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Abstract The linear power amplifier (LPA) suffers from low power supply utilization in the DC power supply mode, leading to reduced output efficiency. This paper constructs an efficient power supply mode based on closed‐loop dynamic power supply system (CLDPSs). The CLDPSs can generate a pair of bipolar dynamic power supplies based on a predetermined signal to provide power to LPA, which can maintain a small voltage drop between the power supply and the output of LPA, thereby improving the power supply utilization of LPA. In addition, the study also focuses on the conflicting relationship between the output ripple and the dynamic performance of the CLDPSs, for which a new parametric design of the LC filter circuit is proposed, aiming to achieve a balance between these two aspects. Finally, a voice coil motor driving platform is constructed for experimental testing. The results demonstrate that the output ripple and dynamic performance of the CLDPSs can be balanced. In comparison to the DC power supply mode, the CLDPSs power supply mode can enhance the efficiency of LPA by 2 times while the driving accuracy of voice coil motor is basically unchanged.

Abstract Aiming to improve the estimation and prediction accuracy of a target's position, this paper proposes a state estimation method for photoelectric tracking systems, based on the evaluation of the tracked target's motion intention. Traditional photoelectric tracking systems utilize external physical quantities such as the position, velocity, and acceleration of the target as the estimated states. While this method can output good results for pre‐modelled target positions, it struggles to maintain the accuracy when facing manoeuvering targets or complex motion patterns targets. Here, the relevant parameters of the tracked target's motion intention are directly estimated innovatively, like estimating the circling point position rather than the circular flying target's position and velocity. This approach enables recognizing the target's motion intention and leads to precise estimation, which specifically consists of an interacting multiple model approach, multiple unscented Kalman estimators, and a robust estimator. The effectiveness and stability of this estimator are validated through software simulations and experiments on a dual‐reflection mirror platform.

Abstract In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios.

Abstract Model predictive current control (MPCC) has been widely recognized as a high‐performance scheme for permanent magnet synchronous motor drives because of its simple control structure. This article proposes a novel multivector MPCC (MMPCC) method based on a hybrid control set, which includes both original basic voltage vectors (VVs) and synthesized VVs. The two active VVs and one null VV are considered as an initial control set, and the exact combination of them depends on their corresponding durations, which are calculated by stator current slopes in dq frame with the use of the prediction formula. Contrary to the traditional cost function, an alternative hybrid control set can achieve superior voltage precision. This is due to the involved cost function that minimizes the maximum of prediction current errors, which typically occur at non‐integer sample instants during the control period. The performance of the proposed MMPCC method has been experimented to confirm its effectiveness, compared with the conventional MPCC (CMPCC) and duty‐cycle MPCC (DMPCC). The results indicated an anticipated enhancement in both dynamic and steady‐state performance at a low rotor speed, alongside a significant robustness against parameter mismatch.

Abstract In the electro‐optical tracking system, due to the full occlusion of clouds or buildings, the traditional tracking algorithm fails to work effectively. In the authors’ study, a dual‐channel anti‐occlusion scheme based on image processing and trajectory prediction is proposed. On the one hand, the image‐processing unit plays a significant role in evaluating the occlusion state of the target. An improved image occlusion judgement method based on Bhattacharyya coefficient occlusion judgement is proposed, and the ability to resist background interference and rapidity of the algorithm is enhanced. On the other hand, according to the different occlusion states, the dual operation modes of tracking and prediction are usually set. When the target is completely occluded, the prediction mode based on a robust predictive filtering method is investigated to predict the real‐time trajectory of the target more accurately. Image occlusion judgement and trajectory prediction can solve the occlusion problems in tracking control. Otherwise, the feasibility and effectiveness of the proposed method are verified by experiments. The cover image is based on the Research Article A target anti‐occlusion method based on image processing and trajectory prediction in electro‐optical tracking system by Yao Mao et al., https://doi.org/10.1049/rsn2.12235.

Abstract In this study, a class of linear system, which is with no exogenous input and suffered from constant measurement delay and uncertain model‐parameter errors, is under consideration. To combat both the parametric uncertainties and constant measurement delay, a novel robust state estimator is proposed. Accounting for the constant measurement delay, a clever approach is utilised to expand the state vector and the system model is converted into an augmented delay‐free model. Considering the deterioration of estimation performance caused by stochastic model‐parameter errors, the sensitivity penalisation function of model‐parameter errors is defined and introduced into the objective function of the regularised least‐squares (RLS) problem, whose solution is the standard Kalman filter. Furthermore, by restricting the range of introduced parameter, the objective function of the modified RLS problem is converted into a strict convex function. Then, the recursive procedure of the proposed estimator is derived. The asymptotic stability conditions of the proposed estimator and the conditions for boundness of the estimation error matrix are given. Numerical simulations show the effectiveness of the estimator proposed in this paper.