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Chapter 9 PID Tuning Methods

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Overall Course Objectives Develop the skills necessary to function as an industrial process control engineer. –Skills Tuning loops Control loop design Control loop troubleshooting Command of the terminology –Fundamental understanding Process dynamics Feedback control

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Controller Tuning Involves selection of the proper values of K c, I, and D. Affects control performance. Affects controller reliability Therefore, controller tuning is, in many cases, a compromise between performance and reliability.

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Tuning Criteria Specific criteria –Decay ratio –Minimize settling time General criteria –Minimize variability –Remain stable for the worst disturbance upset (i.e., reliability) –Avoid excessive variation in the manipulated variable

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Decay Ratio for Non-Symmetric Oscillations

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Performance Assessment Performance statistics (IAE, ISE, etc.) which can be used in simulation studies. Standard deviation from setpoint which is a measure of the variability in the controlled variable. SPC charts which plot product composition analysis along with its upper and lower limits.

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Example of an SPC Chart

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Classical Tuning Methods Examples: Cohen and Coon method, Ziegler- Nichols tuning, Cianione and Marlin tuning, and many others. Usually based on having a model of the process (e.g., a FOPDT model) and in most cases in the time that it takes to develop the model, the controller could have been tuned several times over using other techniques. Also, they are based on a preset tuning criterion (e.g., QAD)

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Controller Tuning by Pole Placement Based on model of the process Select the closed-loop dynamic response and calculate the corresponding tuning parameters. Application of pole placement shows that the closed-loop damping factor and time constant are not independent. Therefore, the decay ratio is a reasonable tuning criterion.

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Controller Design by Pole Placement A generalized controller (i.e., not PID) can be derived by using pole placement. Generalized controllers are not generally used in industry because –Process models are not usually available –PID control is a standard function built into DCSs.

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IMC-Based Tuning A process model is required (Table 9.4 contain the PID settings for several types of models based on IMC tuning). Although a process model is required, IMC tuning allows for adjusting the aggressiveness of the controller online using a single tuning parameter, f.

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Controller Reliability The ability of a controller to remain in stable operation with acceptable performance in the face of the worst disturbances that the controller is expected to handle.

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Controller Reliability Analysis of the closed loop transfer function for a disturbance shows that the type of dynamic response (i.e., decay ratio) is unaffected by the magnitude to the disturbance.

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Controller Reliability We know from industrial experience that certain large magnitude disturbance can cause control loops to become unstable. The explanation of this apparent contradiction is that disturbances can cause significant changes in K p, p, and p which a linear analysis does not consider.

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Controller Reliability Example: CSTR with C A0 Upsets

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Controller Reliability Is determined by the combination of the following factors –Process nonlinearity –Disturbance type –Disturbance magnitude and duration If process nonlinearity is high but disturbance magnitude is low, reliability is good. If disturbance magnitude is high but process nonlinearity is low, reliability is good.

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Tuning Criterion Selection

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Tuning Criterion Selection Procedure First, based on overall process objectives, evaluate controller performance for the loop in question. If the control loop should be detuned based on the overall process objectives, the tuning criterion is set. If the control loop should be tuned aggressively based on the overall process objectives, the tuning criterion is selected based on a compromise between performance and reliability.

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Selecting the Tuning Criterion based on a Compromise between Performance and Reliability Select the tuning criterion (typically from critically damped to 1/6 decay ratio) based on the process characteristics: –Process nonlinearity –Disturbance types and magnitudes

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Effect of Tuning Criterion on Control Performance The more aggressive the control criterion, the better the control performance, but the more likely the controller can go unstable.

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Filtering the Sensor Reading For most sensor readings, a filter time constant of 3 to 5 s is more than adequate and does not slow down the closed-loop dynamics. For a noisy sensor, sensor filtering usually slows the closed-loop dynamics. To evaluate compare the filter time constant with the time constants for the acutator, process and sensor.

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Recommended Tuning Approach Select the tuning criterion for the control loop. Apply filtering to the sensor reading Determine if the control system is fast or slow responding. –For fast responding, field tune (trail-and-error) –For slow responding, apply ATV-based tuning

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Field Tuning Approach Turn off integral and derivative action. Make initial estimate of K c based on process knowledge. Using setpoint changes, increase K c until tuning criterion is met

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Field Tuning Approach Decrease K c by 10%. Make initial estimate of I (i.e., I =5 p ). Reduce I until offset is eliminated Check that proper amount of K c and I are used.

