Holistic Control for Wireless Control Systems Yehan Ma CSE 521S
Industrial Process Automation Ø Large Scale Ø Challenging environment q Physical plant Ø Relative low speed q Network communication Ø Stability is critical q Health, Safety, and Environment (HSE) Ø Automation à Industrial 4.0 Courtesy: Emerson Process Management 1
Cyber-Physical Dependability Most of today’s industrial wireless networks are for monitoring Actuator valve va ve control Dependable control requires command • control performance • resiliency under interference • energy efficiency of wireless network pressure pr sensor data Controller Sensor Wireless Interference Physical Disturbance Reference u t ( ) u t ˆ ( ) Controller Actuators Plant ˆ ( ) x t y t ˆ ( ) State y t ( ) Sensors Observer 2
Control Backgrounds Ø Traditional controller design: PID, optimal control law q Guarantee control performance • Stability Stability index: Lyapunov function 𝑊 𝑦(𝑢) 𝑊 𝑦(𝑢) ≥ 0, 𝑊̇ 𝑦(𝑢) ≤ 0 à System is stable • Regulation error ref ref 𝑦(𝑢) 6 7 Regulation error index: Integral Absolute Error 𝐽𝐵𝐹 = ∫ |𝑠𝑓𝑔 − 𝑦(𝑢)|𝑒𝑢 8 3
Current Industrial Control Ø Wireless: use redundancy to reduce data loss Ø Control: tolerate data loss and physical disturbances Ø Operate in isolation! Network Manager Performance Reconfiguration Measurements Signals Wireless Sensor Network Wireless Interference Physical Disturbance 4
Holistic Control Ø Close the loop between control and network Ø Holistic controller manages both the physical plant and network configurations based on states of the plant and the network Ø Improve control performance while reducing energy cost in spite of cyber and physical interference Network Configuration Network Network Network Network Holistic Holistic Holistic Controller Manager Manager Manager Manager Controller Controller Controller Network State Network State Plant Plant Plant Control Control Reconfiguration Reconfiguration Reconfiguration Reconfiguration Acks Acks Acks Acks Inputs Inputs Outputs Outputs Outputs Signals Signals Signals Signals Wireless Wireless Wireless Wireless Sensor Sensor Sensor Sensor Plant Plant Plant Plant Network Network Network Network 5
Holistic Control Framework Wireless Interference Physical Disturbance Network Reconfiguration Signal R or Tn 1 2 Controller Actuators Holistic Controller Actuators Actuators u ˆ t Controller u t ˆ t x Physical State 3 Physical State 4 Sensors Plant Physical State Sensors Plant Observer Sensors Plant Observer ˆ t y Observer y t Ø Holistic controllers q monitor control performance q compute (1) network configurations and (2) control commands Ø Network q transmits control commands q reconfigures itself when needed Ø Wireless control systems with enhanced resiliency and efficiency! 6 12/3/19
Holistic Control Strategies Ø Reconfigure wireless networks in response to system states q Adapting number of transmissions q Adapting sampling rates q Self-triggered control q Adapting transmission schedules Y. Ma, D. Gunatilaka, B. Li, H. Gonzalez and C. Lu, Holistic Cyber-Physical Management for Dependable Wireless Control Systems. ACM Transactions on Cyber-Physical Systems, 3(1), Article No. 3, 2018. Y. Ma, C. Lu and Y. Wang, Efficient Holistic Control: Self-Awareness across Controllers and Wireless Networks,ACM Transactions on Cyber-Physical Systems, Special Issue on Self-Awareness in Resource Constrained Cyber-Physical Systems, accepted. Y. Ma, J. Guo, Y. Wang, A. Chakrabarty, H. Ahn, P . Orlik and C. Lu, Optimal Dynamic Scheduling of Wireless Networked Control Systems, ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS'19), April 2019. 7
(1) Adapting #Tx Wireless Interference Physical Disturbance #Tx R or Tn 1 2 Controller Actuators Holistic Controller Actuators Actuators u ˆ t Controller u t ˆ t x Physical State 3 Physical State 4 Sensors Plant Physical State Sensors Plant Observer Sensors Plant Observer ˆ t y Observer y t Ø Adjust the number of transmission ( #Tx ) 1 0.8 0.6 1 T x PDR 2 T x 0.4 3 T x 4 T x 0.2 5 T x 6 T x 0 − 84 − 82 − 80 − 78 − 77 − 76 − 75 − 74 − 73 − 72 Noise Strength (dBm) 8
Measure Control Performance Ø Monitor control performance based on Lyapunov function 𝑊(𝒚 : ) Ø Trend of 𝑊(𝒚 : ) à system stability Ø Value of 𝑊(𝒚 : ) à upper bound of physical states errors || 𝒚 : − 𝒚 𝒔 || ( ) t t V x x t t 9
#Tx Adaptation Algorithm 𝑊(𝑢) #Tx ↑ Increase threshold Decrease threshold #Tx ↓ 𝑢 Ø Simplified rate adaptation algorithm If Increase threshold → #Tx ↑ If Decrease threshold for a time interval → #Tx ↓ 10 12/3/19
