Some results on optimal estimation and control for lossy NCS. Luca Schenato

Transcription

1 Some results on optimal estimation and control for lossy NCS Luca Schenato

2 Networked Control Systems Drive-by-wire systems Swarm robotics Smart structures: adaptive space telescope Wireless Sensor Networks Traffic Control: Internet and transportation Smart materials: sheets of MEMS sensors and actuators NCSs: physically distributed dynamical systems interconnected by a communication network

3 NCSs: what s new for control? Classical architecture: Centralized structure Actuators Plant Sensors Controller

4 NCSs: what s new for control? NCSs: Large scale distributed structure A A Plant S S A S Interference Packet loss Connectivity COMMUNICATION Random delay Limited capacity NETWORK Quantization Congestion C C C C C

5 Interdisciplinary research needed COMMUNICATIONS ENGINEERING Comm. protocols for RT apps Packet loss and random delay Wireless Sensor Networks Bit rate and Inf. Theory NETWORKED CONTROL SYSTEMS COMPUTER SCIENCE Graph theory Distributed computation Complexity theory Consensus algorithms SOFTWARE ENGINEERING Embedded software design Middleware for NCS RT Operating Systems Layering abstraction for interoperability

6 Interdisciplinary research needed COMMUNICATIONS ENGINEERING Comm. protocols for RT apps Packet loss and random delay Wireless Sensor Networks Bit rate and Inf. Theory NETWORKED CONTROL SYSTEMS COMPUTER SCIENCE Graph theory Distributed computation Complexity theory Consensus algorithms SOFTWARE ENGINEERING Embedded software design Middleware for NCS RT Operating Systems Layering abstraction for interoperability Martedi prossimo Average TimeSync (ATS): a distributed consensus protocol for sensor networks clock synchronization

7 Sensor nodes w/ motion sensors NCS example: Pursuit Evasion Games w Sensor Networks Information flow from SN Pursuers Evaders

8 Motivating example: wireless sensor networks Forest Temperature Monitoring (data-extraction application) Wildfire detection & tracking (real-time application) sensor node BASE STATION Packet loss Event-triggered routing real-time apps TDMA-based routing data-extraction apps Packet delay Can we design optimal estimators that compensate for random delay and packet loss? What is the performance if we have packet arrival statistics? How can we compare different communication/routing protocols in terms of estimation performance?

9 Optimal LQG Actuators Plant Sensors controller Sensors and actuators are co-located, i.e. no delay nor loss

10 Optimal LQG Actuators Plant Sensors LQ State feedback Static Kalman filter 1. Separation principle holds: Optimal controller = Optimal estimator design + Optimal state feedback design 2. Closed Loop system always stable (under standard cont/obs. hypotheses) 3. Gains K,L are constant solution of Algebraic Riccati Equations

11 Optimal LQG control over DCN Random delay or drop Actuators Plant Sensors Controller? ACK? Controller? DIGITAL COMMUNICATION NETWORK Controller Random delay or drop

12 Some consideration on the separation principle Actuators Plant Sensors Random delay Packet loss State feedback z -1 Kalman filter x x

13 Modeling of Digital Communication Network (DCN) Analog signal Sampling Quantization DSP Data (N bits) Encoder sent packet Digital Digital Communication Communication Network Network arrived packet Decoder CRC redundancy delay data packet header ATM Ethernet Bluetooth Zigbee 384 bits 40 bits >368 bits >499 bits 112 bits ~100 bits <1000 bits 128 bits Assumptions: (1) Quantization noise<<sensor noise (2) Packet-rate limited ( bit-rate) (3) No transmission noise (data corrupted=dropped packet) (4) Packets are time-stamped Random delay & Packet loss (=1) at receiver

14 Estimation modeling PLANT Digital Digital Communication Communication Network Network Buffer ESTIMATOR No packet arrives Packet out of order Multiple packets arrive

15 Minimum variance estimation PLANT Digital Digital Communication Communication Network Network Buffer ESTIMATOR Kalman time-varying linear system

16 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

17 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

18 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

19 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

20 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

21 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

22 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

23 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

24 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

25 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

26 Properties of Optimal Estimator ESTIMATOR Optimal for any arrival process Stochastic time-varying gain K t =K(γ 1,..,γ t ) Possibly infinite memory buffer Inversion of up to t matrices at any time t ESTIMATOR N

27 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

28 Minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

29 What about stability and performance? Additional assumption on arrival sequence necessary: i.i.d. arrival with stationary distribution

