This paper discusses a novel approach to tuning 2DOF PID controllers for a positional control system, with a special focus on filters. It is based on the multiple real dominant pole method, applicable to both standard and series PID control. In the latter case it may be generalized by using binomial nth order filters. These offer filtering properties scalable in a much broader range than those allowed by a standard controller. It is shown that in terms of a modified total variance, controllers with higher order binomial filters allow a significant reduction of excessive control effort due to the measurement noise. When not limited by the sampling period choice, a significant performance increase may be achieved by using third order filters, which can be further boosted using higher order filters. Furthermore, all of the derived tuning procedures keep the controller design sufficiently simple so as to be attractive for industrial applications. The proposed approach is applied to the position control of electrical drives, where quantization noise can occur as a result of angular velocity reconstruction using the differentiated outputs of incremental position sensors.
The paper deals with the recently proposed autotracking piecewise cubic approximation (APCA) based on the discrete projective transformation, and neural networks (NN). The suggested new approach facilitates the analysis of data with complex dependence and relatively small errors. We introduce a new representation of polynomials that can provide different local approximation models. We demonstrate how APCA can be applied to especially noisy data thanks to NN and local estimations. On the other hand, the new approximation method also has its impact on neural networks. We show how APCA helps to decrease the computation time of feed forward NN.