Integrated 3‐D Flight Trajectory Tracking Control with Aerodynamic Constraints on Attitude and control Surfaces

Abstract

In this paper, a new 3‐D trajectory tracking problem for an uncertain high fidelity six‐degree‐of‐freedom (6‐DOF) aerodynamic system is considered. Instead of designing controllers for each subsystem separately, an integrated trajectory tracking control algorithm is proposed to exploit beneficial relationships among interacting subsystems. The high‐order aerodynamic model is first transformed into a quasi‐strict‐feedback form. Then, backstepping technique is utilized to resolve the coupling effect problem of three control channels resulting from the bank‐to‐turn (BTT) control mode. In addition, command filters are introduced to handle state and actuator constraints caused by the physical limitations and the coordinated turn requirement. Furthermore, the uncertain aerodynamic force and moment coefficients are reconstructed by using the B‐spline neural network approximation and adaptive learning approaches. With Lyapunov stability analysis, all the states in the closed‐loop system are shown to be semi‐globally uniformly ultimately bounded (SUUB), and the tracking errors will asymptotically converge into a small compact set around zero by properly adjusting the control parameters. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

Three-dimensional continuous-time integrated guidance and control design using model predictive control

“…The nonlinear 6-DOF interceptor model equations considered in this paper are as follows 31 where φM, ηM, α, β, and γ are the flight path angle, heading angle, angle of attack, side-slip angle, and bank angle, respectively. ωx, ωy, ωz stand for the roll, yaw, and pitch rates.…”

Stabilization control of quadrotor helicopter through matching solution by controlled Lagrangian method

“…The advantage of the CL method lies in that it does not rely on unnatural linearization or decoupling [6]; thus, it provides smooth feedback control laws and maintains the Lagrangian structure of the closed-loop system [3]. Designed based on system energy, the control laws are with physical explains and simple for implementation, compared with the controller involving complicated derivative of virtual control by Backstepping method [7][8][9]. Besides, the control design does not rely on decomposition of the system, and the stability analysis can be deduced rigorously for the whole-state system [10], as opposed to the hierarchical control strategy, where the system is divided into inner-outer loop structure and the stability analysis is usually deduced approximately [11,12,30].…”