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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.

This article presents a Robust Servo Linear Quadratic Regulator (RSLQR) method based on the Linear Extended State Observer (LESO), which solves the problem of optimal tracking control of electro-optical system under disturbance. For the problem that the accuracy of the electro-optical tracking system is reduced due to disturbance and modeling error, the LESO is adopted in this article to obtain and compensate the influence of internal and external disturbances in the controlled object model, so as to improve the disturbance suppression ability of the electro-optical tracking system. Considering the influence of weighting matrix in the RSLQR method on system's dynamic response performance, this article presents a selection criterion of weighting matrix in the RSLQR to meet the performance requirements of closed-loop system. Through simulation analysis and experimental verification, the design method proposed in this article has great reference value for the optimization of the dynamic response performance of the electro-optical tracking system and the improvement of the disturbance suppression ability.

Transition metal selenides have the leading position in the field of energy storage and conversion due to their high theoretical capacity, good electrical conductivity, and cycling stability. Nickel is widely used for the construction of positive electrodes in devices due to its good conductivity, variable valence state, and ideal redox activity. NiSe materials have high internal resistance and are prone to volume change during charging and discharging, thus affecting the practical application of this electrode material, and the reported NiSe materials have not achieved a more desirable capacity value. Therefore, in this study, N, P-NiSe nanoelectrode materials were prepared using nickel foam as the nickel source and hexachlorocyclotriphonitrile as the nitrogen and phosphorus dopant using an efficient, energy-saving, and simple microwave method. It was also characterised by XRD and XPS to confirm the successful preparation of N, P-NiSe materials. In addition, the material yielded a high capacitance value (3184 F g−1) and good cycling stability (72% of the initial capacitance value was retained after 4000 cycles) in electrochemical tests. To demonstrate its excellent suitability for practical applications, an asymmetric supercapacitor was assembled using N, P-NiSe as the anode and activated carbon as the cathode. At an operating voltage of 1.6 V, the device achieved an energy density of 289.06 Wh kg−1 and a power density of 799.26 W kg−1 and retained 80% of its initial capacity after 20,000 cycles.

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

This study concerns the problem of integrated optimization of structure and control based on a fast steering mirror, aiming to achieve simultaneous optimization of the mechanical structure and control system. The traditional research and development path of the fast steering mirror involves a lengthy process from the initial design to the final physical manufacture. In the prior process, it was necessary to produce physical prototypes for repeated debugging and iterative optimization to achieve design requirements, but this approach consumes a significant amount of time and cost. To expedite this process and reduce unnecessary experimental costs, this study proposes an integrated optimization of structure and control (IOSC) method. With the use of IOSC, it is possible to achieve simultaneous optimization of structure and control. Specifically, the use of non-dominated sorting genetic algorithm II (NSGA-II) obtains globally optimal controller parameters and mechanical structure parameters under certain performance indices. This achieves an effective balance between the resonance frequency generated by the system and the working bandwidth, providing a high-precision reference for the research and development of fast steering mirrors.

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.

Grazing is the main way of utilizing understory vegetation in the tropics. However, the effects of grazing on vegetation diversity and soil functions in coconut plantations remain unclear. Therefore, this study was conducted in a young coconut plantation that was grazed by geese in Wenchang, China. We identified four grazing intensities according to the aboveground biomass, namely, no grazing (CK), light grazing (LG), moderate grazing (MG), and heavy grazing (HG). In April 2022, we used the quadrat method to investigate the composition and traits of vegetation, collected and analyzed 0–40-cm soil samples in each grazing intensity. The results showed that grazing changed the composition of understory species. The predominant species changed from Bidens pilosa to Praxelis clematidea + Paspalum thunbergii and then to P. clematidea with increasing grazing intensity. The richness, Shannon-Wiener index, evenness, modified functional attribute diversity (MFAD), functional divergence (Fdiv), and functional evenness (Feve) of CK were 4.5, 1.0, 0.29, 0.20, 0.84, and 0.80, respectively. Taxonomic diversity did not respond to LG, but responded significantly to MG and HG. Compared with CK, MG and HG increased richness by 96% and 200%, respectively, and Shannon-Wiener index increased by 40% and 98%, respectively. HG increased evenness by 95%. For functional diversity, MG and HG increased MFAD by 164% and 560%, respectively, but Fdiv and Feve did not respond to grazing intensity. The carbon (C) functioning, nitrogen (N) functioning, phosphorus (P) functioning, and multifunctionality in the 0–10-cm topsoil of CK were −0.03, 0.37, −0.06, 0.20, and 0.14, respectively. Grazing increased C functioning, P functioning, and multifunctionality in the 0–10-cm topsoil but decreased N functioning. Multiple linear regression showed that the taxonomic diversity and functional diversity could be used to estimate soil functions, but these vary among soil layers. In general, MG and HG can increase vegetation diversity and soil function. It may be possible to promote even distribution of geese by adding water sources or zoning grazing. Furthermore, quantitative grazing experiments are needed to determine the efficient use pattern of the understory in coconut plantations in tropics.

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.

Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters.

Background: The optimal approach for perioperative pain management in laparoscopic gynecological surgery is unclear due to a lack of comprehensive analysis, which limits the development of evidence-based enhanced recovery after surgery protocols. This study aimed to conduct a systematic review and network meta-analysis to support clinical decision-making for optimal analgesia. Materials and methods: This study conducted a systematic literature search in PubMed, Embase, CENTRAL, Web of Science, and CINAHL from inception to 3 December 2021, and updated on 19 August 2022. Randomized controlled trials comparing the perioperative use of nonopioid analgesics and regional techniques in adults undergoing elective laparoscopic gynecological surgery under general anesthesia were included in the analysis, either alone or in combination. The co-analgesic interventions during the perioperative period for the intervention and control groups of each eligible study were also considered. We assessed the risk of bias using the Risk of Bias 2 tool and evaluated the certainty of evidence using the Confidence in Network Meta-Analysis (CINeMA) approach. A Bayesian network meta-analysis was used to estimate the efficacy of the analgesic strategies. The primary outcomes were pain score at rest and cumulative oral morphine milligram equivalents at 24 h postoperatively. Results: Overall, 108 studies with 9582 participants and 35 different interventions were included. Compared with inert treatments, combinations of two or more interventions showed better efficacy and longer duration in reducing postoperative pain and opioid consumption within 24 h than monotherapies, and showed stepwise enhanced effects with increasing analgesic modes. In combination therapies, regional techniques that included peripheral nerve blocks and intraperitoneal local anesthetics, in combination with nonopioid systemic analgesics, or combining local anesthetics with adjuvant drugs, were found to be more effective. Monotherapies were found to be mostly ineffective. The most effective peripheral nerve blocks were found to be ultrasound-guided transversus abdominis plane block with adjuvant and ultrasound-guided quadratus lumborum block. Conclusions: These results provide robust evidence for the routine use of regional techniques in combination with nonopioid analgesics in perioperative pain management. However, further better quality and larger trials are needed, considering the low confidence levels for certain interventions. [ABSTRACT FROM AUTHOR]