Transient-State Adaptive Optimal Control of Aircraft Engine Systems With Input Saturation

Abstract

Control for transition-state dynamics of aircraft engine systems is one of the foremost challenges in the field of aerospace engineering. Herein, the primary challenge lies in how to design transient-state control strategies to achieve the rapid and safe state transition of an aircraft engine, especially when the wide-range dynamics of the engine system are unknown. In this article, a data-driven adaptive optimal control strategy is proposed for the aircraft engine systems with input saturation constraints. Through the application of the Bellman optimality principle, the task of achieving optimal control is reformulated as solving the Hamilton–Jacobi–Bellman (HJB) equation. Following this, by introducing an $\epsilon$-optimal method and a basis function approximation approach, a data-driven adaptive dynamic programming algorithm that can handle input saturation is designed to solve the HJB equation. By converting the dynamic optimization problem into a static constrained optimization problem and solving it iteratively, the algorithm presented can efficiently update the optimal control strategy. Finally, a comprehensive simulation was carried out on the JT9D engine simulation platform, a nonanalytic and nonlinear virtual prototype model, to assess its practical applicability. The obtained results reveal that the proposed design can achieve promising transition performance and ensure that critical parameters of the system remain within a reasonable range.