Industrial, large-scale model predictive control with structured neural networks
نویسندگان
چکیده
The design of neural networks (NNs) is presented for treating large, linear model predictive control (MPC) applications that are out reach with available quadratic programming (QP) solvers. First, we introduce a new feedforward network architecture enables practitioners to obtain offset-free closed-loop performance NNs. Second, discuss the data generation procedure sample state space relevant training NNs based on anticipated online setpoint changes and plant disturbances. Third, use input-to-state stability results in MPC literature establish robustness properties NN controllers. Finally, present illustrative simulation studies process examples. We apply approach compare QP an industrial crude distillation unit 252 states, 32 inputs, control-sample horizon length 140. Parallel computing used graphical processing units training. Anticipated operational scenarios setpoints disturbances may change during operation must be sampled After offline phase, execute three five orders magnitude faster than solver less 1% loss performance.
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ژورنال
عنوان ژورنال: Computers & Chemical Engineering
سال: 2021
ISSN: ['1873-4375', '0098-1354']
DOI: https://doi.org/10.1016/j.compchemeng.2021.107291