ROBUST ADVANCED PID CONTROLRaPID

1
ROBUST ADVANCED PID CONTROL(RaPID)

• By
• Falguni Gandhi
• Feroza Salam
• Preethi Krishnan
• Deepa Deshmukh

2
Outline

• Introduction
• Project Description
• Collection of Data
• Identification
• Control Design
• Control Objective and Control Structure
• Optimization Criteria
• Optimization constraints
• Conclusion

3
Introduction

• Transfer function of the PID controller looks
  like the following kp ki/s kds
• Robust Control- A Controller that will maintain
  the stability as well as achieve specified
  performances over the range of operating
  conditions.
• RaPID is an intuitive tool with multiple levels
  of complexity which can be accessed using the
  control loops.
• RaPID has a library of PID structures of most
  frequently used DCS and PLC systems.

4
Project Description

• Project description describes the methods and
  algorithms used by RaPID for tuning PID loops.
• Information about the control loop such as
  sampling time, ranges, control variables are
  needed to interrupt sampled data and define the
  limits of variables.
• The limits provide saturation constraints and
  appropriate scaling of variables.

5
Contd..

• The controller templates are based on description
  provided by manufacturers such as
  semiens,emerson have advantages
• Exact knowledge of the controller structure
• Elimination of the need for mutual conversion of
  kp, ti, kd to the manufacturers format.
• RaPID has the functionalities to perform
  preprocessing of the data

6
Collection of process data

• The RaPID uses an input-output experiment to
  identify the dynamics of the plant.
• The predefined input signals include step, block,
  polynomial step and other noise input signals.
• The RaPID contains a set of modules to connect
  the most frequently used control systems and also
  able to retrieve data from history

7
Contd..

• Signals that are generated during the experiment
  are loaded into a program by means of files or by
  using object linking and embedding for process
  control to the process computer.
• OPC connection helps us to manipulate the input
  variable signal.
• The control variable signal is used for
  identification.

8
Identification

• RaPID uses a system identification algorithm that
  combines subspace identification , prediction
  error methods and intuitive user interface.
• It finds out the delays and poles and zeros in
  the model and the best fit is automatically
  selected.

9
Contd..

• The signal offset is removed and automatic
  preprocessing is applied as the algorithm detects
  the integral effects.
• Other options include signal filtering using LP,
  HP,BP and BS filters.
• The configuration of the filters can be set by
  using the cut off frequencies and the dynamic
  order.

10
Identification panel of RaPID Dark green is
the measured signal, Light green is the
identified model output and the red line
is the manipulated variable input.
11
User interface for controller optimization Allows
the user to define constraints on noise
sensitivity and robustness
12
Control Design

• Traditionally PID controllers are tuned by
  prescribing closed loop step response. A trial
  and error method is used to achieve the response
  often sacrificing the robustness of the original
  parameters.
• RaPID uses constraint optimization to obtain the
  desired time response while guaranteeing
  robustness.
• The three components that are defined are control
  objective and control structure, cost function,
  and constraints.

13
Control objective and Control structure

• The Objectives pursued by RaPID include tracking
  and variance control.
• RaPID optimizes the PID parameters kp,ki and kd
  as well as parameters alpha, beta gamma.

14
User Interface for Controller
Optimization
15
PID Control with Feed forward PD action and
manual reset
16
Optimization Criteria

• For optimization RaPID uses time defined criteria
  and overall error criteria.
• Time define criterion-the op minizes the time
  needed to reach a given point of the step
  response.
• It includes both settling time and rise time as
  time defined criteria.

17
Error
based optimization criteria
18
Optimization Constraints

• Optimizing the parameters of the controller based
  only on the optimization criteria often results
  in a controller with less than satisfactory
  behavior.
• Over shoot-It allows the user to specify the max
  possible overshoot.
• Saturation-It restricts the actions of the
  controller to the actuators physical limit thus
  avoiding integrator windup

19

• High frequency gain-This limits the effects of
  high frequency measurement noise on the actuator.
• Robustness-A robustness constraint is used which
  can guarantee robust stability and performance of
  the optimized PID values.
• Rapid uses parabolic constraint.

20
NYQUIST PLOT AND THE PARABOLIC CONSTRAINT
21
APPLICATION

• It is used in mechanical systems
• Power plants , refineries, chemical plants.
• Food beverage plants.

22
Conclusion

• RaPID improves stability and safety in plants.
• Productivity is increased.
• Reduction in down times due to actuators
  failures observed.
• Energy savings have been increased.

23
THANK YOU