A photovoltaic grid-connected inverter is a strongly nonlinear system. A model predictive control method can improve control accuracy and dynamic performance. Methods to accurately model and optimize control parameters are key to ensuring the stable operation of a photovoltaic grid-connected inverter. Based on the nonlinear characteristics of photovoltaic arrays and switching devices, we established a nonlinear model of photovoltaic grid-connected inverters using the state space method and solved its model predictive controller. Then, using the phase diagram, folded diagram, and bifurcation diagram methods, we studied the nonlinear dynamic behavior under the influence of control parameters on both fast and slow scales. Finally, we investigated the methods of parameter selection based on the characteristics of nonlinear dynamic behavior. Our research shows that the predictive controller parameters are closely related to the bifurcation and chaos behaviors of the grid-connected photovoltaic inverter. The three-dimensional bifurcation diagram can be used to observe the periodic motion region of the control parameters. After selecting the optimization target, the bifurcation diagram can be used to guide the selection of control parameters for inverter design. The research results can be used to guide the modeling, stability analysis, and optimization design of photovoltaic grid-connected inverters.

The Dung Beetle optimization (DBO) algorithm is an innovative and effective metaheuristic algorithm widely recognised for its excellent numerical optimization performance. However, DBO converges slowly and tends to fall into local optima due to the imbalance between exploration and exploitation, the lack of collaborative search capability and population diversity. To overcome these challenges, this paper proposes a dung beetle optimization algorithm based on multi-strategy collaborative enhancement (MDBO). The algorithm constructs a “search-enhance-escape” collaborative optimization framework through adaptive regulation and elite information sharing, and contains three major innovations: (1) a dual adaptive search strategy, which combines the adaptive contraction mechanism of the leader’s centre of mass and the brownian bidirectional crossover perturbation strength regulation, to enhance the population diversity and collaborative search ability of the dung beetle, achieving a dynamic balance between exploration and exploitation; (2) Elite Enhanced Solution Quality (EESQ) mechanism, which improves the quality of both the current local and global optimal positions and accelerates convergence through structured elite information and dual-phase adaptive perturbation; and (3) Dynamic Oppositional Learning (DOL), which introduces an asymmetric adaptive perturbation in the dung beetle’s foraging and Breeding phases and enhances the ability to escape from local optima. The three act synergistically to achieve a more efficient optimised search. The performance of MDBO is evaluated using the IEEE CEC 2017, CEC 2019 and CEC 2020 benchmarking functions. Compared to the DBO algorithm, the MDBO algorithm improves the convergence accuracy and stability on the CEC2017 benchmark functions by 60.91 % and 63.98 %, respectively. For the CEC2019 benchmark functions, the corresponding improvements are 54.47 % and 41.36 %, while for the CEC2020 benchmark functions, they are 50.71 % and 55.16 %, respectively. In addition, its overall performance is evaluated against two complex real-world engineering problems: UAV path planning and wireless sensor network coverage optimization. The experimental results show that MDBO provides very competitive optimization results compared to DBO, two highly referenced algorithms and five advanced algorithms.

In this paper, a new carrier phase-shift (CPS) modulation method based on switching the displacement angle (SDA) is proposed to compromise the harmonic content of the output voltage and the circulating current. It can be used in medium- and low-voltage applications where the AC-side voltage and DC-side current of the modular multilevel converter (MMC) are required to have low harmonic content simultaneously. In this proposed SDA-based CPS modulation, the carrier displacement angle of the MMC with N submodules in each arm is periodically switched between the values of 0 and π/N degrees, so that the harmonic content of the output voltage and the harmonic content of the circulating current will not be in extreme conditions, which occurs when the displacement angle is set to 0 or π/N degrees. The effectiveness of this method has been verified by simulation and experimental results.

In order to design a photovoltaic inverter with excellent performance, the second-order photovoltaic inverter circuit model under current loop proportional control is established in this paper, the discrete iterative model of system is also obtained. The nonlinear behavior of system is analysed in detail through bifurcation diagram, folding diagram and the phase diagram of output current (i) and output voltage (u _c). The simulation model is constructed in Matlab/Simulink, and the correctness of theoretical analysis is verified through time domain waveform and phase-space map. The influence of bifurcation and chaos on system spectrum is analysed from the frequency domain. According to the Jacobian matrix of system, the stable operation region of system is given. The experiment shows that the correct selection of circuit parameters is important for the stable operation of system.

Automated harvesting is pivotal to smart agriculture, yet precisely localizing picking points in complex orchard environments remains a key challenge for robotic harvesting systems. To this end, this study proposes a collaborative perception framework integrating a lightweight pose estimation model termed RepViT-DSConv-ARC-PoseHead enhanced YOLO (RDA-YOLO) with Segment Anything Model 2 (SAM2). RDA-YOLO is the lightweight component responsible for real-time fruit detection and keypoint localization, while SAM2 is adopted for fruit contour segmentation and occlusion assessment. Based on the YOLOv11n-POSE architecture, we introduce Distribution Shift Convolution (DSConv) to reduce computational overhead and design the C3k2_RepViT module to enhance multi-scale feature fusion. Crucially, a specialized Adaptive Regression Coordination Pose Head (ARC-PoseHead) is developed to replace traditional prediction structures, employing log-scale polar regression and subpixel refinement to significantly improve coordinate perception of jujubes and key points under complex backgrounds. RDA-YOLO performs real-time detection and locates key points such as the pedicel-fruit transition zone to calculate optimal harvesting angles, while SAM2 performs fine-grained contour segmentation based on detection boxes to determine occlusion status and establish harvesting priority. Experimental results show that RDA-YOLO achieves 93.9% precision and 94.9% mAP@50 for green jujube detection, while for key point detection, it attains a superior precision of 90.4% and a mAP@50 of 83.6%. Notably, the model achieves a lightweight design by reducing parameters to 2.490 million and computational load to 5.7 GFLOPs. Deployment feasibility was verified on the field robot control computer and a robotic platform using jujube fruit models, demonstrating the potential of the proposed framework for practical harvesting applications.

The value of the output capacitance (C) should be carefully considered when designing a photovoltaic (PV) inverter since it can cause distortion in the working state of the circuit, and the circuit produces nonlinear dynamic behavior. According to Kirchhoff's laws and the characteristics of an ideal operational amplifier for a strict piecewise linear state equation, a circuit simulation model is constructed to study the system parameters (time, C) for the current passing through an inductor with an inductance of L and the voltage across the capacitor with a capacitance of C. The developed simulation model uses Runge–Kutta methods to solve the state equations. This study focuses on predicting the fault of the circuit from the two aspects of the harmonic distortion and simulation results. Moreover, the presented model is also used to research the working state of the system in the case of a load capacitance catastrophe. The nonlinear dynamic behaviors in the inverter are simulated and verified.