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Model Predictive Controllers (MPC) are widely used for controllingcyber-physical systems. It is an iterative process of optimizing the predictionof the future states of a robot over a fixed time horizon. MPCs are effectivein practice, but because they are computationally expensive and slow, they arenot well suited for use in real-time applications. Overcoming the flaw can beaccomplished by approximating an MPC's functionality. Neural networks are verygood function approximators and are faster compared to an MPC. It can bechallenging to apply neural networks to control-based applications since thedata does not match the i.i.d assumption. This study investigates variousimitation learning methods for using a neural network in a control-basedenvironment and evaluates their benefits and shortcomings.

Modelling Controllers for Cyber Physical Systems Using Neural Networks