State Tracking of Uncertain Dynamic Systems with Actuator Failures Using Model Based Adaptive Control

This paper aims at developing a model based adaptive control scheme to uncertain dynamical systems with actuator failures such that the states of the feed-backed controlled system track the states of the reference model asymptotically, and closed loop stability is achieved even for the case of existences of both parameter uncertainties and actuator failures. Under the knowledge of the nominal plant parameters without any uncertainties, first a desired reference model is designed by an optimal state feedback tracking control approach. Then, under what conditions the reference model and actual plant having uncertainties and actuator failures perfectly match with each other are derived. Using this knowledge, a novel Lyapunov function candidate in terms of tracking state error and controller parameters misalignments is considered to drive the adaptive laws making sure the robust asymptotic stability and reliable performance in all circumstances, even in abruptly changing rough environment conditions, when uncertainties and failures are all bounded and do not violate the perfect matching and invariance conditions. Finally, the effectiveness on the dynamical performance of our control strategy has been tested and verified by various simulation studies carried out on flight control of an aircraft with both actuator failures and parameter uncertainties