A fixed-time adaptive optimal parameter estimation (FxT-AOPE) scheme is proposed to address the difficulties in estimating dead zone parameters and slow convergence speed of tracking errors in permanent magnet synchronous motor systems. First, the continuous piecewise linear neural network is used to model the nonlinear dead zone dynamics. Second, an auxiliary filter is constructed to extract estimation errors, and this filter is used to drive an adaptive law with time-varying gain, minimizing the cost function of estimation errors and achieving adaptive optimal parameter estimation (AOPE). Then, the AOPE method is introduced into the fixed-time non-singular terminal sliding mode control (Fx... More >