Comparison of Flight Control System Design Methods in Landing

The development and application of most present-day systems and control theory were spurred on by the need to resolve aerospace problems. This is roughly the problem of analyzing and designing flight control systems for tactical missiles or aircraft. The control laws used in current tactical missile or aircraft are mainly based on classical control design techniques. These control laws were developed in the 1950s and have evolved into fairly standard design procedures [1]. Current autopilot design processes contain time-and resource-consuming trial-and-error approaches. Especially late changes in the flight control laws contribute to high cost and delay of first delivery. The automatic landing mode development is a good example of a process with trial-and-error design phases, because of the many parameters the system has to be robust against. These parameters originate from different runway, terrain, ILS and weather characteristics, and from aircraft uncertainties like configuration and landing weight. Additional uncertainties arise from uncertain aerodynamic parameters, actuator model uncertainties, etc. The autonomous aircraft landing is an issue that implies three main aspects: the performance of equipment, the process models and the ethics. Generally, the landing is not a standard flight task as it could be thinking. We consider it a nonstandard flight stage because it has a very high sensitivity versus environment perturbation and to the psychological factors. In the last three decades, optimality-based designs have been considered to be the most effective way for a guided missile engaging the target [2-4]. However, it is also known from the optimal control theory that a straightforward solution to the optimal trajectory shaping problem leads to a two point boundary-value problem [2], which is too complex for realtime onboard implementation. Based on the reasons given above, advanced control theory must be applied to a control system to improve its performance. One of the best ways to solve this problem is to approach the artificial intelligence modeling technology based on fuzzy logic and neural network [5]. Intelligent control is a control technology that replaces the human mind in making decisions, planning control strategies, and learning new functions whenever the environment does not allow or does not the presence of a human operator. Artificial neural networks and fuzzy logic are two potential tools for use in applications in intelligent control engineering. Artificial neural networks offer the advantage of performance improvement