Robust Optimal Control for Precision Improvement of Guided Gliding Vehicle Positioning

In this paper, a new method for controlling and guidance of guided gliding vehicle (GGV) is provided. In this regard, a hybrid structure of nonlinear optimal control has been proposed to minimize control effort. The inner loop has a regulating function that guarantees the stability of motion and rotation equations and reduces the effect of external disturbances. The outer loop provides optimal tracking with a straight line scroll criterion for the GGV. The use of state dependent Riccati equation control involves determining the appropriate state dependent coefficient (SDC) form, which requires a complete understanding of the dynamics of the system. One of the methods for determining the relative dynamics of a nonlinear time-varying system and calculating SDC is the online identification method. The advantage of this method in determination of the SDC is more evident when it is not possible to fully understand the dynamics of the system or external factors and disturbance affect the system. This method also eliminates the problem of uncertainty and accurate measurement of parameters. In other words, it provides an adaptive model for different conditions. In this paper, the ANFIS network has been used for the first time for continuous and online identification of the system dynamics and calculating SDC. In order to ensure the stability of identification, the identifier will be trained first with the particle swarm optimization and the online back-propagation learning algorithm. The stability of the closed loop system with a proposed hybrid structure is investigated with the help of the Lyapunov function and the concept of passivity. The optimal and robust performance of the proposed framework has been investigated in terms of the ability to simultaneously pursue the target in the optimal path despite disturbance and optimize the control effort with multiple simulations.