This article presents a finite-time equivalent-input- disturbance (FTEID) approach that estimates the EID in a finite time. A finite-time state observer and a finite-time EID estimator ensure the finite-time convergence of the EID estimation and make improvement for the disturbance-rejection performance of a control system. The FTEID-based control system is divided into three subsystems for stability analysis and an algorithm is carried out to design gains of state feedback and finite-time observer. The effectiveness and superiority of the FTEID approach are verified by experiments of a Stewart-platform control system.

Vibration suppression is important for high-precision motion control because positioning accuracy is an essential figure of merit for a servosystem. The paper presents a vibration-suppression method based on improved adaptive optimal arbitrary-time-delay (IAOAT) input shaping. First, analyzing a conventional optimal-arbitrary-time-delay (OAT) input shaper yields an OAT input shaper with five pulses that completely suppresses two-mode vibrations when the natural frequency and damping ratio are exactly known. Next, embedding an adaptive algorithm in the shaper gives a conventional adaptive optimal arbitrary-time-delay (AOAT) input shaper that handles unknown natural frequencies and damping ratios. Note that the conventional AOAT input shaper contains five pulses that have to be updated, but the delay times of the pulses are prescribed. Then, the IAOAT method extends the symmetry of pulse amplitudes for zero damping ratio to a nonzero case and reduces the number of pulses to three. A recursive-least-squares (RLS) algorithm is devised to update these parameters. This shaper features the smallest number of parameters and high robust performance. Finally, comparisons with other input-shaping methods show the effectiveness and superiority of the developed method over others.

This paper presents a torque oscillation attenuation method for permanent magnet synchronous motors (PMSMs) based on the combination of sliding-mode control (SMC) and the equivalent input disturbance (EID) approach. To deal with the changes in PMSM parameters, we explored a continuous-domain ant colony optimization (CDACO) method to design a control system for such a plant. This is the first application of SMC-EID to uncertain PMSM plants, with CDACO enabling robust parameter tuning in continuous spaces. First, we designed an EID estimator to estimate the disturbance caused by torque oscillation. Next, we added the estimated disturbance to the sliding-mode controller to improve disturbance attenuation performance. Then, we extended an ant colony optimization (ACO) algorithm to the continuous domain to optimize controller parameters for an uncertain plant. Finally, a speed control experiment was carried out on a two-mass experimental system for PMSMs to demonstrate the validity of the method. The experimental results show that our method yields better control performance than the SMC.

This paper presents a vibration suppression method for two-inertia mechanical transmission systems, combining adaptive horizon model predictive control (AHMPC) with the equivalent-input-disturbance (EID) approach. First, conventional model predictive control (MPC) is extended to an adaptive horizon formulation to reduce computational cost while effectively attenuating vibrations. Second, a quadratic-programming-based method is designed to efficiently solve the AHMPC optimization problem. Third, the EID approach is integrated to compensate for external disturbances. Finally, simulation studies on a two-inertia system demonstrate the effectiveness of the proposed method in achieving significant vibration suppression.

Classification accuracy does not show how a surface electromyography (sEMG) classifier combines current and historical evidence. We propose Spatio-Temporal Sparse-Replacement Interaction (ST-SRI), a post-training audit that constructs a fixed-target, fixed-context current--history interaction spectrum from four channel-coherent replacement states and evaluates it with explicit falsification controls. Analytic checks localized known interactions in 119/120 windows within $\pm5$~ms, with 0/160 threshold-positive controls. We then audited 839 windows from fixed LSTM checkpoints for all 40 participants in NinaPro Database~2 and compared downstream diagnostic behavior with TimeSHAP and Dynamask on common temporal support. In a full-manifest learned-parameter falsifier, three independent within-tensor permutations yielded short-lag participant-mean peaks for 40/40 participants, although median positive spectral mass was only 0.0064--0.0077 of that of the trained models. Raising the peak-search lower bound from 5 to 20~ms reduced the trained-model short-lag count from 26/40 to 3/40. These results show that categorical peak locations can be dominated by weak residual spectra and detector boundaries. ST-SRI therefore provides a falsification-gated audit for determining when a temporal interpretation is model-specific, weak, or protocol-sensitive.