MBD in real-world system… Self-Configuring Systems

1. MBD in real-world system…Self-Configuring Systems Meir Kalech Partially based on slides of Brian Williams

2. Outline • Last lecture: • Models of correct + faulty behavior • Sherlock engine • Abductive diagnosis • Qualitative models • Today’s lecture: • Autonomous systems • Model-based programming • Livingstone

3. 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?

4. 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

5. Configuration Goals Action Selection State Estimate Model Observations Command Problem Statement • 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

6. 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

7. Spacecraft Engine System Model Acc latch valves Fuel tank Regulators Pyro valves Helium tank main engines oxidizer tank • Helium pressurizes the fuel and oxidizer tanks with the regulators which control the high pressure. • Acc senses the thrust generated by the engines.

8. latch valves Acc Fuel tank Regulators Pyro valves main engines Helium tank oxidizer tank Spacecraft Engine System Model • High level goal: producing thrust • Several configurations: • Open latch valves in the left engine. • Firing pyro valves and open a set of latch valves to the right engine. • More configurations of valves states…

9. latch valves Acc Fuel tank Regulators Pyro valves main engines Helium tank oxidizer tank Spacecraft Engine System Model • Suppose configuration 1 is selected. • Configuration 1 failed – not enough thrust. • Find lowest cost new configuration that satisfies goals.

10. Outline • Last lecture: • Models of correct + faulty behavior • Sherlock engine • Abductive diagnosis • Qualitative models • Today’s lecture: • Autonomous systems • Model-based programming • Livingstone

11. Model-based Program Evolves Hidden State Thrust Goals Delta_V(direction=b, magnitude=200) Valve Power Stuck open 0.01 Open 0. 01 Point(a) Attitude Open Close 0. 01 Stuck closed Off Engine Off Closed 0.01 inflow = outflow = 0 Model Flight System Control Control Layer Programmer specifiesabstract state evolutions Temporal planner Programmer specifies plant model • Model specifies • Mode transitions • Mode behavior State goals Model-based Executive Observations Command

12. Model-based Executive Reasons from Plant Model Thrust Goals Delta_V(direction=b, magnitude=200) Open four valves Power State Estimates State Goals Point(a) Attitude Estimate & Diagnose Reconfigure & Repair Off Engine Off Engine Off Observations Commands Model Flight System Control Control Layer Goal: Achieve Thrust Temporal planner State Estimates State Goals Model-based Executive Observations Commands

13. Model-based Executive Reasons from Plant Model State Estimates State Goals Thrust Goals Delta_V(direction=b, magnitude=200) Estimate & Diagnose Reconfigure & Repair Power Point(a) Attitude Off Engine Off Diagnose: Valve fails stuck closed Switch to backup Model Flight System Control Control Layer Goal: Achieve Thrust Temporal planner State goals Model-based Executive Observations Command

14. Outline • Last lecture: • Models of correct + faulty behavior • Sherlock engine • Abductive diagnosis • Qualitative models • Today’s lecture: • Autonomous systems • Model-based programming • Livingstone

15. A simple model-based executive (Livingstone) commanded NASA’s Deep Space One probe Started: January 1996 Launch: October 15th, 1998 Remote Agent Experiment: May, 1999 courtesy NASA JPL

16. Livingstone [Williams & Nayak, AAAI96] State goals State estimate Model Mode Estimation Mode Reconfiguration Flight System Control Command Control Layer Observations

17. Thrust Reconfigure modes to meet goals Estimate current likely Modes State goals State estimate Model Mode Estimation Mode Selection Observations Command Flight System Control RT Control Layer

18. Mode Estimation: Select a most likely set of component mode transitions that are consistent with the model and observations Mode Selection: Select a least cost set of allowed component modes that entail the current goal, and are consistent State goals State estimate Model Mode Estimation Mode Selection 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 Observations Command Flight System Control RT Control Layer

19. Current Demonstration Testbeds • Air Force Tech Sat 21 flight • NASA NMP ST-7 Phase A • NASA Mercury Messengeron ground. • MIT Spheres on Space Station • NASA Robonaut, X-37, ISPP • Multi-Rover Testbed • Simulated Air Vehicles