Wiener model identification and predictive control of a pH neutralisation process

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Wiener model identification and predictive control of a pH neutralisation process

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Wiener model identification and predictive control of a pH neutralisation process is presented. Input-output data from a nonlinear, first principles simulation model of the pH neutralisation process are used for subspace-based identification of a black-box Wiener-type model. The proposed nonlinear subspace identification method has the advantage of delivering a Wiener model in a format which is suitable for its use in a standard linear-model-based predictive control scheme. The identified Wiener model is used as the internal model in a model predictive controller (MPC) which is used to control the nonlinear white-box simulation model. To account for the unmeasurable disturbance, a nonlinear observer is proposed. The performance of the Wiener model predictive control (WMPC) is compared with that of a linear MPC, and with a more traditional feedback control, namely a PID control. Simulation results show that the WMPC outperforms the linear MPC and the PID controllers.

Inspec keywords: three-term control; identification; predictive control; chemical variables control; feedback; stochastic processes

Other keywords: model predictive controller; nonlinear first principles simulation model; Wiener model identification; subspace-based identification; feedback control; nonlinear subspace identification method; black-box Wiener-type model; PID control; pH neutralisation process

Subjects: Simulation, modelling and identification; Other topics in statistics; Chemical variables control; Optimal control

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