Disturbance observer-based neural adaptive stochastic control for a class of kinetic kill vehicle subject to unmeasured states and full state constraints

This paper proposes a disturbance observer-based neural adaptive stochastic control approach for the attitude control system of a class of Kinetic Kill Vehicles (KKVs) with unmeasured states and full state constraints. First, a one-one mapping is applied to transform the attitude control system with state constraints into a nonlinear novel system without any constraint. As a result, the control objective is changed into the boundness of the novel system states. Furthermore, the disturbances existing in the system are effectively estimated and eliminated by the nonlinear disturbance observer as well as the radial basis function neural networks (RBFNNs). Moreover, due to the dynamic signal, the dynamic uncertainty induced by the unmeasured states with an unknown dynamic is compensated appropriately. Utilizing the stochastic Lyapunov process, the boundness of all the signals in the system can be proven and the state constraints are satisfied. Finally, two groups of simulations are conducted, which demonstrate the remarkable performance of the proposed algorithm under different working conditions and highlight the advantages compared with existing studies.