Piecewise Affine Model Identification and Predictive Control for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit

In this paper, fully considering the form of the nonlinear model after linearization at the steady-state point, an improved subspace identification method based on the steady-state points' deviations data was proposed to identify piecewise affine model. Besides, the construction of the excitation signal in practical applications is fully considered. The identification results demonstrate that the method has better adaptability to strong nonlinear systems. The identification normalized root mean square error of each working condition is almost all less than 10%. On this basis, a framework that is widely applicable to complex system control is established combining with the mixed logic dynamic model. The canonical form realization is performed to transfer the local models into the same state basis. Predictive control was carried out on the boiler-turbine system of the 660-MW ultra-supercritical circulating fluidized bed unit based on the above framework. The results indicate the effectiveness of the framework.