thrust 2: Verification and Design Tools

Design Mode Verification Faulted Mode Verification Probabilistic Verification

– A rapproachment between stochastic control, Bayesian decision networks and soft computing

– The heart of the approach is to not verify that every run of the hybrid system satisfies certain safety or liveness parameters, rather to check that the properties are satisfied with a certain probabillity, given uncertainties of actuation and sensing

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Research Thrust 3: Perception and Action Hierarchies

Hierarchical Vision

Control Around the Vision Sensor

Surveillance

We are designing a perception and action hierarchy centered around the vision sensor to support the observation and control functions of air vehicles

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ONR N00014-97-1-0946

Exploration of Hybrid and Intelligent Control Architectures in Conjunction with Probabilistic Verification (ONR N00014-97-1-0946)

Architectures for UCAV and Results on Multi-Agent Coordination

Raja Sengupta and Datta Godbole

ONR Project Review, February 24-25, 1998

Electronics Research Laboratory

University of California, Berkeley

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ONR N00014-97-1-0946

Outline

Research Thrusts

– Hierarchical UCAV Architecture Design

– Design of Decentralized Observation, Communication and Control for Discrete Event Systems

• Application to Fault Detection and Identification

– Distributed hybrid control for multi-agent systems

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ONR N00014-97-1-0946

UCAV Architecture

A group of UCAV’s are a coordinating multi-agent system with limited centralized control– mobile communication systems support coordination– due to limitations on computing and communication

resources the UCAV’s must cooperatively and individually exhibit a high degree of autonomy

– low bandwidth, asynchronous, event-driven coordination is preferred to synchronous, time-driven coordination

A single UCAV is a real-time embedded system The architecture should specify observation and control

semantics at each layer– the layers should be consistent and programmable

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ONR N00014-97-1-0946

UCAV Architecture

Strategic Layer

Mission Control

Tactical Layer

Regulation Layer

Strategic ObjectiveShip

UCAV

Inter-UCAV Coordination

Trajectory Constraints

Sensor Info on Targets, UCAV’s

Environmental Sensors

Trajectory

Actuator Commands

UCAV Dynamics

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ONR N00014-97-1-0946

Preliminary UCAV Architecture

Regulation– control of UCAV actuators

• fully autonomous

Tactical– safe and efficient trajectory generation and mode control

• fully autonomous

Strategic– trajectory constraints, UCAV to UCAV/ship coordination,

weapons configuration, fault management Central Mission Control

– mission planning, resource allocation, mission emergency response, mission optimization, pilot interface

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ONR N00014-97-1-0946

PATH AHS Architecture

Network Layer

Link Layer Regulator(flow control)

Roadside Vehicle

Coordination Layer(maneuver protocols)

Regulation Controller

Throttle Actuator Steering Actuator Brake Actuator

Vehicle Dynamics (Plant)

Physical LayerSC&P* Info

*Sensory, Capability & Performance

SC&P* Info

SC&P* Info

Communicationswith neighbors

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Example: AHS Architecture

The layers are consistent and programmable

Coordination

– Synchronous, time-driven for platoon stability (regulation)

– Asynchronous, event-driven for maneuvers (coordination)

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ONR N00014-97-1-0946

Decentralized Observation, Communication, Control for Multi-agent Systems

Given a strategic objective and local observation what is the information exchange with the mothership and other UCAV’s required to command tactical control ?– Given a distributed control problem and the local

observation at each site, what is the inter-site communication (minimal) or coordination protocols required to solve this problem ?

Given a system mission what is the strategic objective (possibly dynamic) of each UCAV ?– How to distribute among the available agents a specified

centralized control problem ?

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Literature: Distributed Control with Decentralized Information

Decentralized control of large-scale systems– linear systems, time-driven, design for stability

Stochastic scheduling– queuing networks, time-driven, design for performance

Distributed control of discrete event systems– event driven, design for correctness, safety

Distributed control of hybrid systems– time and event-driven, design for correctness, safety

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ONR N00014-97-1-0946

Communication and Control Synthesis for DES

Symbolic representation of system actions (events)

Behavior is a causal ordering of symbols (event trace)

Objective: – Given a DES plant model, specification of the control

objective, local observation and control capability, synthesize a minimal inter-agent communication and the control law of each agent.

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ONR N00014-97-1-0946

Communication and Control Synthesis for DES

Advantages:– Will synthesize symbolic, event-driven, inter-agent

communication over a finite message set – Very simple models permitting logical or combinatorial

analysis and insights– AHS Example: Worked for most coordinating maneuvers

other than stability properties for vehicle following. Limitation: No formal way to capture continuous dynamics

– The semantics of an event is generally some alignment or safety conditions in velocity, position, and euler angles with respect to targets or other agents

– Distributed control of hybrid systems

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ONR N00014-97-1-0946

DES Problem 1: Observation and Communication

Agent Communication Channels

A1 A2 A3

Plant (Lp)

• The agents have partial observation, but can exchange messages • The plant has a set of distinguished events (failures) • OBJECTIVE: Design the inter-agent communication scheme required to detect and isolate the distinguished events

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ONR N00014-97-1-0946

DES Problem 1: Observation and Communication Only

Theorem 1 (Lp, poi, fii) is decentrally diagnosable if there exists

n N such that for all f f, ufv Lp, vn, implies

(w Lp) ( i, Ppoi (w) = Ppoi(ufv(f w

If any two sufficiently long plant traces look the same to all the diagnosers, then either they have no failures or have all the same failures.

Synthesis: The communicate all plant observations solution works.

General drawback: Redundant information is communicated. -L() may not be regular even though Lp is regular.

Current focus: Minimal communication, protocol synthesis, trace abstraction

Documentation: Draft paper available and sent to WODES’98

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DES Problem 2: Control, Observation, and Communication

Each agent has a set of controllable events The controllable events are a subset of the set of

observable events The next event is either an uncontrollable event from the

plant, a controllable event enabled by an agent, or a message event scheduled by an agent

Control objective is specified by a language Researching the existence and synthesis of coordination

protocols

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ONR N00014-97-1-0946

Distributed Control and Communication of Hybrid Systems

Symbolic and flow representation of system actions Game/Team-theoretic approach to synthesis

– Agents play as a team against a non-cooperative target• Characterize the saddle disturbance in the team-target game

• Use the saddle disturbance to formulate and solve an optimal control problem to characterize the saddle team strategy

• Derive the inter-agent communication and individual agent control from the necessary conditions

Theorem: If for an initial state the worst disturbance is independent of the team control then the target-team game has a saddle solution

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ONR N00014-97-1-0946

Hybrid Approach: Application to AHS Lane Change

FT generates disturbance in response to downstream traffic and P,RT play as a team

We guess the saddle disturbance Use the saddle disturbance to formulate and solve an

optimal control problem for the saddle team control The inter-agent coordination requires three messages

P

FTRT

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ONR N00014-97-1-0946

Summary

Developed a preliminary UCAV architecture– UCAV to UCAV and UCAV to ship coordination– Hierarchical control and observation

Focused theoretical research efforts on – Sensing, Control, and Communication for Distributed

Multi-agent Systems– Failure Detection and Identification– Design of Hybrid and Decentralized Control Systems– Current Results

• Existence and synthesis of inter-agent communications for partially observed distributed discrete event systems

• Synthesis of safe hybrid control laws for distributed hybrid systems using game theory and optimal control

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Quick Time Movie of Helicopter Flight