Indirect neuroadaptive control of unknown MIMO systems tracking uncertain target under sensor failures

The problem of tracking a moving target with unknown trajectory is interesting and nontrivial. The underlying problem becomes even more challenging if uncertain dynamics and sensor failures are involved. This work presents an indirect adaptive neural network control strategy capable of making uncertain multi-input multi-output nonlinear systems track a moving target with uncertain trajectory closely despite sensor faults. An analytical model is proposed to allow the estimated (predicted) target trajectory to be linked mathematically with the actual disguised (polluted) target trajectory, thus facilitating the control design and stability analysis. A barrier Lyapunov function based design technique is employed to ensure that the inputs to the neural network remain within the compact set such that the neural network unit maintains its learning/approximating functionality during the entire process of system operation. The proposed control scheme guarantees the boundedness of all the closed-loop signals and the uniformly ultimately bounded stable tracking. Numerical simulation results also confirm the effectiveness of the proposed neuroadaptive tracking control method.