In our last few blogs, we’ve been sharing how outcome optimizing controllers use Edge technology running on a general-purpose operating system like Linux to offer a generational advancement to PAC-based industrial control systems. These controllers enable safe, secure communication between real-time deterministic control and non-deterministic applications that leverage external data to analyze and optimize business operations. One of the great advantages of the technology is anticipatory monitoring and control applications.
Traditional PLCs and PACs use feedback control where the process is manipulated so that a measured variable tends towards the desired setpoint value. In feedback control, the system waits for an error to occur between the measured value and the setpoint before responding, making the system reactionary. With an outcome optimizing controller, it is possible to gain a much higher level of efficiency and productivity by running predictive analytics on operational data and automating responses according to the results of these analytics. This process would be akin to feedforward control where the prediction is used to respond to the process before an error occurs and hence makes the process more efficient. Here’s one example of how this technology can be implemented to leverage industrial internet data.
Traditionally, proportional integral derivative (PID) loops are tuned manually, which can take anywhere from minutes to weeks depending on the application. Manual tuning requires an engineer to come out on-site at significant cost to manually tune each PID loop, sometimes taking many days. In some cases, PID loops have to be fine-tuned periodically to get the best output from the machine over time. Using optimizing technology, it can now be possible to automatically tune PID loops with great precision using techniques in the outer loop to analyze process dynamics to determine optimal gains automatically. Businesses can not only reduce the initial time of setting up a PID control loop, but it also gives them the opportunity to dynamically adjust the loop according to changing variables and operational circumstances, increasing their time to market and making their processes run more efficiently over time.
In future blogs, we’ll talk about more ways you can use industrial internet data to make operations faster, more accurate and trouble free with outcome optimizing technology.
Are there any operations you’d like to know more about?