How Can I Correct for Noise Using External Filters?

Choices, choices. In the realm of process control practitioners are regularly forced to choose between competing options. Consider a PID control loop: Should it be tuned for faster disturbance rejection or tighter Set Point tracking? Should the Derivative Term be used or does the PI configuration provide a sufficiently fast Settling Time? And the choices go on and on. In that sense there are multiple choices for filtering noise too – options that provide very different benefits. Fortunately when it comes to filtering for Signal Noise the choice is typically clear.

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How Can I Correct for Noise Using Internal Filters?

Noise is inevitable. To one degree or another it’s evident in the data of most every production process. Sure it can be absent in academic settings and similar lab environments where simulations often generate sanitized data. However, in the real world of industrial manufacturing noise is a factor that cannot be avoided. Failing to account for or manage noise can be a recipe for – well – failure.

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Where Can I Purchase RSTune?

Nowhere…Because RSTune and RSLoop Optimizer Were Discontinued in 2011!

Product names often outlive the products themselves. Investments made to establish a product and a brand can have a lasting impact.

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What is Feed-Forward Control?

In a previous post cascade control was introduced as an effective means of limiting the lag between an upset and the associated PID control loop’s correction. As practitioners know: The longer the delay in responding, the larger the negative impact on a process. Like cascade, Feed-Forward enables the process to preemptively adjust for and counteract the effects of upstream disturbances.

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What Sample Rate is Needed When Tuning PID Controllers?

Practitioners often apply simple guidelines when it comes to data collection requirements and controller tuning. These “rules of thumb” assure that sufficient data resolution exists when a given PID control loop’s dynamics are being analyzed. Without good data a process engineer’s ability to model the dynamics and tune for improved control can be undermined. If the sample rate is too slow, then most any tuning procedure will be hit-or-miss at best. So what exactly are those guidelines?

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What is Model-Predictive Control?

Model-Predictive Control (MPC) is advanced technology that optimizes the control and performance of business-critical production processes. So is Control Loop Performance Monitoring (CLPM) software. But if both help practitioners to optimize control loop performance, then what’s the difference?

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