The Variable Camber Continuous Trailing Edge Flap (VCCTEF) is a novel aircraft control system that intents to prevent undesired aeroelastic deflections by precise lift tailoring along the wing span. However, the unknown dynamics and increased complexity of the new hardware imposes difficulties to establish an optimal controller. One approach is to use Adaptive Critic Designs (ACD). Being part of reinforcement learning techniques, their intelligent and adaptive characteristics provide a fault tolerant solution to non-linear control problems. As a starting point, this paper compares the two fundamental forms of ACD's for aircraft control and evaluates their future potential for the VCCTEF design. The two forms are Heuristic Dynamic programming (HDP) and Dual Heuristic Programming (DHP). In an experimental study, both algorithms are integrated in a F-16 aircraft model. First, the agents are trained offline to learn to control the baseline F-16 dynamics. Then, the aircraft dynamics are changed online and the controllers need to adapt to the new plant dynamics. The results show that DHP has a higher success ratio in the offline learning phase and particularly converges faster to an optimal solution. During online simulations, both algorithms can deal with some changes in the F-16 aircraft dynamics even without adaptation, although HDP reveals more robustness in this case. DHP on the other hand, adapts better to changes in the plant dynamics when online learning is applied.