1. SOUTHERN TAIWAN UNIVERSITY ELECTRICAL ENGINEERING DEPARTMENT
Gain Scheduler Middleware: A Methodology to
Enable Existing Controllers for Networked Control
and Teleoperation—Part I: Networked Control
Professor: Student:
Dr. Chi-Jo Wang Edith-Alisa Putanu, 普愛麗
M972B205
Authors: Yodyium Tipsuwan, Mo-Yuen Chow
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL 51, NO 6, DEC 2004

2. Outline Abstract Introduction System Description
Case Study: GSM for PI DC Motor Speed Controller
Simulation Results
Conclusions

3. I. Abstract
Control over a network implies the need of an algorithm to compensate network delays effects
Usually the existing controller has to be replaced, which is costly, inconvenient and time consuming
A novel methodology is proposed to enable existing controllers for networked control
A gain scheduling algorithm modifies the output of the controller with respect to the current network traffic conditions

4. II. Introduction
Rapid advancement in communication networks, especially Internet and therefore, in control applications such as teleoperation or remote mobile robots
Network delays can degrade performance and even make systems become unstable
Middleware is a implementation that links applications or function calls together
In the proposed methodology the middleware modifies the controller output based on gain scheduling

5. III. System Description
External Gain Scheduling
System dynamics of a remote system to be controlled:
Controller rule:
- state variable of the remote system
- remote system output
- controller output
- remote systems parameters
- controller parameters
- a variable gain used to adjust
- network variable representing network traffic conditions

6. III. System Description
External Gain Scheduling
A method to compensate network delay effects is to adapt externally by finding
A relation between and is complicated to find
We will obtain simulation or experimental data, then apply a lookup table
B. Gain Scheduler Middleware (GSM)
Basic Components:
Network Traffic Estimator
Feedback Preprocessor
Gain Scheduler

7. III. System Description
B. Gain Scheduler Middleware (GSM)
Network Traffic Estimator – monitors and estimates current network traffic conditions q, used by feedback preprocessor or/and gain scheduler
Feedback Preprocessor – preprocesses data such as motor speed and current (filters noises, predicts remote system states).
Gain Scheduler – modifies the controller output, with respect to current network conditions, q

8. IV. Case Study: PI DC Motor Speed Controller
Problem Formulation
Continuous time approach, first assuming IP network delays constant.

9. IV. Case Study: PI DC Motor Speed Controller
Assumptions regarding the PI controller, with step response:
percentage overshoot (P.O.): P.O %
settling time (ts): ts s
rise time (tr): tr s
Using root locus design approach, without considering network delays, feasible choice of
B. DC Motor Model
The dc motor transfer function used:

10. IV. Case Study: PI DC Motor Speed Controller
C. Parameterization for Gain Scheduling: Constant Network Delay
In order to evaluate the best system performance with respect to under different IP network conditions, the next cost function has to be minimized:
MSE0 – nominal mean-squared error
P.O.0 – nominal percentage overshoot
tr0 – nominal rise time
e(k) = y(k) – r(k)

11. IV. Case Study: PI DC Motor Speed Controller
C. Parameterization for Gain Scheduling: Constant Network Delay
With network delays may no longer be optimal
A feasible set of is estimated by the root locus analysis

12. IV. Case Study: PI DC Motor Speed Controller
C. Parameterization for Gain Scheduling: Constant Network Delay
Result: a longer delay gives a smaller feasible set of
Optimal for a specific delay will be found by iteratively running simulations with various in the feasible region, and comparing the cost of J

13. IV. Case Study: PI DC Motor Speed Controller
D. Parameterization for Gain Scheduling: Actual IP Network Delay
Actual IP network delays are not constant, but stochastic and not necessarily continuous in nature
Round Trip Time (RTT) are measured from an Ethernet network in Advance Diagnosis And Control (ADAC) Laboratory for 24h as follows:

14. IV. Case Study: PI DC Motor Speed Controller
D. Parameterization for Gain Scheduling: Actual IP Network Delay

15. IV. Case Study: PI DC Motor Speed Controller
D. Parameterization for Gain Scheduling: Actual IP Network Delay
The histograms skew to the left, indicating also probability
To investigate how the stochastic behavior affects the optimality of , RTT is modeled by the generalized exponential distribution
- median of RTT delays
The controller used in the real IP network environment has to be a discrete-time PI controller
The optimal has to be established again for the discrete PI controller, but it can be searched in the same feasible set as in continuous time
The sampling time is defined T = 1 ms, so that the behavior is close to the one in continuous time

16. IV. Case Study: PI DC Motor Speed Controller
D. Parameterization for Gain Scheduling: Actual IP Network Delay

17. V. Simulation Results
The performance of the proposed GSM is verified by simulations in Matlab/ Simulink 6.1
Environment:
steady state reference value c=1
final simulation time 10s
sampling time of the PI controller, GSM and plant T=1ms
number of packets to evaluate the characteristic of RTT delays N=100
Three scenarios are simulated, and the following costs J are obtained:

18. V. Simulation Results

19. VI. Conclusions
The paper proposed the concept of external gain scheduling via the GSM
The GSM changes the controller output with respect to the current network traffic conditions
The PI control system is initially formulated with constant network delays, approximated by rational function
The concept is extended for actual IP delays based on RTT measurements and the generalized exponential distribution model
Under reasonably long random IP delays, the GSM can adapt the controller gain suitably and maintain the system performance in a satisfactory level

20. Thank You for Your Attention!