Approaches for Minimizing Disruptions to Your Process
It’s generally accepted that tuning a PID controller requires some form of testing. Whether a step, bump, doublet, or pseudo random binary sequence, the test forces a change to the Controller Output (CO) which drives a response in the Process Variable (PV). The dynamic relationship between the CO and PV provides the basis for modeling a process’ behavior. In turn, the model is converted into tuning parameters corresponding with your controller. All of this is made possible by conducting a simple test. But what if it’s not that simple.
Testing the dynamics of a process is less than ideal under any circumstances and it’s sometimes considered impossible under others. With each change in CO (no matter how large or small) the process is forced away from the control objective. Consider for a moment that best-practice is to force a change in CO that is 5-10 times the level of noise in the loop. Quality can be compromised. Throughput can be affected. Whether due to their sensitivity or their economics, some loops are off limits and performing a bump test is not an option.
Fortunately, changes in CO are common. Manual adjustments to the final control element are virtually an everyday occurrence for some loops. These changes present the following opportunities for controller tuning:
- Tried and True
If the CO change is large enough and forces a clear change in the PV, then it may be suitable for use in place of a step test. Manual analysis of process data generally requires knowledge of the process and data that both begins and ends at a relative steady-state. Methods for estimating the Gain, Time Constant, and Dead-Time of the process are widely available and can be picked up from a workshop on process control. This is a tried and true approach. Still knowledge of the controller’s algorithm is needed before the model parameters can be converted for use with your controller.
- Just Click Here
Many commercial software packages can tune PIDs using historical process data. By importing the data these tools can generate a model of the process’ dynamics and convert the model into controller-specific tuning parameters with the touch of a button. Some software tools will even simulate the controller’s responsiveness, enabling users to adjust or “fine tune” the settings to satisfy a specific control objective. In most situations a steady-state condition is still required, and both excessive noise and oscillatory behavior generally hamper the accuracy of tuning software. One software has overcome these limitations, making it possible to tune highly dynamic control loops for improved performance.
- Optimize On the Fly
Control Loop Performance Monitoring (CLPM) is a growing category of process diagnostic and optimization solutions. Certain CLPM solutions actively scan process data on a plant-wide basis in search of CO changes. These tools automatically model data associated with each CO change and calculate tuning parameters. Depending on the technology, users receive alerts when the newly calculated tuning parameters are significantly different from existing settings. Capabilities such as this limit the need for additional disruptions to the process for tuning purposes. They facilitate control loop optimization on a wide scale as well as on a near real-time basis.
In terms of tuning a PID controller the need for testing remains. Even so, a variety of options exist that all but eliminate the need for additional disruptions to a process. Whether by applying a little training or by using technology, it’s possible to improve control loop performance with the data you already have.
