Abstract: An extended operational space kinematics and dynamics formulation is presented for control of redundant non-serial compound robotic manipulators. A broad spectrum of high load capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through simulation and control of a multi-degree of redundancy non-serial compound material loader manipulator, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness.

Abstract: Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed for grasping drones that utilize low-altitude visual servo to land on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated multi-source information sensors for the treatment of air to a water-related airplane crash. Dynamics model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The efficacy of this grasp paradigm when compared with a previous top-to-down model without power recovery has been verified by retrieving vessels in the military fields.

Abstract: Nowadays, the optimization methods are widely used to adjust controller parameters to tune their optimal values in order to enhance the efficiency and performance of dynamic systems. In this study, the parameters of a linear PI controller were optimized by using five different optimization algorithms such as Artificial Tree Algorithm (ATA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DEA), Constrained Multi-Objective State Transition Algorithm (CMOSTA), and Adaptive Fire Forest Optimization (AFFO). The optimized controllers were implemented in real time for temperature control of a Heat-Flow System (HFS) under various step and time-varying reference signals. In addition, the Ziegler–Nichols (Z-N) method was also applied to the system as a benchmark to compare the temperature tracking performance of the proposed optimization methods. To further evaluate the performance of each optimization algorithm, Mean Absolute Error (MAE) values ​​were calculated and improvement ratios were obtained. The experimental results showed that the proposed optimization methods provided more successful reference tracking and enhanced controller performance as well.

Abstract: To solve the problem of insufficient joint tracking performance in the feedforward control of industrial robots caused by model and control quantity errors, a data-driven feedforward optimization algorithm is proposed. This algorithm not only enhances joint tracking performance but also optimizes trajectory accuracy. Firstly, aiming at the nonlinear residuals in the dynamic model resulting from linear assumptions, neglect of high-order terms and other factors, a joint torque residual fitting method based on ensemble learning is proposed. This method constructs a hybrid dynamic model that combines mechanism-based and data-driven approaches, thereby improving the accuracy of torque feedforward quantities. Secondly, based on the principle of iterative learning control, a data-driven velocity feedforward optimization algorithm is proposed. By comparing historical and real-time data, the learning gain is dynamically adjusted, and the velocity feedforward parameters are updated iteratively to reduce joint tracking deviations. Finally, experiments on industrial robot verify that the torque residual fitting based on ensemble learning reduces the joint torque prediction error by more than 77%. After optimizing the feedforward control quantities, the root mean square error of joint tracking is significantly improved: it is reduced by more than 60% for linear trajectories and by more than 20% for circular trajectories.

Abstract: The control of wastewater treatment plants (WWTPs), with the ultimate goal of reducing as much as possible the contamination of aquatic ecosystems, constitutes an important multidisciplinary environmental objective. One of the best-known wastewater treatment procedures consists of the use of the so-called Activated Sludge Processes (ASP), which are biological processes that reduce organic contamination thanks to the vital activity of certain bacteria. The control of this type of processes is not easy, precisely due to its biological nature. Consequently, the control of wastewater treatment plants based on ASP processes constitutes an important challenge in the field of Automatic Control, with numerous strategies proposed to date. With the aim of testing and evaluating the different existing strategies, in an objective and orderly manner, the so-called Benchmark Simulation Models (BSM) emerged, which are standard models of wastewater treatment plants based on ASP processes. The main objective of this article is precisely to test the feasibility and evaluate a specific Fuzzy Model Based Predictive Control (FMBPC) strategy, applied to the wastewater treatment plants represented in the BSM1 benchmark (a particular case of the BSM benchmarks). The FMBPC strategy is potentially appropriate for the control of complex, changing or unknown systems and this article demonstrates that this strategy, used in the BSM1 benchmark control configuration (as an alternative to the default control configuration), is viable and performs satisfactorily, to the point that it can even be considered a competitive strategy compared to more traditional control strategies. In the experiments carried out, in a simulation environment, a specific FMBPC control modality has been used, called FMBPC/CLP, which incorporates mechanisms for imposing restrictions on the control action. The base model of the plant to be controlled, necessary for the implementation of the FMBPC strategy, is obtained by prior fuzzy identification of the plant, which is, in our case, the WWTP plant integrated into the BSM1 benchmark itself. The identification procedure developed is based on the information contained in input output data series of the plant in open loop, previously obtained by simulation. Identification is achieved with the help of a software tool that uses mathematical clustering methods, based on the Gustafson Kessel algorithm, through which it is possible to extract Takagi Sugeno type fuzzy models, from numerical input output data of a given plant.

