• Formulated a ship decarbonization design integrating PCC with the ship energy system. • Developed a hybrid model to capture the PCC dynamics under varying ship engine loads. • Designed an EMPC for energy-efficient PCC operation with high carbon capture rate. • Employed cross-entropy to efficiently solve the complex EMPC optimization problem. • Conducted extensive simulations verifying superior modeling and control performance. Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the International Maritime Organization. In this work, we address the energy-efficient operation of shipboard carbon capture processes by proposing a hybrid modeling-based economic predictive control scheme. Specifically, we consider a comprehensive shipboard carbon capture process that encompasses the ship engine system and the shipboard post-combustion carbon capture plant. To accurately and robustly characterize the dynamic behaviors of this shipboard plant, we develop a hybrid dynamic process model that integrates available imperfect physical knowledge with neural networks trained using process operation data. An economic model predictive control approach is proposed based on the hybrid model to ensure carbon capture efficiency while minimizing energy consumption required for the carbon capture process operation. The cross-entropy method is employed to efficiently solve the complex non-convex optimization problem associated with the proposed hybrid model-based economic model predictive control method. Extensive simulations, analyses, and comparisons are conducted to verify the effectiveness and illustrate the superiority of the proposed framework. The proposed hybrid model-based economic model predictive control reduced the overall economic cost by 8.07% compared to conventional optimal set-point tracking nonlinear model predictive control and achieved a 4.20% lower economic cost with a 9.10% higher carbon capture rate than the imperfect first-principles model-based economic model predictive control.

Nonlinear active vibration control of variable-thickness smart sandwich panel under impact loads

Nonlinear model predictive position control based on an adaptive barrier Lyapunov tracking error constraint for PMSM servo control systems

Birefringence, as a manifestation of optical anisotropy in crystalline materials, is a fundamental physical property that plays a crucial role in nonlinear optics, polarization control, and photonic device engineering. Traditionally, birefringence has been primarily ascribed to geometric anisotropy in crystal structures. However, recent research has demonstrated that anisotropic electronic distributions, even in geometrically symmetric frameworks, can significantly enhance optical anisotropy. In this work, we report on the synthesis, structural characterization, and optical property evaluation of Rb2ZnGe3Se8, a newly identified member of the AI2BIIMIV3Q8 chalcogenide family, which exhibits a large theoretical birefringence of 0.257 at the wavelength of 1064 nm. First-principles calculations reveal that the large birefringence arises from spatially anisotropic electron distributions within the [GeSe4] tetrahedra, rather than from overt structural distortion. The results highlight a distinct electronic origin of birefringence and expand the current understanding of how directionally polarized bonding interactions can induce significant optical anisotropy in nominally symmetric frameworks. This work deepens the understanding of structure-electronic-optical coupling mechanisms in layered chalcogenide frameworks and supports the rational design of materials with large birefringence.

This study presents a flapping-wing micro aerial vehicle designed for three-dimensional trajectory tracking. The design features center-of-gravity-based steering, three actuators, and omits a dedicated yaw actuator, time-varying wingbeat patterns, and high-frequency actuator switching. To investigate the dynamics of the proposed design, a modified nonlinear controllability framework was developed for theoretical analysis, and a series of flight experiments were conducted to validate the analytical results. The analysis indicates that, despite the absence of a dedicated yaw actuator, the yaw torque required for three-dimensional maneuvering arises from center-of-gravity shifts, asymmetric wing aerodynamics, and an intrinsic feedback mechanism that stabilizes the angle difference between vehicle's yaw angle and trajectory rotation angle. Furthermore, the yaw dynamics exhibit nonminimum phase behaviors in response to center-of-gravity movement. Based on these findings, a multi-tier control architecture is proposed to accommodate these unique dynamics for autonomous flight. Experimental results demonstrate that the proposed design can perform three-dimensional trajectory tracking, with attitude errors below 5° and altitude deviations within 10 cm in most cases.

