The paper discusses the modeling and control of a 800 metric tons servo mechanical forming press. The control objective is to actuate the kinetic energy recovery system so to minimize the amplitude of the oscillations on the Direct Current bus line. The paper describes and models all the components of the system from an energy standpoint. Measured data on an instrumented press validate the model. Two controllers are proposed: a perfect knowledge controller that requires knowledge of the working cycle and a nested causal closed loop controller. A detailed simulation study compares the two approaches, showing that both approaches successfully reduce the variance of the power. The analysis further confirms that the perfect knowledge control outperforms the closed loop controller in nominal conditions; the closed loop controller is on the other hand capable of guaranteeing a 3 kW peak to peak variation in a robust way.

Modeling and Control of a Servo Mechanical Press

Corno, M.;ZAFFARONI, SERENA;Girotti, A.
2018-01-01

Abstract

The paper discusses the modeling and control of a 800 metric tons servo mechanical forming press. The control objective is to actuate the kinetic energy recovery system so to minimize the amplitude of the oscillations on the Direct Current bus line. The paper describes and models all the components of the system from an energy standpoint. Measured data on an instrumented press validate the model. Two controllers are proposed: a perfect knowledge controller that requires knowledge of the working cycle and a nested causal closed loop controller. A detailed simulation study compares the two approaches, showing that both approaches successfully reduce the variance of the power. The analysis further confirms that the perfect knowledge control outperforms the closed loop controller in nominal conditions; the closed loop controller is on the other hand capable of guaranteeing a 3 kW peak to peak variation in a robust way.
2018
2018 IEEE Conference on Control Technology and Applications, CCTA 2018
9781538676981
Aerospace Engineering; Control and Optimization; Automotive Engineering; Safety, Risk, Reliability and Quality
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1075743
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