Abstract: This paper presents the Normalized Coefficient Linear Combination (NCLC) as a unifying structural framework for analyzing systems in digital signal processing (DSP), discrete-time control, and numerical sequence analysis. While traditional design focuses on pole–zero placement, the NCLC perspective emphasizes the decomposition of signals into a global carrier function (scale) and a residual modulated by normalized coefficients. We formalize conditions under which this structure guarantees asymptotic preservation of the carrier. Furthermore, we demonstrate how coefficient normalization serves as a direct design tool in finite-order discrete-time LTI systems to simultaneously satisfy BIBO stability and prescribed steady-state gain constraints. The versatility of this framework is illustrated through three applications: a first-order digital filter, a parametric model correction scheme, and the signal analysis of prime number asymptotics viewed as a discrete sequence with arithmetic noise.

Abstract: This paper introduces Axiomatic System Dynamics (ASD), a novel, formally self-consistent framework for describing the evolutionary processes of diverse complex systems. ASD is founded on three fundamental axioms which establish a unified, action-driven causal architecture. By utilizing the formal syntax of "State-Action-State Increment," ASD reduces distinct phenomena to Generalized Constitutive Relations that possess a unified structure. This formalization creates a critical bridge between observed phenomena and their mathematical expressions, directly addressing the challenge of incommensurability by providing a common semantic language for models across scientific domains. The framework's efficacy is demonstrated through three progressively complex applications. The analysis of Newtonian Mechanics re-interprets inertia as an Internal Action. The study of material constitutive relations validates the use of the Generalized State Parameter to formally model path dependence and system memory in Non-Markovian systems. Finally, the application to soil consolidation illustrates ASD's power in logically integrating multi-scale, multi-mechanism coupled processes. ASD provides a powerful conceptual tool for advancing the goal of scientific methodological unification.

Abstract: Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes [10–16]. Furthermore, we formalize the biological governance mechanisms, demonstrating that unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers [17–33]. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency and energy budgets, and safety-case hooks that support certification and adoption across autonomous domains [6,11,14,28].

Abstract: This review explores the link between engineering and creativity, challenging the perception gap between structured training and creative fields. It reframes human creativity insights from prominent scholars to inform the development of AI systems capable of creative problem-solving. The paper translates abstract and philosophical models into structured, computationally tractable frameworks to bridge human creativity research and machine learning applications. The review focuses on four core frameworks to guide AI design: Wallas’s Four-Stage Process, Rhodes’ Four Ps Model, Simonton’s Creativity-as-Influence Model, and Runco’s prevailing framework. It traces the historical progression of creativity research from early efforts by Guilford and Torrance to later dynamic frameworks by Amabile and Csikszentmihalyi. The document discusses how these models, which evolved from abstract theorizing to structured, multidimensional constructs, provide a foundation for examining and applying creativity within technical domains. It also addresses the growing integration of AI, distinguishing between human creativity and artificial creativity produced by machines. The forward-looking perspective suggests an augmentative role for AI within hybrid human-AI workflows. Ultimately, the review aims to provide a blueprint for developing AI systems that move beyond rote problem-solving to exhibit adaptive, context-sensitive, and generative capabilities, capitalizing on the synergy between creativity science and AI.

Abstract: This paper presents the design and implementation of a 12kW@2K supercritical helium cryogenic control system for the Shanghai Hard X-ray Free Electron Laser Facility (SHINE). The system provides a stable cryogenic environment for 56 standard 1.3 GHz superconducting modules, meeting the stringent temperature control requirements of the accelerator for high-precision operation of superconducting cavities. A distributed PLC architecture integrated with EPICS is adopted, comprising three refrigerators, four distribution valve boxes, and 39 module subsystems. Redundant network design ensures reliable data transmission. Core functionalities include a four-stage automated cooling process (from 300 K to 2 K), dynamic power compensation, and a safety interlock mechanism.Experiments show that the system can stabilize the liquid level of the helium tank within ±1% and maintain the pressure at 3100Pa ± 10Pa, effectively ensuring the smooth progress of low-level experiments. Dynamic power compensation adjusts heater power in real time based on a model correlating cavity pressure with heat load, ensuring stability during cryogenic operation. The system has been successfully applied in the commissioning of the injector section, validating its efficiency and safety in providing a cryogenic environment for superconducting accelerators, and serving as a key technical foundation for the SHINE project.

