An Optimization Framework for Designing Input Signals for Plant Testing
- Type: Conference Presentation
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The plant test is a critically important step in the development of model-based process controllers such as MPC, since the resulting data becomes the basis for identifying a multivariable dynamic model of the plant. A range of testing approaches are used in practice that entail both manual and automatic (computer-generated) test signal designs, most often in open loop, but increasingly in closed loop. The testing in industry continues to rely on uncorrelated input signals, either tested manually one input at a time or generated randomly to achieve a low level of spatial correlation between any two inputs (e.g., PRBS); However, such signals are statistically optimal only when the constraint bounds are limited to the inputs. Academic investigations have shown the benefits of designs which lead to correlated, higher-amplitude input signals.
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