Grid-forming (GFM) converters are required to meet multiple operational and control demands in power systems. Under grid fault conditions, maintaining transient synchronization stability (TSS), suppressing fault overcurrent, maximizing fault current support, and preserving voltage source characteristics are all of critical importance. However, achieving effective coordination among these objectives remains challenging. To overcome these limitations, this paper proposes a systematic fault ride-through control strategy capable of rapid current limiting while ensuring the existence of a stable equilibrium point (SEP). Specifically, a novel concept of the GFM operational region is introduced, where the existence of the SEP is rigorously determined via a geometric interpretation, providing a solid stability foundation for the control design. Subsequently, a two-stage virtual admittance (VA) control strategy with accurate current-limiting capability is developed. This strategy employs a predominantly inductive steady-state VA for precise fault current regulation, while incorporating a transient virtual resistance to increase the resistive component of the transient VA, thereby dynamically suppressing current surges. Compared to conventional fixed virtual impedance methods, this approach achieves a balance between steady-state accuracy and transient suppression performance. Furthermore, a coordinated active and reactive power reference strategy is proposed to enhance TSS and ensure maximum fault current injection. Experimental results demonstrate that, the proposed control strategy exhibits superior comprehensive performance in maintaining system stability, achieving fast current limiting, and providing enhanced fault current support, validating its effectiveness.

Microbial fuel cells (MFCs) have received extensive attention in recent years as a new source of wastewater treatment. This paper aims to address the limitations of slow convergence rates and excessive overshoot in existing controllers by introducing a novel control strategy to attain rapid and stable convergence performances. Firstly, a control-oriented parameterized model for a dual-chamber system is established to address the limitations of low electricity generation and slow convergence of system state variables in single-chamber models. Then, a sliding mode controller with a novel reaching law is designed, and its fixed-time stability is proven by using Lyapunov stability theory. A principal advantage of this methodology resides in its convergence rate being unaffected by initialization states. Finally, an improved chaos game optimization algorithm is introduced to tune the controller parameters, ensuring acquisition of optimum parameter combinations within designated boundaries. Numerical simulations validate the model's reliability and the fixed-time sliding mode control scheme exhibits superior performance compared with the alternative algorithms concerning convergence rapidity, overshoot suppression, and steady-state precision. The dual-chamber model and fixed-time sliding mode controller provide technical support for further applications of MFCs in wastewater treatment.

Addressing the limitations of myopic greedy decision-making, frequent local coordination conflicts, and excessive redundant motion in conventional multirobot coverage (MRC) planning for unknown grid environments—tasks that are vital in applications from fire monitoring to automated cleaning—this article proposes a hierarchical decoupled multirobot coordination architecture (HDMCA) for efficient MRC with robust task allocation and conflict handling. At the macro level, a central, lightweight coordinator operates on a shared cognitive map and employs a connected-component workload quantification scheme to elevate task assignment from discrete cells to topologically coherent regions, enabling proactive decoupling of robot intents and dynamic region reallocation under failures or severe workload imbalance. At the micro level, each robot runs a unified utility-driven controller that integrates boustrophedon-style sweeping, global precise coverage, fine-grained residual clean-up, and exploratory escape behaviors, together with a negotiation-based collision-avoidance mechanism grounded in a concession-cost quantification rule to resolve local conflicts in high-density scenarios. Compared with existing monolithic or purely decentralized planners, the proposed HDMCA explicitly separates strategic region planning from tactical motion control, supporting scalable operation and conditional completeness under the stated assumptions. Simulation results on benchmark unknown environments show that HDMCA achieves complete coverage with a repetition ratio of 0.08, while reducing the total distance by approximately 4.4% and 16.9% compared with distributed complete coverage path planning (CCPP) and boustrophedon and backtracking (BOB), respectively, and maintaining practical execution times. A real-robot test on a physical platform further confirms the feasibility of the generated paths under practical motion constraints.

This paper examines how various terrains affect the kinematic performance of a quadruped robot using virtual model control (VMC). It emphasizes the importance of understanding the effects of the ground on stability and efficiency in complex environments. The study includes kinematic and dynamic models of quadruped robots, details on stance and swing phase strategies in VMC, and experimental tests on surfaces such as grass, marble, and snow. Results analyze VMC effectiveness and kinematic performance under different ground conditions, highlighting impacts on gait stability and energy efficiency.

