Design of an analytic constrained predictive controller using neural networks

This paper shows how the solution of the standard predictive control problem can be recast as a continuous function of the state, the reference signal, the noise and the disturbances, and hence can be approximated arbitrarily closely by a feed-forward neural network. The existence of such a continuous mapping eliminates the need for linear independency of the active constraints, and therefore the resulting analytic constrained predictive controller will combine constraint handling with speed while being applicable to fast and complex control systems with many constraints. The effectiveness of the proposed controller design methodology is shown for a simulation example of an elevator model and for a real-time laboratory inverted pendulum system.