Documents
Resources
Learning Center
Upload
Plans & pricing Sign in
Sign Out

TMR 4240 Marine Control Systems (PowerPoint download)

VIEWS: 7 PAGES: 43

									1




    An overview of signal
        processing
      Operator

                                                    Output
                 Controller     D/A
                              converter   Process
                                           Plant




                                A/D
                              converter


                Lecture 4, Spring 2006
           TMR4240 Marine Control Systems
           Department of Marine Technology,
     Norwegian University of Science and Technology,
                 Trondheim, Norway.
                      3 Feb 2006
                                                      Tristan Perez – TMR4240 Spring 2006
2


    Why studying signal procesing?

    To date vessel operations depend on large amounts of information.

    All this information is mostly processed by computers, and the way
      these computers communicate and see the outside world is via
      signals which contain essential information!

    As we will see, computer systems dedicated to monitoring and control
      on marine vessels perform many different task to the received
      signals—this easier done in discrete time. understanding this is
      essential for implementing control systems.




                                                        Tristan Perez – TMR4240 Spring 2006
3

    Automation system architecture




     Computer-based control
                                     Tristan Perez – TMR4240 Spring 2006
4

    Integrated automation system
               POSITIONING SYSTEM
                                     POWER MANAGEMENT
                                     VESSEL AUTOMATION




         ESD
         FIRE&GAS                   PROCESS CONTROL
                                    CARGO CONTROL




                                          Tristan Perez – TMR4240 Spring 2006
5

    Positioning control system architecture

                                  TAUT-WIRE
                                 ARTEMIS                   GYRO
                                  HPR                         MRU
                                                                               MOORING
       Thrusters                 DGPS                           WIND           SYSTEM




        Thrust                             Sensor signal
      allocation                            processing


                    Wind load
                   feedforward


                    Reference                              Reference
                   feedforward                              model



                     Optimal      Vessel                    Model
                    controller    model                    adaption




                                                                       Tristan Perez – TMR4240 Spring 2006
6

    Sensor Signal Processing Unit

                             HW Signal Communication already checked


                                Features:
                                • Online Signal Quality Check

                                • Online weighting of sensor signals
    Signal Processing Unit
                                • Multiple signal voting algorithms

                                • Filtering and smoothing of signals




                                                    Tristan Perez – TMR4240 Spring 2006
7

    Position reference systems

                                     SATELLITE
                                    NAVIGATION
                                      SYSTEM
                                  (DGPS / GLONAS)




             SURFACE
            REFERENCE
              SYSTEM




                                 HYDROACOUSTIC
                                   POSITIONING
                        TAUT         SYSTEM
                        WIRE




                                                 Tristan Perez – TMR4240 Spring 2006
8
    Signal Processing - Example

                   Examples of four different signal failures
                   the signal QA module is detecting.

          35

                   High derivative
          30
                   Frozen signal
          25
                   High variance
          20
                   Wild point
          15


          10


           5


           0
               0     0.5   1    1.5   2    2.5   3   3.5    4       4.5




                                                                Tristan Perez – TMR4240 Spring 2006
9
    Signal quality checking

    Three level testing:
      1. Tests on individual signals
        – Range check
        – Variance check
        – Wild point detection and removal
      2. Sensor voting
        – Detection of sensor drift
      3. Sensor weighting
        – Unbiased minimum variance measurements
        – Observer test (Innovation/injection term)




                                                      Tristan Perez – TMR4240 Spring 2006
10
     Statistics
        Consider the sequence of n-1 historical values:



      Average value:




     Variance:




                                                          Tristan Perez – TMR4240 Spring 2006
11
     Range check

      • Most of the signals available have a defined range:
         – Example: Gyro signal within 0-360 degrees.

      • Signal outside the range will indicate that the sensor is faulty:
         signal will be rejected.

      • Alarm issued to the operator.




                                                                   Tristan Perez – TMR4240 Spring 2006
12

     Variance check

     • The variance of each signal
       is calculated.
                                               High variance limit
                                               High variance limit

     • High variance may indicate
       sensor failure or inaccurate
       measurement.                    Calculated signal variance



     • Low variance may indicate a
       frozen signal.
                                                                                      Low variance limit


     • Alarm issued to the operator.




                                                                     Tristan Perez – TMR4240 Spring 2006
13

     Wild point check

     • The signal value is a wild point if
       it is outside a band around the       Wild point

                                                                 Wild point limit
       estimated signal mean.

     • The signal value will be rejected
                                                               Measurement
       for one sample.

