MBD in real-world system…

Self-Configuring Systems

Meir Kalech

Partially based on slides of Brian Williams

Outline Last lecture:

1. Models of correct + faulty behavior

2. Sherlock engine

3. Abductive diagnosis

4. Qualitative models

Today’s lecture:

1. Autonomous systems

2. Model-based programming

3. Livingstone

Motivation

Machines are increasingly aware of themselves

& environment

They are increasingly able to detect and

respond to conditions

What is the next level of awareness,

robustness, adaptivity?

NASA Research Challenges

Some machines mustsurvive years without repair

Relatively short down time can destroy a mission Development & operation costs must be contained

Challenge: Easily developed, highly capable control systems

CommandObservations

Configuration Goals

ModelState

EstimateAction

Selection

Given A model of a physical system such as a spacecraft The internal actions taken and observations

Determine The most likely internal states of the system The commands needed to move to a desirable state

Problem Statement

Typical Domain Engineers model the local, qualitative behavior of system components

Components are things like valves, switches, tanks, engines

Properties of interest are transmission of flow, voltage, etc

Goals are “produce acceleration”, “maintain pointing ability”, etc

Spacecraft Engine System Model

main

en

gin

es

Heliu

m t

an

kPyro valves

Fuel tank

oxidizer tank

latch valves

Regulators

•Helium pressurizes the fuel and oxidizer tanks with the regulators which control the high pressure.•Acc senses the thrust generated by the engines.

Acc

Spacecraft Engine System Model

High level goal: producing thrust Several configurations:1. Open latch valves in the left engine.2. Firing pyro valves and open a set of latch valves to the

right engine.3. More configurations of valves states…

main

en

gin

es

Heliu

m t

an

k

Pyro valvesFuel tank

oxidizer tank

latch valves

Regulators

Acc

Spacecraft Engine System Model

• Suppose configuration 1 is selected.

• Configuration 1 failed – not enough thrust.

• Find lowest cost new configuration that satisfies goals.

main

en

gin

es

Heliu

m t

an

k

Pyro valvesFuel tank

oxidizer tank

latch valves

Regulators

Acc

Outline Last lecture:

1. Models of correct + faulty behavior

2. Sherlock engine

3. Abductive diagnosis

4. Qualitative models

Today’s lecture:

1. Autonomous systems

2. Model-based programming

3. Livingstone

Programmer specifiesabstract state evolutions

Model

Temporal plannerTemporal planner

Model-based ExecutiveModel-based Executive

Command

goals

Observations Flight System Control

Control Layer

State

Thrust Goals

Attitude Point(a)

Engine OffOff

Delta_V(direction=b, magnitude=200)

Power

Model-based ProgramEvolves Hidden State

ClosedClosed

ValveValve

OpenOpen StuckStuckopenopen

StuckStuckclosedclosed

OpenOpen CloseClose

0. 010. 01

0. 010. 01

0.010.01

0.010.01

inflow = outflow = 0

Programmer specifies plant model

Model specifies•Mode transitions•Mode behavior

Model

Temporal plannerTemporal planner

Model-based ExecutiveModel-based Executive

Commands

State Goals

Observations Flight System Control

Control Layer

Thrust Goals

Attitude Point(a)

Engine OffOff

Delta_V(direction=b, magnitude=200)

Power

Model-based Executive Reasons from Plant Model

State Estimates

State Estimates

Reconfigure & Repair

Estimate & Diagnose

State Goals

s

Observations Commands

Goal: Achieve Thrust

Open fourvalves

Engine Off

Model

Temporal plannerTemporal planner

Model-based ExecutiveModel-based Executive

Command

goals

Observations Flight System Control

Control Layer

State

Thrust Goals

Attitude Point(a)

Engine OffOff

Delta_V(direction=b, magnitude=200)

Power

Model-based Executive Reasons from Plant Model

State Estimates

Reconfigure & Repair

Estimate & Diagnose

State Goals

s

Goal: Achieve Thrust

Diagnose:Valve fails

stuck closed Switch to

backup

Outline Last lecture:

1. Models of correct + faulty behavior

2. Sherlock engine

3. Abductive diagnosis

4. Qualitative models

Today’s lecture:

1. Autonomous systems

2. Model-based programming

3. Livingstone

A simple model-based executive (Livingstone) commanded NASA’s Deep Space One probe

courtesy NASA JPL

Started: January 1996Launch: October 15th, 1998Remote Agent Experiment: May, 1999

Livingstone [Williams & Nayak, AAAI96]

State estimate

ModeReconfiguration

ModeEstimation

CommandObservations

Model

Flight System Control

Control Layer

State goals

s

Thrust

State estimate

ModeSelection

ModeEstimation

CommandObservations

Model

Flight System Control

RT Control Layer

State goals

s

Estimate current likely Modes Reconfigure modes to meet goals

State estimate

ModeSelection

ModeEstimation

CommandObservations

Model

Flight System Control

RT Control Layer

State goals

s

Mode Selection:

Select a least cost set of allowed component modes that entail the current goal, and are consistent

Mode Estimation:

Select a most likely set of component mode transitions that are consistent with the model and observations

arg max Pt(m’)

s.t. M(m’) ^ O(m’) is consistent

P – probability, M – modes, O - observations

arg min Ct(m’)s.t. M(m’) entails G(m’)s.t. M(m’) is consistent

C – cost, G - goals

Current Demonstration Testbeds

Air Force Tech Sat 21 flight NASA NMP ST-7 Phase A NASA Mercury Messenger

on ground. MIT Spheres on Space Station NASA Robonaut, X-37, ISPP

Multi-Rover Testbed Simulated Air Vehicles