30 Optimal estimation with constant gains and buffer finite memory ESTIMATOR N Does not require any matrix inversion Simple to implement Upper bound for optimal estimator: N is design parameter GOAL: compute

31 Suboptimal minimum variance estimation Open loop Closed loop

32 Suboptimal minimum variance estimation Lyapunov Equation (unstable) Riccati Equation (stable)

33 (off-line computation) Steady state estimation error Fixed gains: Optimal fixed gains: Modified Algebraic Riccati Equation (MARE) ( 1 (P)=ARE)

34 Numerical example (I) Discrete time linearized inverted pendulum: Expected covariance error P

35 Numerical example (II) Time-varying arrival probability distribution arrival probability delay (h) λ 1 λ 2

36 Back to the control problem Actuators Plant Sensors State feedback z -1 Static Kalman filter

37 Back to the control problem Actuators Plant Sensors Random delay Packet loss Random delay Packet loss State feedback z -1 Time-varying Kalman filter w/ memory Estimation error coupled with control action no separation principle

38 LQG over TCP-like (ACK-based) protocols Actuators Plant Sensors Packet loss State feedback z -1 z -1 Time-varying Kalman filter w/ memory Random delay Packet loss Separation principle hold (I know exactly u a t-1) t Bernoulli rand. var and independent of observation arrival process Static state feedback, L solution of dual MARE

39 LQG over UDP-like (no-ack) protocols Actuators Plant Sensors Packet loss Random delay Packet loss LQG problem still well defined: No separation principle hold ( u a t-1 NOT known exactly) but still have some statistical information about u a t-1

40 LQG over UDP-like (no-ack) protocols Actuators Plant Sensors Packet loss Packet loss State feedback z -1 Static Kalman filter Bernoulli arrival process Sub-optimal controller forced to be state estimator+state feedback Optimal choice of K,L is unique solution of 4 coupled Riccati-like equations Compensability and Optimal Compensation of systems with white parameters, De Koning, TAC 92

41 L LQG as optimization problem Non convex problem even for ==1, i.e. classic LQG Classic and TCP-based LQG become convex when exploiting optimality conditions like uncorralation between estimate and error estimate For UDP-like problem non convex but unique solution using Homotopy and Degree Theory (DeKoning,Athans,Bernstain) (maybe using Sum-of-Squares?) Stability on and is coupled K

42 Side note: Kalman filter is not always optimal! Optimal Regulator Kalman filter K klm LQ State feedback L LQ Kalman filter K klm Stabilizing State feedback L Filter K=K(L) Stabilizing State feedback L Kalman filter always gives smallest estimate error regardless of controller L If controller L L LQ, then performance improves if my estimate is bad!

43 Numerical example: TCP vs UDP Arrival packet probability

44 To hold or to zero control input? Actuators Plant Sensors Packet loss Controller Most common strategy: (mathematically appealing) (most natural)

45 To hold or to zero control input: no noise (jump linear systems) Zero-input Strategy Plant Hold-input Strategy Plant Controller Z -1 Controller Using cost-to-go function (dynamic programming) Riccati-like equation

46 Example: unstable scalar system A=1.2, U=0 (fastest convergence) A=1.2, U=10 (small input) Loss probability Loss probability

47 LQG over TCP-like protocols revised Actuators Plant Sensors Packet loss ACK = t z -1 Random delay Packet loss State feedback z -1 Time-varying Kalman filter w/ memory Conjecture: Separation principle hold Optimal function Design parameter obtained via LQ-like optimal state feedback

48 Smart sensors & smart actuators Actuators Plant Sensors classic LQ contoller Time-varying kalman no input packet loss classic static kalman Random delay Packet loss controller Optimal LQG control across a packet-dropping link, Gupta, Spanos, Murray, Submitted to Sys.Cont.Lett. 05 Estimation under controlled and uncontrolled communications in networked control systems, Xu, Hespanha, CDC 05

49 Numerical example: remote vs co-located controller Arrival packet probability

50 Takeaway points Input packet loss more dangerous than measurement packet loss TCP-like protocols help controller design as compared to UDP-like (but harder for communication designer) If you can, place controller near actuator If you can, send estimate rather than raw measurement Zero-input control seems to give smaller closed loop state error ( x t ) than hold-input (but higher input) Trade-off in terms of performance, buffer length, computational resources (matrix inversion) when random delay Can help comparing different communication protocols from a real-time application performance

51 Future work A A A Plant S S S COMMUNICATION NETWORK C C C C C Multiple sensors: data fusion, i.e. y 1,..,y m arrive at different times distributed estimation & consensus Multiple actuators trade-off between distributed control & centralized coordination