FPGA Implementation of ADALINE-Based Speed Controller in a Two-Mass System

The paper presents the application of an adaptive neural controller used for speed control of electrical drives with elastic joint. The described project is realized in CompactRIO controller (cRIO-real-time embedded controller with reconfigurable input and output modules) equipped with an FPGA chip. The proposed speed controller is based on Adaptive Linear Neuron (ADALINE) model with on-line updated weights coefficients. The main advantages of the tested controller are simplicity and a reduced number of parameters for selection in the design process. Several stages of the real implementation are described. The two-mass drive system is modeled using the main processor of the cRIO, to emulate the real system, while the structure of the ADALINE model and its adaptation law are implemented in the FPGA module. Thus, hardware in the loop simulation is obtained. The obtained results present correct speed control with high dynamics and show the influence of the adaptation coefficient of the ADALINE-based controller on drive transients. Except for this the robustness of the proposed controller against changes of mechanical time constant of the load machine is presented.