Data-Driven Hybrid Parametrically Optimized Adaptive Control Framework for Enhancing Stability Boundaries of Nonlinear Systems

Nonlinear behavior is exhibited by many real-world processes (e.g., power grids, mechanical processes, such as power plant cooling systems). For such processes, depending on the characteristics of the system, various control strategies have been used. However, a single control method alone may not always provide optimal stable performance, based on the process conditions. This article proposes a hybrid adaptive control architecture with a method to improve system knowledge. The proposed hybrid control architecture is aimed to provide improved performance for a nonlinear process during transient conditions via an adaptive control method with improved system knowledge that produces optimized performance. The hybrid adaptive control technique combines controllers with separate cost functions, each desirable during different process conditions to leverage the strengths of each control method, with a method to improve system knowledge for input to the adaptive control functions. In this article, the proposed control architecture methodology (e.g., mathematical model) is detailed first and then the stability of the proposed method is evaluated. For validation of the proposed control method, an illustrative example is then provided based on a real-life nonlinear system (e.g., pressurizer pressure control). The representative test case is detailed and the performance results are compared (e.g., conventional and other adaptive control methods). It is demonstrated that the proposed control method reduces the peak time and overshoot by 27% when compared to a linear quadratic regulator acting alone.