Purpose The purpose of this paper is to meet load demand and reduce the impact of renewable energy sources’ intermittency, microgrids (MGs) rely on the coordinated operation of photovoltaic (PV), wind and hybrid renewable energy sources (HRSS). A hybrid energy storage system (HESS) integrates both long-term energy storage in batteries and short-term energy compensation in supercapacitors. To keep direct current (DC) MGs stable, manage power sharing and regulate DC bus voltage, which indicates a supply–demand imbalance, power electronic converters and good management procedures are necessary. To guarantee dependable operation of a low-voltage direct current (LVDC) MG in dynamic environments, robust power management is crucial. Design/methodology/approach Maintaining a constant DC bus voltage is essential for reliable LVDC MG operation and proper HESS charging/discharging under varying generation and load. To achieve this, a dynamic frequency-decoupled energy management strategy (DFD-EMS) is proposed, which uses a hierarchical structure to separate low-frequency (steady-state) and high-frequency (dynamic) power components, enabling coordinated power sharing among PV, wind, battery and supercapacitor units. The architecture uses cascaded PI-LagLead (PILL) controllers in outer voltage and inner current loops, as shown in Figure 3, enabling hierarchical coordination for stable and efficient power management. Findings This paper presented a robust control and energy management framework for LVDC MGs with hybrid renewable sources and hybrid energy storage. A dynamic frequency-decoupled EMS enabled coordinated power sharing between batteries and supercapacitors, with a novel cascaded PILL controller implemented in both voltage and current loops to enhance stability, transient response and noise rejection. The performance of the proposed PILL-based controller was systematically compared with four conventional cascaded control configurations using proportional–integral (PI) and lag-lead controllers. Comparative simulation and real-time hardware-in-the-loop (HIL) results across seven operating scenarios demonstrated superior DC-bus voltage regulation, reduced overshoot and faster settling times compared to conventional controllers. Quantitative analysis confirmed low overshoot (<4%) and fast settling times of less than 0.008 s under generation and load disturbances, validating the robustness and practical applicability of the proposed approach. The simplicity and real-time feasibility of the classical PILL-based framework make it well-suited for practical LVDC MG applications. Future work will focus on experimental validation using full-scale hardware prototypes and the extension of the proposed framework to grid-interactive DC MG architectures. Originality/value This work suggests a comprehensive control and power management framework for LVDC MGs integrating HRES and HESS. The significant contributions of this work are as follows: A novel cascaded control structure using phase-compensated PILL controllers in both the outer DC-bus voltage loop and the inner current loop is proposed. A dynamic frequency-decoupled EMS is proposed to enable coordinated power sharing among PV, wind, battery and supercapacitor sources. Comparative evaluation of five cascaded control configurations for HRES using HESS, including the proposed controller and four conventional control strategies designed and analysed for performance assessment: Outer PI–Inner PI, Outer LagLead–Inner LagLead, Outer PI–Inner LagLead, Outer LagLead–Inner PI and Outer PILL–Inner PILL (Proposed). A unified classical control framework with stability analysis is developed with the proposed cascaded PILL controller, which outperforms other classical configurations under diverse operating conditions. Although non-linear control strategies can provide enhanced dynamic performance, their practical implementation may require increased computational resources, accurate system modelling and higher tuning effort. For moderate-power 48 V LVDC MG applications rated at 1.2 kW, the proposed PILL-based controller offers an attractive trade-off between control performance, implementation simplicity and cost-effectiveness. The effectiveness and robustness of the proposed PILL-based DFD-EMS are validated through real-time HIL experiments under seven distinct operating scenarios involving independent and simultaneous variations in PV power, wind power and load demand. These results confirm reliable operation under multi-timescale disturbances and demonstrate the practical applicability of the proposed strategy for LVDC MGs.

Ozone-driven non-linear controls on nighttime ClNO2 formation: Unintended oxidant increases under NOx control.

