An adaptive fault tolerant tracking control scheme is proposed for a class of multi-input single-output (MISO)
minimum phase systems with actuators lock-in-space or/and variant actuator failures. Adaptive algorithm is used to estimate
system uncertainties, and neural network is used to approximate actuators unknown variant failures. Then, tracking control
problem is solved for the system with actuators combinatorial failures. The control law proposed can guarantee that
the closed-loop systems are stable, all the states are bounded, and the tracking errors are uniformly ultimately bounded.
Simulation results show the effectiveness of the proposed method.