Performance of the new neural network based control structure and learning algorithm

This paper presents a new neural netwodc (NN) based Adaptive Backthrough Control (ABC) & e m for both linear and nodinear dynamic plants. A feedforwanl approach p r e s e n t e d here falls into the direct design category. In its simplest form the implementation requires an -on of the process parameters at any sample t . UnWre the other feedforward NN based control schemes the ABC hem comprises of one neural netwodr only which ~ t a n m l y acis as both plant model (emulator) and the contmller (iivers of the emulator). For linear plants, without noise, the resulting feedforwd controller, providing that the order of the plant and plant model are equal, is a perfect adaptive poleszeros canceller. In the case d nonlinear dynamic system, and for the monotonic nonlinearity, the Hoposed ABC control represents the nonlinear predictive controller. The ABC scheme is based on the discrete nonlinear (NARMAX) dynamic model. For such models and for monotonic nonlinearity, the calculation of the desired control si@ js the result of the nonlinear optimization procedure with guaranteed convex searcb function and consequently with an unique solution.