Event‐triggering‐based robust optimal control for post‐stall pitching maneuver of aircraft

Abstract

In this paper, the problem of the post‐stall pitching maneuver of aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances (UADs). A nonlinear disturbance observer is employed to estimate the UADs in order to suppress their adverse effects. And the nonzero equilibrium adaptive dynamic programming (NEADP) control method derived from the reinforcement learning is adopted to achieve the optimal control of aircraft. Simultaneously, a critic neural network is constructed to approximate the value function in the Hamilton‐Jacobi‐Bellman equation so as to realize adaptivity. Furthermore, the event‐triggering (ET) scheme with an improved triggering condition is applied in the control method in order to decrease the deflection frequency of control actuator. In addition, the Lyapunov stability analysis method is used to prove that all signals in the closed‐loop control system are uniformly ultimately bounded. And the proof by contradiction is applied to confirm that the Zeno behavior is excluded with the analysis of the minimum dwell time. Finally, simulation results verify the effectiveness of the improved ET‐based NEADP control method.

Data‐based learning control for optimization of nonlinear systems

“…A data‐driven $$$ {\mathrm{H}}_{\infty } $$$ controller design method is studied for continuous‐time linear periodic systems 18 . The problem of the post‐stall pitching maneuver of an aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances 19 …”

“…11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third group of papers [13][14][15][16][17][18][19] focuses on robustness on data-based optimal learning control. A novel Nash game-theoretical optimal adaptive robust control design approach is proposed to address the constraint-following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory.…”

“…18 The problem of the post-stall pitching maneuver of an aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances. 19 The fourth group of papers [20][21][22][23] focuses on neural networks and deep neural networks learning methods for optimal control. An optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine is considered, and an intelligent real-time optimal control method based on deep neural networks is developed for the online tracking of the flow front position to improve the efficient production process of the plastics.…”