Neural network based optimal adaptive attitude control of near-space vehicle with system uncertainties and disturbances

Abstract

In this paper, a neural network based optimal adaptive attitude control scheme is derived for the near-space vehicle with uncertainties and external time-varying disturbances. Firstly, radial basis function neural network (RBFNN) approximation method and nonlinear disturbance observer (NDO) are used to tackle the system uncertainties and external disturbances, respectively. Subsequently, a feedforward control input under backstepping control frame with RBFNN and NDO is designed to transform the optimal tracking control problem into an optimal stabilization problem. Then, a single online approximation based adaptive method is used to learn the Hamilton–Jacobi–Bellman equation to obtain the corresponding optimal controller. As a result, the compound controller consists of feedforward control input and optimal controller which can ensure that the near-space vehicle attitude angles are able to track reference signals in an optimal way. Lyapunov stability analysis method is used to show that all the closed-loop system signals are uniformly ultimately bounded. Finally, simulation results show the effectiveness of the proposed optimal attitude control scheme.

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

“…For the PSPM system of aircraft ( 4) with the UADs and external disturbances, the NDOs are designed in the form of ( 9), (10), (11), and (12). Then, the estimation errors e H 23 > 0 and 𝜍 24 > 0 are the adjust parameters.…”

“…. By considering (9), (10), and (23), the derivative of q c with respect to t is ideally developed as…”

“…39 To solve the problem of nonzero equilibrium, the scheme of feed-forward and optimal feedback was introduced. 22,23 With the feed-forward, the nonlinear system with nonzero equilibrium is transformed into the form of zero equilibrium. And the optimal feedback for the transformed nonlinear system is designed by the ADP method.…”

Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

“…Substituting equations (21) to (23) and equations (26) to (28) into equation (25), the time derivative of V N can be rephrased as Hence, _ V N is negative and the neural network can keep stable as long as the parameters W jk , T ij , and j are updated as equation (21).…”

“…It is well known that the backstepping algorithm is widely employed to construct the adaptive neural controller. For the optimal attitude tracking control problem of the near-space vehicle, Xia et al 25 proposed the radial basis function neural network (RBFNN) and nonlinear disturbance observer (NSO) to estimate the system uncertainties and external disturbances, respectively. Based on the RBFNN and NSO, a backstepping method is designed to transform the optimal attitude tracking control problem into the optimal stabilization of an error system.…”