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Model-based PID tuning methods Two degree of freedom controllers

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Presentation on theme: "Model-based PID tuning methods Two degree of freedom controllers"— Presentation transcript:

1 Model-based PID tuning methods Two degree of freedom controllers
PID Controller Tuning Introduction Model-based PID tuning methods Two degree of freedom controllers On-line PID controller tuning PID tuning guidelines and troubleshooting Simulink example

2 Introduction PID control law Transfer function Controller tuning
Need to select PID parameters (Kc, tI, tD) that yield “good” closed-loop response to disturbances and setpoint changes Only know: KKc > 0, tI > 0, tD >0 Trial-and-error tuning difficult and time consuming Need methods to determine good initial parameter values Refine parameter values by trial-and error fine tuning

3 Impact of Controller Tuning

4 Closed-Loop Performance Criteria
Stability of closed-loop system Minimization of disturbance effects Rapid, smooth tracking of setpoint changes Elimination of steady-state offset Avoidance of excessive control action Robustness to changes in process conditions Robustness to errors in process model

5 Model-Based PID Controller Tuning
Often based on first-order-plus-time-delay model Integral error criteria

6 ITAE Tuning Rules

7 General Trends The controller gain (Kc) is inversely proportional to the process gain (K). Kc decreases as the ratio of the time delay (q) and the dominant process time (t) constant increases. Both the integral time (tI) and the derivative time (tD) increase as q/t increases. A reasonable initial guess for the derivative mode is tD = 0.25tI. Kc decreases as integral control is added to a proportional controller. Kc increases as derivative control is added to a PI controller.

8 ITAE Controller Tuning Example
Process model ITAE tuning for disturbance (more aggressive) ITAE tuning for setpoint (less aggressive)

9 Two Degree of Freedom Controllers
Seek to tune the setpoint tracking response independent of the disturbance rejection response Setpoint filtering Controller operates on Y*sp instead of Ysp Tuning parameter tf determines speed of response Setpoint weighting Tuning parameter b determines speed of response

10 On-Line PID Controller Tuning
Often a process model is not available for controller tuning Need to determine tuning parameters directly from plant data Continuous cycling method Utilizing a proportional controller, find the Kc value that produces sustained oscillations. This is the ultimate gain Kcu. Determine the period of the sustained oscillations. This is the ultimate period Pu. Kc and Pu can also be found from an available transfer function model by direct substitution.

11 Tuning Rules Based on Ultimate Gain

12 Shortcomings of Continuous Cycling Method
Time consuming for processes with large time constants Process pushed to stability limit Ultimate gain does not exist for first- and second-order systems without time delays Generally not applicable to integrating and open-loop unstable systems because stability is achieved only for intermediate Kc values First two shortcomings can be overcome using relay autotuning method to determine Kc and Pu (see text)

13 Step Test Method Model structure

14 Step Test Method cont. Model structure Calculation of q and t
Determine times when output has reached 35.3% (t35) and 85.3% (t85) Calculate q and t Then use tuning method for FOTPD models

15 Shortcomings of Step Test Method
Experimental test performed under open-loop conditions Method is not applicable to open-loop unstable processes Step response can be sensitive to the direction and magnitude of the step change if the process is nonlinear

16 Guidelines for Common Control Loops
Flow rate control – aggressively tuned PI controller due to fast dynamics and few disturbances Liquid level control – conservatively tuned P or PI controller depending on whether offset-free performance is required Gas pressure control – moderately tuned PI controller with little integral action due to small time constants and stability concerns Temperature control – PID controllers with application dependent tuning Composition control – PID or more advanced controller due to sampling delay and measurement noise

17 Troubleshooting Control Loops
Surveys indicate that as many as 35% of industrial controllers are placed in manual at any given time Determine if control problem is attributable to: Transducer – degradation or complete failure Controller – poor tuning or inadequate design Final control element – control valve stiction Process – change in operating condition Troubleshooting procedure Collect data on control system performance Determine if operating conditions have changed Check individual control system components Retune or redesign the controller

18 Simulink Example: continuous_cycling.mdl

19 Determining the Ultimate Gain

20 PID Controller Tuning Kc = 7.88, Pu = 11.66
Ziegler-Nichols tuning rules

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