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L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK.

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Presentation on theme: "L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK."— Presentation transcript:

1 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 1 Actuator Fault Detection in Nonlinear Systems Using Neural Networks Rastko Selmic, Ph.D. Department of Electrical Engineering and Institute for Micromanufacturing Louisiana Tech University Ruston, LA 71272, USA Web:

2 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 2 Contents Introduction Problem Formulation Actuator Fault Detection, Fault Dynamics, and Fault Detectability Two cases considered: -State feedback -Output feedback Simulation Results Conclusion Other projects, ideas, etc.

3 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 3 Introduction Collaborative work with Marios Polycarpou and Thomas Parisini An actuator fault identification in unknown, input-affine, nonlinear systems using neural networks is presented Two cases are considered: state feedback and output feedback case Neural net tuning algorithms and identifier have been developed using the Lyapunov approach A rigorous detectability condition is given for actuator faults relating the actuator desired input signal and neural net- based observer sensitivity Simulation results are presented to illustrate the detectability criteria and fault detection in nonlinear systems.

4 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 4  What kind of actuator faults can be detected?  Under what conditions faults are detectable using NN identifiers?  If faults are not presently detectable, how identifier parameters need to be adjusted in order to detect the faults? Questions to be Answered

5 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 5 Problem Formulation

6 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 6 Problem Formulation

7 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 7 Case I: State Feedback

8 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 8 Figure 1. NN system observer – fault identifier. A NN System Observer

9 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 9 NN Tuning Law

10 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 10 Stability Analysis

11 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 11 The State Observer Error

12 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 12 Dynamics of a Fault

13 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 13 Detectability of Actuator Faults

14 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 14 Case II: Output Feedback

15 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 15 A NN Observer

16 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 16 A NN Observer Figure 2. NN system observer – fault identifier.

17 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 17 NN Observer Tuning Law

18 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 18 Dynamics of a Fault

19 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 19 Detectability of Actuator Faults

20 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 20 Detectability of Actuator Faults The result relates observer parameters, i.e. NN weights, with fault detectability and the actuator control signal It also shows when actuator faults can not be detected or what needs to be done with NN observer to improve sensitivity.

21 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 21 Simulation Example

22 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 22 Simulation Example

23 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 23 System state observer errors e 1 ( t ) (full line) and e 2 ( t ) (dotted line). Norm of the error e ( t ). Simulation Example

24 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 24 Simulation Example

25 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 25 Actuator fault at t =5sec; system state observer errors e 1 ( t ) (full line) and e 2 ( t ) (dotted line). Actuator fault at t =5sec; norm of the error e ( t ). Simulation Example

26 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 26 Conclusions  It is shown how neural net-based system can be used for actuator fault detection in unknown, nonlinear, input-affine systems.  Stable neural net tuning laws are given and estimate on the state observer error is provided using Lyapunov approach.  Sufficient conditions for actuator fault detectability are presented.  An open research problem is to combine active fault detection methods in case detectability conditions are not satisfied.

27 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 27 Intelligent Sensors and Actuators Group Research interests:  Wireless sensor networks for chemical agents monitoring  Suboptimal coverage control missions in mobile sensor networks.  Intelligent actuator control using neural networks  Actuators and sensors failure detection and compensation  Intelligent wireless sensor networks Group members: Dr. Rastko Selmic, 3 Ph.D. students, 4 M.S. students, and 2 undergraduate students. The group has two laboratories with several control system setups, sensors, wireless sensor nodes, two mobile robots, 11 PC computers.

28 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 28 Intelligent Sensors and Actuators Laboratory The newest lab in EE – 11 PC computers, 8 control system experimental setups, sensors, wireless sensor nodes, two mobile robots, 2 oscilloscopes.

29 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 29 Smart Actuator Control Using IEEE 1451 Standard Develop a smart actuator control that is compatible with IEEE 1451 standard for smart transducers. The concept allows for intelligent control based on data and metadata gathered by the network of smart sensors. Control action depends on sensor data and information stored in TEDS and HEDS.

30 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 30 Testbed Development – Chemical Agent Monitoring Base Station Remote Computer Local Computer Link Remote Sensor Nodes Radio Link RS-232  Developed a chemical sensor board for WSN applications based on Xbow motes.  Sensor nodes monitor for carbon monoxide (CO), nitrogen dioxide (NO2), and methane (CH4).  Research problem: a suboptimal sensor network coverage of the area of interest while providing quasi real-time tracking and monitoring of the focus area observation space.

31 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 31 Simulation Tool for Coverage Control in Mobile Sensor Networks Simulation tool is needed to experiment with variety of algorithms for sensor node deployment under localization and network connectivity conditions. Development based on C (optimization, network conditions) and C++ (GUI). C language chosen so simulation can be ported to High Performance Computing machines in case it is needed for very large networks. Examples of different scenarios in sensor network coverage control: uniform coverage, focused coverage, balanced coverage control of sensor nodes.

32 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 32

33 L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 33 Thank you! Any questions?


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