LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and

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LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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 Email: rselmic@latech. edu Web: http: //www 2. latech. edu/~rselmic/ 1

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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. 2

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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 netbased observer sensitivity Simulation results are presented to illustrate the detectability criteria and fault detection in nonlinear systems. 3

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

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS NN Tuning Law 9

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS A NN Observer 15

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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. 20

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

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

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS Intelligent Sensors and Actuators Group Research interests: Ø Ø Ø 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. 27

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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. 28

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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. 29

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS Testbed Development – Chemical Agent Monitoring Ø Developed a chemical sensor board for WSN applications based on Xbow motes. Ø Sensor nodes monitor for carbon monoxide (CO), nitrogen dioxide (NO 2), and methane (CH 4). Ø 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. Link Local Computer Remote Sensor Nodes RS-232 Remote Computer Base Station Radio Link 30

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 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. 31

LOUISIANA TECH UNIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK OUTPUT FEEDBACK OTHER PROJECTS 32