In modern-day, real-time challenges are increasing in Load frequency control (LFC). This work presents a novel reduced-order generalized active disturbance rejection control (RGADRC) strategy. The generalized structure of ADRC incorporates detailed plant information in the design. The proposed work incorporates an innovative hyperbolic non-linear error function in a reduced-order extended state observer (RESO). This refinement in observer gain due to non-linear gain variation improves the time domain specification of the response, specifically in terms of overshoot, settling time, and error indices. The proposed approach is simulated for various delayed single area non-reheat and reheat systems, including non-linearities like Generation rate constraint (GRC) and Governor dead band (GDB). Further, the proposed strategy is extended for two-area PV-thermal hybrid systems with non-linearity, and a practical IEEE 39 Bus New England system, which is a widely used benchmark system in power system studies. The robustness of this method is analyzed under the condition of + 50 % parametric uncertainty in gain and time constant simultaneously. This design technique effectively manages non-linearities, constant, and varying time delays, and assesses cyber-attack vulnerabilities. Further, the real-time challenges in the IEEE system are studied for multistep load variations with communication delay.

This paper proposes a nonlinear controller framework for the tracking and stabilization control of rotary inverted pendulum (RIP) systems. These systems present significant control challenges due to their underactuation, high sensitivity, and inherent instability. Most existing studies rely on linearized models of RIP systems. However, these approaches limit the controller's performance to a small area around the equilibrium point. On the other hand, applying advanced nonlinear control techniques to the RIP underactuated system remains challenging. To overcome these limitations, the second-order sliding mode controller (SOSMC) based on a PID sliding surface is developed and applied to the RIP system. The designed procedure incorporates the nonlinear dynamic model to stabilize the pendulum in a greater region and address the challenges of underactuation. The designed control scheme enhances the system performance in stabilizing the pendulum in the vertical upright position and maintaining its balance when the horizontal arm tracks a desired trajectory. The efficacy of the designed controller is compared with three other control approaches: a standard sliding mode control (SMC), a reinforced PID-SMC, and a linear quadratic regulator (LQR). The simulation results demonstrate that the proposed controller scheme exhibits high-speed tracking performance with reduced overshoot. The results show that the Mean Square Error (MSE) for the arm angle is decreased to 0.2229, compared to 0.22346 for reinforced PID-SMC, 0.2250 for SMC, and 0.2299 for LQR. Moreover, the MSE for the pendulum angle is reduced by approximately 69.36%, 28.6%, and 25.68% compared to the reinforced PID-SMC, SMC, and LQR algorithms, respectively. Furthermore, the designed controller achieves a 41.99%, 10.06%, and 18.33% reduction in the RMS value of the control input compared to the reinforced PID-SMC, SMC, and LQR controllers, respectively. The simulation results reveal that the designed controller achieves superior tracking accuracy, faster response, and reduced overshoot, while maintaining efficient energy consumption.

To address the inherent limitations of the Dhole Optimization Algorithm (DOA)-limited exploration range, insufficient population diversity, and slow convergence-this paper proposes a Modified Dhole Optimization Algorithm (MDOA) integrating a Beta distribution-based opposition learning strategy, a DE/rand-to-best/1 differential mutation mechanism, and nonlinear parameter control. MDOA is evaluated on 41 CEC2017 and CEC2022 benchmark functions, outperforming 11 state-of-the-art algorithms in convergence speed, accuracy, and robustness. It is then applied to five engineering optimization problems: compression spring design, speed reducer weight minimization, rolling bearing optimization, tubular column design, and moisture content prediction of Dendrobium huoshanense using near-infrared spectroscopy with a BP neural network. The MDOA-BP model reduces MAE, RMSE, MSE, and MAPE by 27.5%, 27.8%, 47.6%, and 31.0%, respectively, while increasing R2 from 0.8339 to 0.9130, achieving the best results among all comparison models. These results demonstrate that MDOA is a highly effective and robust optimizer for complex constrained engineering and high-dimensional optimization tasks.

