Performance-adaptive control system for a hammerstein system using GPGPU

In this study, a nonlinear system is controlled using a linear adaptive method. A nonlinear system is approximated a linear model at each operating point, and a control law is designed based on the approximated linear model. To obtain a suitable linear model at each operating point, many linear models are simultaneously identified. However, the computation load for identifying many models is considerably heavy. Hence, many linear models are identified using General-Purpose computing on Graphics Processing Units (GPGPU). In this study, the assessment of modeling performance is newly introduced. As a result, the control system is updated only when modeling performance is degraded, and frequent update of a control law can be avoided. Finally, numerical results are shown to demonstrate the effectiveness of the proposed method.