<p>This article investigates the implementation of adaptive neural network event-triggered multi-tan-combined command-filtered control incorporating tan-function error compensation signals for multi-input multi-output nonlinear systems. An innovative integration of compensated state errors and tan-type barrier Lyapunov functions is introduced to prevent these errors from exceeding time-varying limits. Concurrently, by employing novel tan-based error compensation signals, output constraints are effectively enforced. An enhanced switching threshold event-triggered mechanism is designed to alleviate the communication load between the actuator and the controller. Subsequently, a neural filter state observer is designed, enabling the direct measurement of all states. Finally, a practical example is presented to illustrate the efficacy and superiority of the proposed method.</p>