     • Alarm issued to the operator.
                                                                   Wild point limit




                                                          Tristan Perez – TMR4240 Spring 2006
14
     Sensor voting                                                                            SATELLITE
                                                                                             NAVIGATION
                                                                                               SYSTEM
                                                                                           (DGPS / GLONAS)


      • Purpose                                      SURFACE
                                                    REFERENCE
                                                      SYSTEM
         – To detect drift of a sensor or
           position reference system                                                   HYDROACOUSTIC
                                                                                         POSITIONING
      • Actions                                                 TAUT
                                                                                           SYSTEM


         – Alert the operator                                   WIRE


         – If possible, automatically
           ignore the erroneous sensor
      • Advantage                           Value



         – Improved safety
         – Better utilization of
           redundant sensor
           configurations
                                                          1            2        3        Sensor No




                                                                           Tristan Perez – TMR4240 Spring 2006
15

     Sensor weighting

     • Manual weighting
                                                     3 sensors:
        – Operator decide weights wi
        – Advantages:
            • intuitive understanding of operation
            • operator experience and judgement
              is utilized

     • Automatic weighting
        – System calculate unbiased minimum
          variance measurements
        – Advantages:
            • best possible measurements in most
              situations
            • automatic operation



                                                             Tristan Perez – TMR4240 Spring 2006
16
     Handling loss of signals
      • Filtering should not give phase to the measurement.
      • Tf depends on difference of sensors. Maximum change
        [m/s] is specified.
      • A change in average value is inevitable.




                                      average




                                                 Tristan Perez – TMR4240 Spring 2006
17
     Enabling of sensors
     • When enabling sensors, average remains smooth.
     • No filtering of the sensor signals => no phase added to
       the measurement.




                   average




                                                      Tristan Perez – TMR4240 Spring 2006
18


     Simplified computer control system

 The implentation of control systems todate require knowledge of

 •   Vessel dynamics
 •   Signal processing
 •   Digital control
 •   Real-time OS




                                                       Tristan Perez – TMR4240 Spring 2006
19


     Continuous-discrete time domains



           Operator

                                                                                    Output
                      Controller     D/A
                                   converter            Process
                                                         Plant




                                     A/D
                                   converter


        Discrete-time domain                   Continuous-time domain


                                                                  Tristan Perez – TMR4240 Spring 2006
20


     Control design
      The designer of a control system has two choices:

      • Design in continuous time and implement a
        discrete-time approximation of the controller

      • Obtain a dicretised modelled of the plant and
        design the controller in Discrete time.

      Each approach has advantages and disadvantages.

                                            Tristan Perez – TMR4240 Spring 2006
21

     Continuous time design Discrete time
     implementation
     Advantages:
     • Our world is easy to understand in term of continuous time signals and
       systems.
     • Models obtained from first principles of physics are continuous time models.
     • Tools for control system design are more developed for continuous time---
       nonlinear control methods deal almost exclisuvely with continuous-time
       formulations.

     Disadvantages:
     • Controllers are implemented on computers, therefore, we need to implement
        discrete time approximations of our continuous time controllers.
     • A good approximation of a continuous-time controller often requires fast
        sampling. We may not be able to do this because of limited computer power
        and speed, and then the discrete-time nature of the implemented controller
        may affect performance and stability.
     • If our model is not complete, we may need to use system identification,
        which often yields models in discrete time.


                                                                   Tristan Perez – TMR4240 Spring 2006
22


     Discrete time design
     Advantages:
     • Takes advantage of models obtained from system identification
     • Allows to do things that continuous-time controllers do not allow:
       dead-beat control.
     • Current computer power and speed allow the use of on-line
       optimisation, and this is having a tremendous impact in the idustry.
     • Some filtering and control problems are more easyly solved in
       discrete time (Kalman filtering, contrained optimal control).
     • It is easy to predict stability problems if sampling is not so fast.

     Disadvantages:
     • Not so intuitive
     • Analysis of non-linear control system in discrete-time is more
       difficult.

                                                          Tristan Perez – TMR4240 Spring 2006
23


     Review continous-time linear models
 ODE:




      Solution:                        (Convolution)



 State-space:


        Solution:

                                     Tristan Perez – TMR4240 Spring 2006
24


     Laplace-transform Domain
 SISO:                      The transfer function is the
                            Laplace transform of output
                            divided by the Laplace
                            transform of the Input




 MIMO:




                                        Tristan Perez – TMR4240 Spring 2006
25


     Linear systems frequency reponse
 If the input is



Then, due to the linearity of the systemIf the input is



 where



     The Transfer Function        evaluated at                 gives the
     Frequency Reponse of the system           .
                                                          Tristan Perez – TMR4240 Spring 2006
26


     Frequency response




                          Tristan Perez – TMR4240 Spring 2006
27


     Sampling continuous-time signals
 When we sample signals to be processed by a computer, we create
 ambiguity:
 Discrete time sinusoids whose frequencies are separated by an
 integer multiple of 2p fs are identical!




