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Model predictive control using neural networks and genetic algorithms
Potočnik, Primož, 1969- ; Grabec, Igor
Nonlinear model predictive control (MPC), based on neural networks and genetic algorithms, is proposed. The control scheme comprises a process, a model, an optimizer, a controller and a corrector. ...Neural networks are used for experimental modeling of the process and the model is applied to MPC. A genetic-algorithm-based optimizer is developed for the optimization of control trajectories. Coding with real numbers and specially designed genetic operators, including initialization, mutation, crossover and termination, are proposed to implement the optimizer, which performs constrained optimization in MPC. A neural-network-based controller is included in the control scheme for enhanced optimizer initialization and for autonomous control after the learning period. The proposed nonlinear MPC methodology is suitable for control of complex nonlinear processes.
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