To address the challenges of low efficiency and limited dynamic planning capabilities in multi-robot battery replacement tasks within unknown environments, we propose a charging scheduling algorithm that incorporates a graded charging threshold (GCT) function for enhanced multi-robot path planning. Initially, a raster map is employed to represent the operational area, and the region is covered using a full coverage algorithm that combines an obstacle-guided approach with a start point and backtracking mechanism (OSPGB). Next, the GCT function is integrated for optimization, enabling the robot to determine the optimal time for battery replacement. This approach helps prevent power loss and minimizes longer charging routes during the coverage process. Ultimately, simulation experiments are conducted across various environments, demonstrating that the proposed algorithm significantly outperforms the OSPGB algorithm in terms of the number of turns and path uniformity. Specifically, the number of turns is reduced by 7.8%, and the optimization rate for path uniformity reaches 94.2%.

In multi-robot systems performing regional coverage search tasks, reasonable task point allocation and path planning are essential to improve coverage efficiency and reduce travel distances. This paper proposes a multi-robot regional search planning method based on Voronoi partitioning and K-means clustering. First, the Voronoi diagram is used for the initial partitioning of the search area, and the Lloyd algorithm is applied iteratively to optimize task point distribution for greater uniformity. Subsequently, an adaptive path optimization approach is employed to determine the optimal number of clusters K for K-means clustering, minimizing the total path length. Finally, within each task region, the genetic algorithm (GA) is utilized to optimize robot paths, reducing path redundancy and enhancing task execution efficiency. Simulation results demonstrate that the proposed method effectively optimizes task point allocation, reduces path length, and improves search efficiency, making it suitable for multi-robot coverage search tasks in irregular regions.

The neuronal cell membrane has a bilayer structure, where the flexible material shows nonlinearity and can be simulated by a nonlinear resistor. The inner and outer membranes are modeled by two capacitors. In this paper, a nonlinear resistor connected with two capacitors simulates a nonlinear cell membrane. Based on the property of the magnetic flux-controlled memristor to perceive external magnetic fields, a novel functional double membrane neuron model with a nonlinear membrane is obtained. The dynamic equations of this neuronal circuit are established based on Kirchhoff's law, and the Hamilton energy function of the neuron is formulated following Helmholtz's theorem. The dynamics dependent on initial states are investigated, and the behavior of the functional neuron is explored both in the absence of external interference and under different magnetic field stimuli by using time sampling sequences, phase diagrams, Lyapunov exponents, and bifurcation diagrams. Furthermore, the energy distribution and self-regulatory capacity of the double membrane neuron under magnetic fields are also analyzed. The results demonstrate that complex firing patterns (spiking and chaotic) can be elicited in the double membrane neuron by adjusting the initial values of variables, circuit parameters, and magnetic field signals. These observed complex dynamics reflect the sensitivity of the model to external stimuli. This work offers novel insights and methodologies for neural network design and application.

To address the issue of high energy consumption resulting from poor synergy among multiple robots during full-coverage dynamic online planning in unknown terrain, this paper proposes a multi-robot coverage algorithm guided by the energy activity (EA) function. Additionally, a backtracking mechanism based on the terrain environment is incorporated. First, occupancy grid is employed to represent the area to be covered, with the local raster activity value function guiding the coverage of the working environment. Next, a terrain-based backtracking mechanism is incorporated into the algorithm to facilitate online collaboration among the robots and help them escape "dead zones," thereby preventing conflicts in backtracking areas and reducing the likelihood of lengthy backtracking paths. Finally, by simulating various scenarios that a cleaning robot may encounter in an unknown terrain environment, we compared the results with those of other algorithms and with scenarios that did not consider terrain factors. The experimental results demonstrate that accounting for terrain is more effective in reducing the robot's energy consumption. The experiments conducted in different situations highlight the benefits of considering terrain factors. Specifically, the average path length and the number of turns were reduced by 5.2% and 30.5% compared to the BOB algorithm, and by 3.1 % and 19.3% compared to the epsilon* algorithm. Thus, the feasibility and effectiveness of the proposed algorithm are confirmed.

Biological neural network (BNN) algorithms have become popular in coverage search in recent years. However, its edge activity values are weak, and it is simple to fall into a local optimum at a late stage of coverage. When applied to complex environments, the 3D BNN network structure has high computational and storage complexity. In order to solve the above problems, we propose an algorithm for multi-robot cooperative coverage of complex terrain based on an improved BNN. The algorithm models the complex terrain using a 2.5-dimensional (2.5D) elevation map. Combining the dual-layer BNN network with the 2.5D elevation map, we propose an elevation value priority mechanism. This mechanism lets the robot make elevation-based decisions and prioritizes higher terrain areas. The dual neural network's first layer plans the robot's path in normal mode. The second network layer helps the robot escape the local optimum. Finally, the algorithm's full coverage effect in complex terrains and the speed of covering high terrain are verified by simulations. The experiments show that our algorithm preferentially covers high points of the region and eventually covers 100% of complex terrain. Compared with other algorithms, our algorithm covers more efficiently and takes fewer steps than others. The speed of covering high terrain areas has increased by 34.51%.

