Optimal output feedback control of a class of uncertain systems with input constraints using parallel feedforward compensator

In this paper, an output feedback control approach based on online optimization for a class of constrained-input systems with uncertainties is presented. For this propose, the system is augmented with the aid of a Parallel Feedforward Compensator (PFC), which is designed based on an inaccurate linear model. The proposed method is performed by placing the transmission zeros of the augmented linear system at the left-hand side of the complex plane, creating a systematic and LMI-based procedure. It is worth noting that the method improves many commonly adopted restrictive assumptions on the linear model and is applicable for a class of nonlinear systems as well. In the augmented system, the augmented output is considered to form the external states, while the other linearly independent variables constitute the internal dynamics. The control signal is derived by finding online solution of a Quadratic Programming (QP) problem. This problem regulates the augmented output over a constrained input space, producing an optimal control law. Online solution of the QP is computed using a Recurrent Neural Network (RNN). Guaranteed stability and rapid convergence to the optimal solution are among advantages of such networks, which make the proposed control method more reliable. Furthermore, boundedness of the internal states during regulation of the external states is ensured in the presence of a class of uncertainties and nonlinearities. Effectiveness of the proposed method illustrated using a simulating example. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.