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 must survive 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 Model State Estimate Action 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 engines Helium tank Pyro 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 engines Helium tank Pyro valves Fuel 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 engines Helium tank Pyro valves Fuel 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 specifies abstract state evolutions Model Temporal planner Model-based Executive Command goals Observations Flight System Control Control Layer State Thrust Goals Attitude Point(a) Engine Off Delta_V(direction=b, magnitude=200) Power Model-based Program Evolves Hidden StateClosed Valve Open Stuckopen Stuckclosed OpenClose inflow = outflow = 0 Programmer specifies plant model Model specifies Mode transitions Mode behavior

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

Model Temporal planner Model-based Executive Command goals Observations Flight System Control Control Layer State Thrust Goals Attitude Point(a) Engine Off Delta_V(direction=b, magnitude=200) Power Model-based Executive Reasons from Plant Model State Estimates Reconfigure & Repair Estimate & Diagnose State Goals 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 1996 Launch: October 15th, 1998 Remote Agent Experiment: May, 1999

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

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

State estimate Mode Selection Mode Estimation CommandObservations Model Flight System Control RT Control Layer State goals 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 P t (m’) s.t. M(m’) ^ O(m’) is consistent P – probability, M – modes, O - observations arg min C t (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