Optimal neuro-controller in longitudinal auto-landing of a commercial jet transport

In the last three decades, optimality-based auto-landing designs have been considered to the most effective way by many authors. However, it is known that the straight forward solution to the optimal control problem leads to Two Point Boundary Value Problem (TPBVP) (Riccati equation), which is usually too complex in solution, backward in the time, and real-time onboard implementation, or the final time, as a boundary condition, may also not be known precisely. To avoid these problems, first, a suboptimal solution by assuming t_f→∞ has been considered and its inapplicability has been discussed. Then an optimal controller for landing phase of a typical commercial aircraft has been designed. Finally, seven neural networks were being trained to learn the costates of the system to estimate the costates in similar scenarios without using the final time value, which usually is needed in solving the optimal control problems.