Sliding mode control (SMC) because of its significant robustness. To ensure the stability of the missile system under uncertain disturbances, a sliding mode controller is thus constructed. Firstly, a sliding mode observer (SMO) is designed to estimate the state vector of the system. Subsequently, based on the state estimation, a sliding mode controller is constructed, and its validity is proven by the Lyapunov theorem. Compared with other controllers, the designed controller improves the robustness of the system and simplifies the sliding mode controller design, which is easier to implement in practical engineering. Finally, the SIMULINK model of the missile rigid-body system is established, and simulation experiments are carried out. The simulation results verify the effectiveness and practicality of the controller, thereby providing a universal scheme for the anti-disturbance and automatic control of other industrial devices.

The maze problem, as a classic path planning issue, has always attracted significant attention . This paper proposes an improved bio-inspired neural network method aimed at enhancing the efficiency of multi-robot collaborative search in unknown maze environments. First, an improved GBNN model is introduced by incorporating the signal transmission characteristics of biological neurons, ensuring that robots select the correct movement path within a limited sensing range based on neuron activation values. Furthermore, a wall-following mode is introduced to help robots escape from deadlock states. Finally, simulation experiments are conducted to validate the effectiveness of the proposed method.

The research on multirobot collaborative search in unknown 3-D environments, based on bio-inspired neural networks, holds significant value and importance. However, challenges arise in 3-D environments, including excessive turning and vertical movement, as well as the potential for collisions between robots. In response, we propose an improved Glasius bioinspired neural network (GBNN) that mitigates decision conflicts among robots and considers the impact of turning and vertical movement on robot decision-making. Furthermore, to address the issue of robots getting trapped in local deadlocks during the search process, we present a dual neural network algorithm based on Levy flights. In the method, dual GBNN based on Levy flight (LF-DUAL-GBNN) proposed in this article, robots obtain random target points through Levy flights and are then guided by a dual neural network to navigate to the vicinity of the target points, thus breaking free from local deadlock states. Finally, we conducted simulation experiments to validate the algorithm's effectiveness.

The control barrier function (CBF) is used as an effective tool to design the safety controls for constrained robotic systems. When both the control input and state constraints are involved, it is important to guarantee the CBF-based quadratic program (QP) is feasible. To address the constraint satisfaction and CBF-QP feasibility issues for robotic systems with saturation constraints both in control input and velocity state, in this article, we propose an augment CBF (ACBF), which is constructed by introducing a time-varying function to a typical ZCBF, and the time-varying function is treated as a ZCBF by introducing the auxiliary dynamics. To realize the safe tracking purpose for the robotic systems, the ACBF-based QP tracking control is designed by applying the computed torque control (CTC) as a nominal tracking control and the designed ACBF condition as the constraint of the QP problem. The performance of proposed ACBF-QP control method is illustrated by a two-link manipulator system.

The multirobot coverage search problem in unknown environments has attracted significant attention. However, the existing methods are inefficient in the search process. The aim of the present study is to improve the search efficiency through an enhanced bioinspired neural network method. In this work, a connected Glasius bioinspired neural network (CGBNN) model is introduced to address the lack of consideration for neuronal connectivity and transmission properties in existing studies. The dynamic search environment is represented by the changes in neurons' activity values, which guide the robots in performing the search task. Each robot automatically plans its search path according to the principle of the decreasing gradient of CGBNN activity values until the task is completed. Experimental results demonstrate that the robots can avoid different types of obstacles to complete the coverage search, confirming the effectiveness of the proposed method. Meanwhile, it indicates that the proposed method outperforms others, the coverage rate is improved by 6.90%, 6.22%, and 4.02% compared to the GBNN, A-RPSO, and DMPC algorithms, respectively. In adition, the decision time is less affected by the complexity of the environment, which fulfills the practical demands of real-time decision-making in a large-scale complex environment.