This paper presents a theoretical framework for analyzing the stability of higher-order attitude control laws on the Special Orthogonal Group SO ( 3 ) . In particular, it considers a class of dynamic state-feedback compensators in which the controller state x K ∈ R n has arbitrary finite dimension. This class of compensators includes, as special cases, several existing geometric nonlinear PID-type attitude controllers in which the dynamic component is an integrator state having dimension 3. As a result, the proposed analytical framework extends existing results on geometric nonlinear PID-type attitude control to more general dynamic state-feedback compensators on SO ( 3 ) . The stability analysis of the higher-order attitude controller is carried out in two main steps. First, a quadratic candidate Lyapunov function is used to obtain sufficient conditions, in the form of bilinear matrix inequalities (BMIs), which ensure that the desired equilibrium of the closed-loop tracking error system is almost globally asymptotically stable (AGAS). Then, a convex relaxation of the proposed conditions is used to obtain sufficient conditions in the form of linear matrix inequalities (LMIs). To reduce conservatism, matrix gains are used for the controller gains as well as the Lyapunov function coefficients. The use of quadratic Lyapunov functions and LMIs is motivated by their ubiquitous use in the design and analysis of dynamic compensators for linear time-invariant (LTI) systems. The applicability of the proposed analytical framework to practical problems on SO ( 3 ) is illustrated by designing a 9-state almost globally asymptotically stabilising attitude controller for an agile multicopter.

To optimise the service life and operating cost of the hydrogen fuel cell ship energy system and improve the ship’s endurance, a two-layer energy management strategy (EMS) is proposed in this paper. The first layer of the EMS is based on nonlinear model predictive control (NMPC), which combines the analysis of the load demand of the multi-fuel cell stack (MFCS) with the battery to minimise the operating cost and maximise the endurance. The dynamic programming (DP) is used to provide an initial reference and an optimisation range for NMPC to further enhance its convergence speed and optimisation quality. The second layer addresses the power allocation to each fuel cell (FC) in the MFCS and based on the MFCS optimal efficiency range (MER) and considering power generation characteristics and performance degradation of FC, the MFCS optimal power allocation curve (MOC) is proposed to improve the fuel efficiency of the MFCS while decreasing the performance degradation rate of the FC and promoting the consistent performance degradation of the MFCS. The experiment demonstrates that the EMS proposed in this paper can effectively reduce the operating cost, extend the system service life, promote consistent FC performance degradation, and enhance the endurance of the ship.

Autonomous hydraulic excavators are widely used in construction, mining, and material-handling operations, offering improved efficiency and safety. Precise trajectory tracking is essential for such systems. However, inherent nonlinearities and significant time delays in the hydraulic actuators hinder accurate control for autonomous operations. To address these challenges, a nonlinear model predictive control (NMPC) algorithm is proposed. Specifically, a Hammerstein–Wiener structure is employed to model the nonlinear hydraulic system, with parameters identified from experimental data. Based on this model, an NMPC trajectory-tracking algorithm is developed, which accounts for actuator and control input constraints. To mitigate the intrinsic 0.5-second response delay of the hydraulic system, a predictive delay compensation strategy is introduced, whereby predicted joint states over the next 0.5 seconds serve as real-time control references. Simulation results demonstrate that the proposed controller substantially outperforms proportional–integral–derivative (PID) and fuzzy PID methods, maintaining the bucket-end error within 20 cm. Field experiments on an autonomous excavator implemented under the robot operating system (ROS) framework confirm that the maximum trajectory-tracking error remains within 50 cm, validating the effectiveness and robustness of the proposed NMPC approach under real-world operating conditions.

