Uncertainty and time delay are common occurrences in practical engineering applications, inevitably imposing constraints on the stability and reliability of disturbance observers (DOs). This paper presents a Nyquist-based stability criterion method for DOs. Theoretical analysis demonstrates that the stability domain defined by the small-gain theorem is a proper subset of the stability domain determined by the Nyquist criterion. Additionally, an experimental measurement approach for the disturbance rejection stability margins is proposed, providing a quantitative metric for DO stability and effectively guiding parameter tuning under uncertainty and time delay. The proposed methods significantly mitigate the potential instability risks associated with closed-loop disturbance rejection, thereby ensuring the operational safety of DO systems in engineering environments.

Given that traditional model predictive control (MPC) suffers from limited dynamic performance or a heavy computational burden, this paper proposes an efficient MPC method with optimized duty cycle error for dual three-phase synchronous motors. First, an unconstrained modulating signal is derived to minimize current tracking errors. Then, based on four active voltage vectors, a cost function is formulated using both the unconstrained and optimized modulation signals, leading to a quadratic programming (QP) problem with linear bound constraints for determining the optimal duty cycle. Next, an efficient computational procedure for solving the QP problem using the primal-dual interior-point algorithm (IPA) is presented in detail. Finally, the proposed method is experimentally validated on a dual three-phase permanent magnet synchronous motor (PMSM), with comparisons against conventional field-oriented control (FOC) and MPC in terms of transient and steady state, demonstrating its superiority.

Electro-optical tracking systems have been widely used in the cutting-edge domains of free space environment detection and communication owing to their exceptional performance. However, external disturbances often significantly impact the working accuracy of these systems. As their scope of application continues to broaden, increasingly complex operating conditions introduce more intricate environments and disturbances. This paper introduces a composite control structure of an enhanced error-based observer, rooted in the repetitive control strategy, tailored for two types of complex disturbances: periodic harmonic disturbance and narrow-band peak periodic disturbance. This structure not only ensures the system’s stability, but also suppresses periodic disturbances across multiple frequencies, effectively addressing the challenge that current disturbance suppression methods face in mitigating complex periodic disturbances. Moreover, necessary proofs are provided and an experimental platform is established for the electro-optical system, demonstrating the efficacy and reliability of the proposed control methods under various conditions.

While augmenting the observer gain can diminish the estimation errors of the extended state observer (ESO), it inevitably leads to an amplification of the observing noise, thus creating the trade-off between high-gain observation and noise sensitivity. To address the trade-off, this paper introduces the disturbance-compression extended state observer (DC-ESO) from a frequency-domain perspective. By employing disturbance compression, the DC-ESO modifies the disturbance characteristics encountered by the observer, effectively diminishing the upper bounds of the disturbance and its derivative. Frequency-domain analysis indicates that the DC-ESO necessitates substantially lower observer gain than the ESO while maintaining the same estimation errors. The principles of disturbance compression and its effects on disturbance estimation and noise amplification are elucidated, revealing that the DC-ESO effectively alleviates the inherent conflict between high-gain observation and susceptibility to noise. The parameter tuning methodology and frequency-domain stability criteria are user-friendly for practicing engineers. The effectiveness of the proposed approach is empirically verified through its application to an electro-optical tracking system.

The parameter and model uncertainties are inevitable in modern industrial systems. To address this problem, a digital-twin-based model-free approximated optimal trajectory tracking control strategy is proposed in this article for the photoelectric tracking system (PTS). With the online operational data, the digital replica of the physical PTS is dynamically established based on the designed parameter learning law to estimate the model parameters iteratively for the subsequent control optimization. To prevent the direct solving of Hamilton–Jacobian–Bellman differential equation, the performance index in this article is estimated by employing the Taylor series expansion, and a computationally efficient control law with exponential convergence is explicitly derived based on the projection neural controller for model-free approximated optimal control of PTS. Additionally, comprehensive numerical simulation and experiments on a laboratory prototype of PTS verify the excellent performance and practical feasibility of the proposed method even in scenarios of serious perturbations.

In this paper, we investigate the remote robust state estimation problems for nonlinear cyber-physical systems under denial-of-service attacks. The well-known extended Kalman filter is a commonly used method for state estimation of nonlinear systems. However, it does not take into account unmodeled dynamics or parametric uncertainties caused by first-order approximations, which makes its estimation performance unsatisfactory. In addition, denial-of-service attacks prevent the sensor from sending measurements to a remote state estimator by congesting the communication channel, which further deteriorates the estimation performance. To surmount these problems, a robust state estimation algorithm is developed based on sensitivity penalization, taking into account an explicit packet loss parameter. Under certain conditions, the boundedness of estimation error is proved. By selecting a negative resistance oscillation circuit for numerical simulations, it is demonstrated that the remote robust state estimator can markedly improve the estimation performance.

This article presents two computationally efficient methods for selecting the optimal modulated voltage that can achieve superior dynamic performance for surface-mounted permanent magnet synchronous motors (SPMSMs). Specifically, when an SPMSM suffers a large reference or sudden load change, the controller might command a voltage reference, which is beyond the range of voltages that a modulator can synthesize. In such cases, the transient behavior of the motor can deteriorate when the demanded voltage is not properly limited to the voltage boundary. To address this issue, a simple overmodulation method based on common-mode-saturation injection (CMSI) is proposed. This strategy comes with very low computational cost and can easily find the voltage vector on the boundary, which is nearest to the reference voltage vector. Moreover, an alternative control method, referred to as quadratic program (QP) based deadbeat (DB) control, is proposed that also ensures optimal system performance during overmodualtion. According to this strategy, the control problem is formulated as a constrained QP, which is solved with an efficient solver based on an active-set method. Finally, extensive simulative and experimental investigations for an SPMSM are presented to demonstrate the effectiveness of the proposed overmodulation methods.

This article presents a hybrid nonsingleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high-precision photoelectric tracking system (PTS) to improve state estimation performance in the complex case of measurement noise change and velocity state mutation. The proposed H-NFSTKF is composed of three parts: A strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a nonsingleton FLS (NFLS). To compare different nonsingleton firing strength approaches, standard method (Sta-NS), centroid-based method (Cen-NS), similarity-based method (Sim-NS), and subsethood-based method (Sub-NS) are discussed. In addition, two traditional control strategies, i.e, dual closed-loop and feed-forward control, as well as four filtering methods including Kalman filter (KF), STKF, fuzzy STKF (FSTKF), and single nonsingleton FSTKF (S-NFSTKF) are also designed to show the superiority of the proposed H-NFSTKF. Finally, by means of comparative simulation analyses and experimental results, the excellence of the proposed H-NFSTKF is verified.

Abstract In wireless sensor networks, dynamic environmental changes may lead to the distribution of some sensors becoming very dense, which leads to the waste of communication resources in distributed estimation. In this paper, it is proposed that Event‐triggered distributed dynamic adaptive‐neighbour selection LMS (ET‐dyANDLMS) algorithm can effectively solve the problems mentioned above. On the one hand, the network communication resources are reduced by the adaptive‐neighbour selection. On the other hand, the network data redundancy is reduced by the event‐trigger mechanism. From a theoretical point of view, the algorithm converges stably in mean meaning and mean square meaning, and the communication cost is reduced compared with other distributed algorithms. The theoretical analysis results verified by experimental simulation demonstrate the effectiveness of the proposed algorithm.

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins1

Provides an abstract of articles to be presented in a forthcoming issue.