The impedance control of the constrained robotic systems with input saturation is investigated in this article. The High-order Control Barrier Function (HoCBF) is applied to address the state and/or output constraints for robotic systems, where the HoCBF-based controller can be obtained by a quadratic program (QP). Input saturation poses significant challenges to the solvability of the QP-based control framework and constraint satisfaction. To surmount these issues, we introduce a time-varying functions-based HoCBF (TFHoCBF), which is reconstructed by introducing time-varying functions to a typical To inherit the properties of the zeroing CBFs (ZCBF), the time-varying functions are treated as standard ZCBFs by introducing the auxiliary system to make sure the funcitons are non-negative. To enable robotic systems to comply with external contact forces when interacting with the environment, we design the TFHoCBF-based impedance control strategy. In this design, the impedance control is applied as the nominal control, while the designed barrier conditions are set as the QP constraints. To validate the efficacy of this proposed control method, we conduct simulations experiments using a two-link manipulator system.

This article introduces CIMAP, a high-performance motion planning algorithm for robotic manipulators in complex environments, based on the clearance inference network (CIN). CIMAP incorporates a batch collision estimation module powered by CIN, which efficiently predicts collisions by dividing the manipulator’s workspace into voxels and estimating clearances between the manipulator and surrounding obstacles. The algorithm also features a batch adaptive bidirectional expansion mechanism, enabling the simultaneous extension of multiple nodes within joint space. Leveraging CIN for batch collision estimation, CIMAP accelerates the discovery of feasible paths. Additionally, CIMAP includes a phased path optimization mechanism that identifies local shortcuts through CIN, improving path efficiency. A geometric collision checker ensures safety, performing necessary repairs when required. To assess CIMAP’s effectiveness in continuous motion planning, we compared its performance against four existing algorithms (CN-RRT, B-RRT, GB-RRT*, and NPB-RRT*-DC) across various obstacle scenarios. Experimental results demonstrate that CIMAP achieves an average motion planning time of under 0.7 s, improving planning efficiency by at least 89% compared to the baseline algorithms, while maintaining shorter path lengths.

The research on multi-robot collaborative search in unknown 3D environments, based on bio-inspired neural networks, holds significant value and importance.However, challenges arise in 3D environments, including excessive turning and vertical movement, as well as the potential for collisions between robots. In response, we propose an improved Glasius Bio-Inspired Neural Network(GBNN) that mitigates decision conflicts among robots and considers the impact of turning and vertical movement on robot decision-making. Furthermore, to address the issue of robots getting trapped in local deadlocks during the search process, we present a dual neural network algorithm based on Levy flights. In the method Dual Glasius Bio-Inspired Neural Network based on Levy Flight(LF-DUAL-GBNN) proposed in this paper, robots obtain random target points through Levy flights and are then guided by a dual neural network to navigate to the vicinity of the target points, thus breaking free from local deadlock states. Finally, we conducted simulation experiments to validate the algorithm’s effectiveness.

Model predictive control (MPC) has been applied to parallel power converters to achieve current tracking and zero-sequence circulating current (ZSCC) suppression. To facilitate real-time implementation, simplifications of the optimization problem are commonly made by reformulating the cost function or restricting the feasible control set. However, inappropriate simplifications can lead to suboptimal solutions, degrading control performance. To address this issue, this work formulates a formal finite control set MPC (FCS-MPC) problem for grid-connected parallel power converters. It obtains the optimal solution using the computationally efficient modified sphere decoding algorithm. The guaranteed optimality allows the proposed FCS-MPC to maximize the system control performance in suppressing ZSCC, reducing current distortion, and improving transient response. Furthermore, we propose an alternative cost function that includes the grid-side total current as one of the control variables. Owing to the interleaving-wise mechanism, there is a significant enhancement in the power quality of the grid integration current, particularly at lower switching frequencies. Finally, experimental results from a lab-constructed test bench validate the theoretical analysis.

This brief concerns with the tracking control problem for time-varying input-output linearizable systems with output constraints under only measurable output signals. To prevent constraint violation, a high-order control barrier function (HoCBF) is introduced. Furthermore, a continuously differentiable auxiliary reference signal function is constructed to reduce the chattering at the safe boundaries and avoid the constraint violation at the switching time when the constraints become active or inactive. Based on that, blending a time-varying coordinate transformation and the HoCBF conditions, a safety-based dynamic output feedback tracking control method is proposed in an explicit form such that the output is driven to track the reference signal to a small neighborhood and ensured within the constrained region simultaneously. Consequently, it is unnecessary to solve the quadratic program (QP) problem online, which avoids the undesirable infeasibility problem, and facilitates the stability analysis in a closed form by the proposed controller. The effectiveness of the proposed control approach is illustrated by a numerical example.

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment. Note to Practitioners-The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability.