Adaptive finite-time disturbance-observer-based fault-tolerant control for output-constrained PMLM system with unknown dead-zone input and uncertain control coefficient

This paper investigates the disturbance-observer-based finite-time fault-tolerant control problem for an output-constrained permanent magnet linear motor system characterized by unknown dead-zone inputs and uncertain control coefficients. To address this challenge, a set of novel parameter functions, along with corresponding adaptive control laws, are developed as the primary means to estimate the unknown dynamic effects. Furthermore, a radial basis function neural algorithm is used to approximate the nonlinear composite friction where a proportional-differential-based gradient descent accelerator is embedded to fasten the convergence speed of the neural network weight. Additionally, uncertain disturbances are mitigated using a modified disturbance observer equipped with an adaptive compensation law, which effectively eliminates compensation deviation. In conclusion, we present rigorous proof procedures, supported by extensive simulation experiments, to validate the effectiveness and feasibility of the proposed theory.