Search

The internal model control (IMC) has received extensive attention due to strong robustness, while it is challenging to tune the parameters of the controller. So far, strange nonchaotic optimization algorithm, based on grazing bifurcation, has been proposed to tune the parameters of the IMC controller in some recent progress, which processes a strong ability to escape from local optimization. However, the nonuniformity of the strange nonchaotic sequence distribution can result in insufficient optimization capability. In this paper, in order to obtain an excellent optimization effect, an improved strange nonchaotic optimization algorithm (ISNOA) based on grazing bifurcation is proposed to tune the parameters of the IMC controller. First, the boundary of a strange nonchaotic sequence is redefined by analyzing the strange nonchaotic sequence distribution. Furthermore, normalization function is applied to determine the optimization space. Consequently, the proposed ISNOA has better ability to escape from the local minima, higher stability and finer performance than the general SNOA, which is more suitable to tune the parameters of the IMC controller. Simulation results illustrate the effectiveness of ISNOA. In addition, for mismatch model, numerical simulations demonstrate the good performance of the control system based on ISNOA, meanwhile, the ISNOA is also demonstrated to be superior to particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]