Data-driven adaptive control of wide-area non-linear systems with input and output saturation: A power system application

In this paper, a Data-Driven Adaptive Controller is proposed for wide-area Multi-Input and Multi-Output (MIMO) non-linear systems featuring input-output saturations. Complexity has always been a major concern in the modeling and control of real-world industrial processes. Traditionally, researchers look for simplified and enough precise models of these systems. However, with the significant advancement in sensor and communication technologies, a considerable amount of data is available from the process which may compensate for the lack of precise models. The emerging data-driven control systems perform both identification and control actions based on data collected from the process, making the system less dependent on a pre-identified model. In this context, an efficient MIMO Data-Driven Adaptive Control (MIMO-DDAC) is designed in this paper for stabilizing a class of unknown non-linear systems. A recursive modeling/control process is proposed for the MIMO systems in the presence of saturation in sensors and actuators, and its stability and performance are mathematically proved. This approach is applied to the load frequency control problem in an interconnected multi-area power grid in the presence of governor saturation, load and parametric uncertainties. Simulation results reveal the effectiveness of the proposed method.