Abstract: Loop Closure Detection (LCD) is a key component of Simultaneous Localization and Mapping (SLAM) systems, responsible for correcting odometric drift and maintaining global consistency in localization and mapping. However, single-modality LCD methods suffer from inherent limitations: LiDAR based approaches are affected by point cloud sparsity, limiting feature representation in unstructured environments, while vision-based methods are sensitive to illumination and weather variations, reducing robustness. To address these issues, this paper presents a LiDAR–vision multimodal fusion LCD algorithm. Spatiotemporal alignment between LiDAR point clouds and images is achieved through extrinsic calibration and timestamp interpolation to ensure cross-modal consistency. Harris corner detection and BRIEF descriptors are employed to extract visual features, and a LiDAR-projected sparse depth map is used to complete depth information, mapping 2D features into 3D space. A hybrid feature representation is then constructed by fusing LiDAR geometric triangle descriptors with visual BRIEF descriptors, enabling efficient loop candidate retrieval via hash indexing. Finally, an improved RANSAC algorithm performs geometric verification to enhance the robustness of relative pose estimation. Experiments on the KITTI and NCLT datasets show that the proposed method achieves average F1 scores of 85.28% and 77.63%, respectively, outperforming both unimodal and existing multimodal approaches. When integrated into a SLAM framework, it reduces the Absolute Trajectory Error (ATE) RMSE by 11.2%–16.4% compared with LiDAR-only methods, demonstrating improved loop detection accuracy and overall system robustness in complex environments.

Abstract: In a globalized world where data plays an important role in system operation, process automation, decision-making, etc., the development of real-time control systems is essential because it allows operators or supervisors to know the current status of a process based on the physical variables that are part of it. Therefore, there is currently a wide range of software/hardware tools available through which control systems can be implemented to operate in real time, some of which are Arduino, ESP32, and PIC microcontrollers. As an alternative to the current limitations of the aforementioned systems, this paper presents a novel proposal for the implementation of digital controllers using Texas Instruments embedded systems, which is based on an experimental framework using different test plants for which different control strategies were designed. The results obtained highlight the ability of Texas Instruments microcontrollers to execute real-time control loops applied to different physical systems and operated under different parameters. In conclusion, it was evident that Texas Instruments embedded systems equipped with different microcontrollers are an interesting alternative in the development of control systems, not only on a small scale but also in industrial applications.

Abstract: This study presents a simulation-based framework for PID controller design in strongly nonlinear dynamical systems. The proposed approach avoids system linearization by directly minimizing a performance index using metaheuristic optimization. Three strategies—Particle Swarm Optimization (PSO), GreyWolf Optimizer (GWO), and their hybrid combination (PSO–GWO)—were evaluated on benchmark systems including pendulum-like, Duffing-type, and nonlinear damping dynamics. The chaotic Duffing oscillator was used as a stringent test for robustness and adaptability. Results indicate that all methods successfully stabilize the systems, while the hybrid PSO–GWO achieves the fastest convergence and requires the fewest cost function evaluations, often less than 10% of standalone methods. Faster convergence may induce aggressive transients, which can be moderated by tuning the ISO (Integral of Squared Overshoot) weighting. Overall, swarm-based PID tuning proves effective and computationally efficient for nonlinear control, offering a robust trade-off between convergence speed, control performance, and algorithmic simplicity.

Abstract: Traditional CSAMT omnidirectional radiation causes low energy efficiency, while near-field effects and far-field interference limit detection accuracy. To address the issues of energy dispersion, low signal-to-noise ratio, and restricted exploration range in conventional Controlled Source Audio-frequency Magnetotelluric (CSAMT) methods for deep exploration, this paper proposes a tensor CSAMT directional pattern synthesis and adaptive beamforming method based on an L-shaped array artificial field source. An L-shaped source composed of two orthogonal coaxial dipole linear arrays is designed, and Taylor-weighted directional pattern synthesis is employed to reduce sidelobe levels. An adaptive beamforming system is constructed based on the maximum Signal-to-Noise Ratio criterion to achieve dynamic beam steering and energy focusing. Verified via COMSOL simulations and field experiments, this study upgrades the CSAMT artificial source from "omnidirectional radiation" to "directional and controllable" using array antenna technology, providing a high signal-to-noise ratio and efficient electromagnetic detection paradigm for deep resource exploration.

