Unique abilities of humans such as adaptive behavior in dynamic environments, social interaction and moral judgment capabilities, make humans essential elements of many control loops, operating in close collaboration with autonomy.
Most existing human models do not consider human adaptive behavior. Inspired by humans’ ability to adapt to changing environments, the proposed adaptive human model mimics this ability despite input bandwidth deviations and plant uncertainties. The proposed human pilot model structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov Krasovskii stability criteria. Experimental data shows that the proposed model mimics human behavior in the presence of uncertainty.
Conventional control allocation methods distribute the total control effort among redundant actuators. These methods require identification techniques or persistent excitation assumption to handle actuator uncertainties. The proposed adaptive control allocation is able to distribute control signals among uncertain redundant actuators without any identification or assumptions on signals. Asymptotic stability of the proposed method is guaranteed.
Although optimization based control allocation methods are able to handle the actuator constraints, their computational complexity is high, specially when the number of actuators increases. The proposed control allocation method uses the degrees of freedom provided by the null space of the control matrix and restricts the control signals in a fixed time slot.