Network Reconfiguration Ø Asymmetric scheduling q 1 Tx for sensing flows q adapt #Tx for actuation flows Ø Piggyback mechanism q Piggyback #Tx in each packet sent to actuators q Each node checks #Tx and switches schedule in next period 11
Network Reconfiguration Cont. D C F1:A à B à C #TX = 3 #TX = 1 F2:A à B à D #TX = 3 B #TX = 1 #TX = 2 #TX = 3 #TX = 1 #TX = 2 A 12
Wireless Cyber-Physical Simulator Ø Realistic and holistic simulation environment for wireless control systems (open source: http://wcps.cse.wustl.edu) q Integrate TOSSIM and Simulink Simulink Reference Controller Command Data Data with SS/UDS decisions Data Block Plant model Command Data Sensor Data after delay and loss Sensor Data after delay and loss Packet collector Cross-platform User Inputs function call Message pool Interfacing Block Return values of function call Sensor and Command Data TOSSIM Python interface Network Manager Wireless Network Routing Routing Routing layer Scheduling Scheduling TDMA MAC layer Wireless Signal RSSI Wireless link model Wireless Noise Noise 13
Wireless Cyber-Physical Simulator Ø Realistic and holistic simulation environment for wireless control systems. q Incorporate WirelessHART network protocol stack q Provide Dockerized (container-based) installation q Incorporate holistic control mechanisms • run-time network adaptation • simulate communicational and computational latency 14
Experimental Settings Ø Physical plant: 5-state linear time-invariant plant model Ø Wireless network: 16-node WirelessHART network Ø Network interference: noise in wireless channels Ø Physical disturbance: sensor bias 15
Performance under cyber interference 2 2 Physical Physical states states (a) 0 (a) 0 − 2 − 2 3 3 10 10 Lyapunov Lyapunov function function 0 (b) 0 (b) 10 10 − 3 − 3 10 10 Wireless − 75 Wireless − 75 noise noise (c) (c) − 78 − 78 4 4 3 #TX 3 #T x (d) (d) 2 2 1 1 1 1 Actuation Actuation PDR PDR (e) (e) 0.5 0.5 0 0 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 Time (s) Time (s) 16
Performance under interferences 2 1 2 TX 3 TX 0.8 4 TX Mean Absolute Error 1.5 Mean Absolute Error Pure Network Adaptation Holistic Management 0.6 1 0.4 0.5 0.2 0 0 140 160 180 200 220 140 150 160 170 180 190 200 System Lifetime under network interference (Day) System Lifetime under physical disturbance (Day) Holistic control closes the loop between network and control à Ø prolong network lifetime Ø maintain resilience to both cyber and physical interferences 17
(2) Rate Adaptation Wireless Interference Physical Disturbance Sampling Rate R or Tn 1 2 Controller Actuators Holistic Controller Actuators Actuators u ˆ t Controller u t ˆ t x Physical State 3 Physical State 4 Sensors Plant Physical State Sensors Plant Observer Sensors Plant Observer ˆ t y Observer y t 𝑊(𝑢) Sampling rate ↑ Increase threshold Decrease threshold Sampling rate ↓ 𝑢 18 12/3/19
(3) Self-triggered Control Ø Event trigger rule q Stability index is specified by: 𝑇(𝑢) 𝑇(𝑢) 𝑊(𝑢) q Trigger when 𝑊 𝑢 ≥ 𝑇 𝑢 Ø Self triggered control 𝑢 q Predict when the trigger condition will be violated based on model à Inter-transmission time Wireless Interference Physical Disturbance Inter-transmission Time R or Tn 1 2 Controller Actuators Holistic Controller Actuators Actuators u ˆ t Controller u t ˆ t x Physical State 3 Physical State 4 Sensors Plant Physical State Sensors Plant Observer Sensors Plant Observer ˆ t y Observer y t 19 12/3/19
Network Design Ø Glossy flooding q One to many q Fast (propagation delay < 10 ms in 100-node mesh network) Ferrari, F., et. al. Efficient network flooding and time synchronization with glossy. In IPSN , 2011. 20 12/3/19
Glossy flooding Ø One to many Ø Constructive interference + = 𝑢 𝑢 𝑢 Ø Radio event driven Ø Fast (propagation delay < 10 ms in 100-node mesh network) 21 12/3/19
Network Reconfiguration Ø Glossy flooding q One to many q Fast (propagation delay < 10 ms in 100-node mesh network) Ø Low-power Wireless Bus (LWB) network protocol q Maps all communication on fast Glossy floods à many to many Ferrari, F., et. al. Efficient network flooding and time synchronization with glossy. In IPSN , 2011. Ferrari, F., et. al Low-power wireless bus. In Sensys , 2012. Ø Advantages of LWB q Fast q T opology independent q Suitable for network-wide adaptation 22 12/3/19
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