When this is done often enough, the brain learns what not to do. In this case, these excursions (which would likely cause a process trip) are experiences for the brain to learn what behaviors to avoid. Many of these states would never be encountered in the real world, as the AI engine (brain) may try to operate the plant much too close or beyond to the operational limits of a physical facility. The DRL engine can be exposed to a wide variety of process states by the simulator. Hence, these controllers can discover nuances and exceptions that are not easily captured in expert systems, and may be difficult to control when using fixed-gain controllers.

Learning: DRL-based controllers learn by methodically and continuously practicing – what we know as machine teaching.

Three characteristics of AI-based controllersĪI-based controllers (that is, deep reinforcement learning-based, or DRL-based, controllers) offer unique and appealing characteristics, such as: One of the latest advancements in this area is the application of reinforcement learning-based controls, as Figure 1 shows.

The latest evolution of control strategies is the adoption of AI technologies to develop industrial controls. Model predictive control (MPC) adds further capabilities to this by layering predictions of future control action results and controlling multiple correlated inputs and outputs. For example, feed forward improves PID control by predicting controller output, and then uses the predictions to separate disturbance errors from noise occurrences. Each control system technology offers its own characteristics not available in prior technologies. Erie is one of ten counties that use ES&S DS-200 to scan their ballots.The proportional-integral-derivative (PID) controller can be interpreted as a layering of capabilities: the proportional term points toward the signal, the integral term homes in on the setpoint and the derivative term can minimize overshoot.Īlthough the controls ecosystem may present a complex web of interrelated technologies, it can also be simplified by viewing it as ever-evolving branches of a family tree. 'I am perplexed by your email because Erie County does not use Dominion equipment. Consequently, the machine would be useless in the field.'

They indicated that the Erie Board of Elections programmed their Dominion machine to produce multiple ballot faces, but when it was tested as if at a polling site (after the seals were broken), it took 18 hours to boot up. The example they cited was the test experience of Erie County. ' The objection is this: because the machine takes so long to boot up on site, it is impractical to use. To: NY State Co-Chair Douglas Kellner, describing information she had received from Westchester County Election Commissioners: Election Commissioners seem to be misrepresenting this information.īelow is correspondence from an attorney from Indivisible Scarsdale, Myra Saul, to the NY State Board of Elections.

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