Genetic Programming in Application to Flight Control System Design Optimisation
Anna Bourmistrova, Sergey Khantsis · InTech eBooks · 2010
In this chapter, an application of the Evolutionary Design (ED) is demonstrated. The aim of the design was to develop a controller which provides recovery of a fixed-wing UAV onto a ship under the full range of disturbances and uncertainties that are present in the real world environment. Evolutionary computation is an attractive and quickly developing technique. Methodological shortcomings of the early approaches and lack of intercommunication between different EA schools contributed to the fact that evolutionary computation remained relatively unknown to the engineering and scientific audience for almost three decades. Despite computational demands, evolutionary methods offer many advantages over conventional optimisation and problem solving techniques. The most significant one is flexibility and adaptability of EAs to the task at hand. In this study, a combination of the EA methods is used to evolve a capable UAV recovery controller. An adapted GP approach is used to represent the control laws. However, these laws are modified more judiciously (yet stochastically) than commonly accepted in GP and evolved in a manner similar to ES approach. One of the greatest advantages of developed methodology is that minimum or no a priori knowledge about the control methods is used, with the synthesis starting from the most basic proportional control or even from `null' control laws. During the evolution, more complex and capable laws emerge automatically. As the resulting control laws demonstrate, evolution does not tend to produce parsimonious solutions. The method demonstrating remarkable robustness in terms of convergence indicating that a near optimal solution can be found. In very limited cases, however, it may take too long time for the evolution to discover the core of a potentially optimal solution, and the process does not converge. More often than not, this hints at a poor choice of the algorithm parameters. The simulation testing covers the entire operational envelope and highlights several conditions under which recovery is risky. All environmental factors--sea wave, wind speed and turbulence--have been found to have a significant effect upon the probability of success. Combinations of several factors may result in very unfavourable conditions, even if each factor alone may not lead to a failure. For example, winds up to 12 m/s do not affect the recovery in a calm sea, and a severe ship motion corresponding to Sea State 5 also does not represent a serious threat in low winds. At the same time, strong winds in a high Sea State may be hazardous for the aircraft. On the whole, Evolutionary Design is a useful and powerful tool for complex nonlinear control design. Unlike most other design methodologies, it tries to solve the problem at hand automatically, not merely to optimise a given structure. Although ED does not exclude necessity of a thorough testing, it can provide a near optimal solution if the whole range of conditions is taken into account in the fitness evaluation. In principle, no specific knowledge