Abstract: Mobile robot controllers are often complex due to their multi-layered architecture and the numerous inputs and outputs handled at each layer. This work models the lowest level of a differential-drive mobile robot's controller stack using a set of state machines, demonstrating how this approach streamlines control system development. This level handles robot locomotion and sensor data acquisition. The resulting state machines are easily implemented in any procedural language, including C++ and Lua. We use C++ to program controllers for actual Arduino-based robots and Lua to program models of such robots in the CoppeliaSim simulator. Both real and virtual robots have been used in an Embedded Systems course at our university since 2011, with continuous improvements.

Abstract: A decentralized DC power exchange method is proposed to enable direct bidirectional power transfer among geographically distributed DC microgrids. Each microgrid is connected to a shared power exchange grid via a bidirectional DC/DC converter, al-lowing flexible participation regardless of location. The architecture supports dynamic scalability, permitting microgrids to join or leave the exchange network without dis-rupting overall operation. To evaluate the feasibility of the proposed method, a 2-to-2 power exchange experiment was conducted using lithium-ion batteries configured to emulate microgrid baselines. The results demonstrated that arbitrary power ratios can be achieved through appropriate adjustment of converter parameters, and that trans-mission loss and efficiency varies depending on the power distribution ratio. In addi-tion, the operational stability of the system was experimentally verified under sudden fluctuations in baseline voltage, such as those caused by abrupt changes in generation or load. Stable power exchange was maintained even under disturbances of several percent. These findings confirm the practicality and robustness of the converter-based archi-tecture and highlight its applicability to scalable, distributed DC microgrid intercon-nection.

Abstract: Classical macroscopic models of metal–hydride (MH) hydrogen storage rely on empirical Arrhenius laws that neglect quantum phenomena such as tunneling, zero-point motion, and hydrogen–lattice interactions. As a result, their predictive and control performance degrade across wide temperature ranges, particularly in cryogenic regimes where quantum transport remains active. This paper presents a unified quantum-informed diffusion and control framework that bridges microscopic hydrogen–lattice physics with macroscopic predictive control. A temperature-dependent quantum correction operator is incorporated into the classical diffusion law, yielding an analytically tractable yet physically enriched model. Parameters are identified through weighted robust regression with bootstrap-based uncertainty quantification and integrated into a model predictive control (MPC) scheme that adapts to temperaturedependent dynamics. Simulation results show that tunneling-enhanced diffusion improves lowtemperature response and reduces steady-state error and control effort by up to 50% compared with classical Arrhenius-based control. While the present study focuses on numerical validation, the proposed architecture establishes a transferable foundation for digital-twin development—linking microscopic quantum transport and system-level predictive control for next-generation hydrogen storage technologies.

Abstract: This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments.

Abstract: Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy.

Abstract: Sub-hourly operational optimization of Power-to-X (PtX) hydrogen systems remains largely unexplored, despite their growing importance as flexible assets in renewable-dominated energy systems. Existing models typically assume hourly market resolution and linear process behavior, overlooking how intra-hour price volatility and non-linear electrolyzer efficiencies shape operational costs, flexibility, and emissions. This study pioneers a data-driven optimization framework that integrates synthetic 15-minute electricity-price generation, agent-based simulation, and mixed-integer quadratically constrained programming (MIQCP) to evaluate hydrogen-production strategies under the forthcoming European 15-minute market regime. Using a Danish PtX facility with on-site wind and solar generation as a case study, the framework quantifies how adaptive scheduling compares with non-adaptive baselines across multiple volatility scenarios. Results show that dynamic 15-minute optimization reduces hydrogen-production costs by up to 40 % relative to hourly scheduling and that extending the objective function to include electricity-sales revenue improves net profitability by approximately 11%. Although adaptive scheduling slightly increases CO2 intensity due to altered renewable utilization, it substantially enhances flexibility and cost efficiency. Scientifically, the study introduces the first reproducible synthetic-data approach for sub-hourly optimization of non-linear electrolyzer systems, bridging a critical gap in demand-side-management and sector-coupling literature. Practically, it provides evidence-based guidance for PtX operators and regulators on designing adaptive, volatility-responsive control strategies aligned with Europe’s transition to high-frequency electricity markets and net-zero objectives.