The liquid-phase epitaxy (LPE) layer-by-layer method can be used to prepare metal-organic framework (MOF) materials suitable for nonlinear optical regulation due to its advantages of controllable thickness and smooth surface. In this study, Co-BTE MOF thin films were successfully synthesized by this technology, and their nonlinear control performance was studied by combining the spatial self-phase modulation method (SSPM). The results demonstrate that the Co-BTE MOF film has a significant nonlinear response with obvious third-order nonlinear optical effects observed. It was determined that the third-order nonlinear absorption coefficients of the Co-BTE MOF film were -0.03, 0.16, 0.33, and 0.04 cm/GW at wavelengths of 400, 532, 635, and 785 nm, respectively. To further investigate the nonlinear behavior of Co-BTE MOFs, the methods of SSPM and spatial cross-phase modulation (SXPM) were proposed to analyze their nonlinear regulation performance. Fortunately, the nonlinear properties of Co-BTE MOFs can be used to realize the functions of all-optical switches and photonic diodes, providing a reference for the development of nano-optoelectronic devices.

Run-to-run (R2R) control is widely used in advanced manufacturing. Conventional R2R control methods often assume that system disturbances exhibit linear structures or low-rank approximations in high-dimensional data, such as images or videos. While these assumptions facilitate implementation, they limit the effectiveness of R2R control in managing sophisticated manufacturing processes with intricate nonlinear disturbances. To address these challenges, we propose a novel nonlinear spatio-temporal R2R control framework that estimates system disturbances using a diffusion model and applies control actions to compensate for them. The proposed approach integrates an offline modeling phase to capture disturbance dynamics and an online control phase that dynamically adjusts control actions to minimize deviations from the target system response. Unlike conventional deterministic control strategies, the proposed method explicitly quantifies uncertainty, enabling more robust and informed decision-making. The effectiveness of this method is validated through simulation studies and a case study, demonstrating its adaptability in complex, high-dimensional environments.

Optimized adaptive fractional-order sliding mode control for quadrotor trajectory tracking under external disturbances.

Development of sigmoid based nonlinear controllers in a PID framework for load frequency stabilization.

Estimation of interactive controls and threshold levels of ozone under traffic-influenced near-road conditions.

This paper investigates the optimal control of a stochastic delayed system with infinite delay of general functional type. By introducing a non-anticipative path derivative and its infinite-window dual operator, we formulate the infinitely anticipated backward stochastic evolution equation (IABSEE) as the adjoint equation and establish both necessary and sufficient maximum principles. As applications, we investigate two optimal control problems featuring infinite delay. For both the classical linear-quadratic (LQ) problem and the nonlinear emission control model, the optimal controls are derived explicitly.

This paper proposes a cascaded simplified PD-like fuzzy logic controller and fractional-order PID (SFLC + FOPID) control strategy for DSTATCOMs in electric vehicle-integrated microgrids. The simplified fuzzy logic controller employs a reduced rule base, preserving effective nonlinear control characteristics while significantly lowering computational burden. The cascaded FOPID enhances tuning flexibility and robustness against system uncertainties, improving both transient and steady-state performance under voltage disturbances. A constrained optimization framework is formulated to tune the controller parameters to achieve fast voltage regulation and enhanced dynamic response. To validate the proposed approach, five recent metaheuristic algorithms, Starfish Optimization Algorithm (SFOA), Mountaineering Team-Based Optimization, Animated Oat Optimization Algorithm, Enzyme Action Optimizer, and Rapidly-Exploring Random Trees-based Optimizer, are employed for parameter tuning and performance comparison. Simulation results confirm that the proposed SFLC + FOPID-controlled DSTATCOM achieves superior voltage regulation and robustness compared to the PI and FOPI, the cascaded FOPI controller, the simplified fractional-order fuzzy logic controller (SFO-FLC), and the two-input FO-FLC. In addition, the results demonstrate the superiority of SFOA in tuning the SFLC + FOPID parameters relative to the other algorithms.