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An Example of Inadequate Integral Action Oscillations not centered about setpoint and slow offset removal indicate inadequate integral action.

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Demonstration: Visual Basic Simulator Field Tuning Example

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ATV Identification and Online Tuning Perform ATV test and determine ultimate gain and ultimate period. Select tuning method (i.e., ZN or TL settings). Adjust tuning factor, F T, to meet tuning criterion online using setpoint changes or observing process performance: K c =K c ZN /F T I = ZN ×F T

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ATV Test Select h so that process is not unduly upset but an accurate a results. Controller output is switched when y s crosses y 0 It usually take 3-4 cycles before standing is established and a and P u can be measured.

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Applying the ATV Results Calculate K u from ATV results. ZN settings TL settings

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Comparison of ZN and TL Settings ZN settings are too aggressive in many cases while TL settings tend to be too conservative. TL settings use much less integral action compared to the proportional action than ZN settings. As a result, in certain cases when using TL settings, additional integral action is required to remove offset in a timely fashion.

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Advantages of ATV Identification Much faster than open loop test. As a result, it is less susceptible to disturbances Does not unduly upset the process.

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Online Tuning Provides simple one-dimensional tuning which can be applied using setpoint changes or observing controller performance over a period of time.

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ATV Test Applied to Composition Mixer

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CST Composition Mixer Example Calculate K u Calculate ZN settings Apply online tuning

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Online Tuning for CST Composition Mixer Example F T =0.75 F T =0.5

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Control Performance for Tuned Controller

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Critically Damped Tuning for CST Composition Mixer

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Comparison Between 1/6 Decay Ratio and Critically Damped Tuning

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Demonstration: Visual Basic Simulator ATV based tuning

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PID Tuning Procedure Tune PI controller using field tuning or ATV identification with online tuning. Increase D until minimum response time is obtained. Initially set D =P u /8. Increase D and K c by the same factor until desired response is obtained. Check response to ensure that proper amount of integral action is being used.

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Comparison between PI and PID for the Heat Exchanger Model

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Comparison of PI and PID The derivative action allows for larger K c which in turn results in better disturbance rejection for certain processes.

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Demonstration: Visual Basic Simulator PID Tuning Example

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Initial Settings for Level Controllers for P-only Control Based on critically damped response. F MAX is largest expected change in feed rate. L MAX is the desired level change under feedback control. Useful as initial estimates for slow responding level control systems.

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Initial Settings for Level Controllers for PI Control A c is cross-sectional area to tank and is liquid density. F MAX is largest expected change in feed rate. L MAX is the desired level change under feedback control. Useful as initial estimates for slow responding level control systems.

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Initial Settings for Level Controllers Use online tuning adjusting K c and I with F T to obtain final tuning. Remember that K c is expressed as (flow rate/%); therefore, height difference between 0% and 100% is required to calculate I.

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In-Class Example Calculate the initial PI controller settings for a level controller with a critically damped response for a 10 ft diameter tank (i.e., a cylinder placed on its end) with a measured height of 10 ft that normally handles a feed rate of 1000 lb/h. Assume that it is desired to have a maximum level change of 5% for a 20% feed rate change and that the liquid has a density corresponding to that of water.

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Control Interval, t t is usually 0.5 to 1.0 seconds for regulatory loops and 30 to 120 seconds for supervisory loops for DCSs. In order to adequately approach continuous performance, select the control interval such that: t < 0.05( p + p ) For certain processes, t is set by the timing of analyzer updates and the previous formula can be used to assess the effect on control performance

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Effect of Control Interval on Control Performance p =0.5 When the controller settings for continuous control are used with t=0.5, instability results. Results shown here are based on retuning the system for t=0.5 resulting in a 60% reduction in K c.

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Overview Controller tuning is many times a compromise between performance and reliability. Reliability is determined by process nonlinearity and the disturbance type and magnitude. The controller tuning criterion should be based on controller reliability and the process objectives.

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Overview Classical tuning methods, pole placement and IMC tuning are not recommended because they are based on a preset tuning criterion and they usually require a process model. Tune fast loops should be tuned using field tuning and slow loops using ATV identification with online tuning.

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