                                                       Tristan Perez – TMR4240 Spring 2006
28

      Sampling theorem and Nyquist rate and
      Frequency

     The sampling theorem states that for a limited bandwidth
     (band-limited) signal with maximum frequency fmax, the
     equally spaced sampling frequency fs must be greater than
     twice of the maximum frequency fmax, i.e., fs > 2·fmax in
     order to have the signal be uniquely reconstructed without
     aliasing.

     The frequency 2·fmax is called the Nyquist sampling rate.
     Half of this value, fmax, is sometimes called the Nyquist
     frequency.



                                                          Tristan Perez – TMR4240 Spring 2006
29

     Spectrum of sampled signal
     Sampled at greater than the Nyquist rate




                                      Tristan Perez – TMR4240 Spring 2006
30

     Spectrum of sampled signal
     Sampled at less than the Nyquist rate




                                      Tristan Perez – TMR4240 Spring 2006
31


     Disctretising state-space models
     If we have access to a continuous time LTI model, then there are
     different methods that we can use to convert it into a discrete-time
     model:




     where the index k denotes that the value of the variables is known at
     the time instant tk = t0 + Ts k, where Ts is the sampling period, and
     the index takes the values k = 0, 1, 2, ...
                                                            Tristan Perez – TMR4240 Spring 2006
32


     Euler Method
     A simple method (although not the one with the best numerical
     properties) is to approximate the derivative in the state equation in a
     state space model by an increment:




 which leads to the Euler Method:




                                                            Tristan Perez – TMR4240 Spring 2006
33


     Zero-order hold (ZOH)
                                                 ZOH

             Operator

                                                                                Output
                        Controller     D/A
                                     converter        Process
                                                       Plant




                                       A/D
                                     converter


     The control signal is kept constant in the time interval



                                                                Tristan Perez – TMR4240 Spring 2006
34


     Zero-order hold (ZOH)
     Using the solution of the state-space equation:




     We obtain the ZOH equivalent:




 Note that this is not an approximation, and it reverts to the Euler Method if we use a
 series expansion of the exponential and keep the 1st term only
                                                                     Tristan Perez – TMR4240 Spring 2006
35


     Continuous-time filter design

 Bacause the Transfer Function           evaluated at                   gives
 the Frequency Reponse of the system             .


 We can then design               so we select the frequencies of interest
 and eliminate the undesired ones:
 • Low pass filter: reduce high frequencies
 • High pass filter: reduce low frequencies
 • Band pass filter: reduces low and high with respect to a desired band
 • Notch: eliminates a particular band
                                                         Tristan Perez – TMR4240 Spring 2006
36


     Filter specification




                            Tristan Perez – TMR4240 Spring 2006
37


     Filter design


     Filters can be designed in continuous time and
       implemented with analog electronics, or
       discretised.

     Alternatively the can be designed in discrete time
      directly.



                                             Tristan Perez – TMR4240 Spring 2006
38


     Butterworth Filter
 • Very flat pass band
 • Small transtiotn band requires high order filters, which introduce a
   significant phase lag.




                                                         Tristan Perez – TMR4240 Spring 2006
39


     Example 4th order Batterworth




                                     Tristan Perez – TMR4240 Spring 2006
40


     Chevychev Filters

     • Chebyshev filters have a steeper roll-
     off and more passband ripple than
     Butterworth filters.




                                                Tristan Perez – TMR4240 Spring 2006
41


     Chevychev Filter Example




                                Tristan Perez – TMR4240 Spring 2006
42


     Low- High- Band-pass and notch Filters


     Once we have a low pass filter protetype, we can
      easy conver it to other type by a transformation.




                                             Tristan Perez – TMR4240 Spring 2006
43


     Advantages of digital filter design
     • Digital filter are programmable, i.e. their operation is determined by a program.
     This means the digital filter can easily be changed without affecting the circuitry
     (hardware).

     • Digital filters are easily designed, tested before being implementied.

     • Analog filter circuits (particularly those containing active components) are
     subject to drift and are dependent on temperature.

     • Digital filters can handle low frequency signals accurately. As the speed of DSP
     technology continues to increase, digital filters are being applied to high
     frequency signals in the RF (radio frequency) domain, which in the past was the
     exclusive preserve of analog technology.

     • Digital filters can be adaptive to changes in the characteristics of the signal.


                                                                         Tristan Perez – TMR4240 Spring 2006

								
To top