Advanced Control Research Articles

Advanced control and optimization strategies for a 2-phase interleaved boost converter

Renowned for their adeptness in smoothing current flow and maintaining balanced operation, 2-phase interleaved boost converters (IBC) demonstrate remarkable efficiency, especially when confronted with demanding loads. This makes them a preferred choice for high-power applications such as renewable energy systems, high-power supplies, and electric vehicle power trains. In contrast, standard boost converters are typically favored in low-power, low-demand scenarios. The control of a 2-phase IBC involves running two boost converters in parallel but with a phase shift to reduce ripple currents, improve efficiency, and increase power handling capabilities. To ensure stability and optimal performance, the control strategies for these converters focus on achieving balanced operation between the phases. Hence, the control of 2-phase IBC presents a significant challenge due to their non-minimum phase behavior. The core focus of this article is the implementation of a composite model predictive control (MPC) technique to regulate a 2-phase interleaved boost converter. It introduces a novel approach, model predictive sliding mode control (MPSMC), which leverages the strengths of both MPC and sliding mode control (SMC). The benefits of this hybrid method, termed MPSMC, are thoroughly developed and simulated using MATLAB/Simulink. The results, as discussed in the respective section, provide an in-depth understanding of its effectiveness in practical applications.

Performance analysis of interlinking converter exchanging power between AC/DC renewable microgrids with advanced control technique

In a distribution system there are many types of AC/DC microgrids integrated at different locations. These microgrids may contain conventional or renewable sources which generate power using natural resources. The power generated by these microgrid sources is uncertain as the natural resources are unpredictable. Therefore, powers from these AC/DC micro grids need to be shared so as to compensate the load demand throughout the distribution system. An interlinking converter (ILC) is connected between the AC/DC microgrids operated by a novel advanced PQ control technique. Both the DC and AC microgrids are interconnected through ILC achieving power exchange between them as per the power demand and generation on both microgrids. Different operating conditions are considered for the analysis and the simulation of these modules are done using MALTAB/Simulink software. Several parameters like DC link voltage, ILC currents, point of common coupling (PCC) frequency, total harmonic distortions (THDs) and active power values are considered for validating the stability of the considered system.

Estimation of carbon sequestration potential and air quality impacts of biochar production from straw in China

Aiming to optimize straw resource utilization and to reduce air pollutant and greenhouse gas emissions in China, the environmental benefits of biochar production using straw and utilization need to be assessed. Two biochar production scenarios were studied: centralized production scenario (CPS) utilizing an industrialized production method with advanced air pollution control devices, and decentralized production scenario (DPS) based on a distributed production method of biochar at the village level, compared to an open-burning scenario (OBS). The potential GHG emission reductions under DPS, including the benefit of soil biochar application, were evaluated as 680 Tg CO2-eq yr-1, which was lower than the reductions under CPS (780 Tg CO2-eq yr-1). Although the attentional centralized production method of biochar has more potential environmental benefits, at present the distributed production method is more practical in China due to its convenience, management ease, and lower costs. Modeling on air quality in China showed that in comparison with OBS, DPS could reduce the average monthly PM2.5 concentration of key regions by 19%, and about 190 Tg yr-1 biochar could be produced with 150 Tg C yr-1 sequestered in biochar. Our findings support widespread biochar use in China for improved air quality and climate neutrality.

Floating offshore wind turbine nonlinear model predictive control optimisation method

This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.

Enhanced wind energy extraction and power quality using advanced super-twisting control for a dual-excited synchronous generator-based wind energy conversion system

Enhanced wind energy extraction and power quality using advanced super-twisting control for a dual-excited synchronous generator-based wind energy conversion system

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

Gas engine generator sets are applied in microgrids due to their capability to provide reliable, responsive and efficient distributed power generation, enhancing grid resilience and enabling the integration of renewable energy sources. This article proposes a methodology for stabilizing the frequency of an isolated microgrid subjected to a measurable disturbance by controlling the power of a premixed turbocharged natural gas engine. Control difficulties of this task include strongly nonlinear dynamics, time delay in the air-fuel path and constrained operating conditions. Conventional control methods like proportional-integral control have difficulties solving these problems, and require extensive tuning efforts and gain scheduling to deliver acceptable performance, prompting adoption of advanced control strategies. The presented approach utilizes a cascaded control architecture with a nonlinear model predictive controller (NMPC) for high level control and engine specific low-level controllers for controlling the intake manifold pressure and air-fuel ratio. The NMPC captures the inherent nonlinear dynamics, accounts for an input delay and handles state-dependent constraints. The cascaded control architecture enables the usage of a comparably simple model for the NMPC design, reducing the computational cost and the parametrization effort. This additionally enables application of the high level controller for different gas engine types and allows design of the controller in a simple simulation environment prior to the experiments on the real engine. Experimental results highlight the NMPC’s ability to control the engine at its physical limits over the entire load range without inducing frequency oscillations using just one parameter set. This emphasizes the robustness of the presented approach, making it a promising solution for real-world applications.

Solar Energy Applications in Protected Agriculture: A Technical and Bibliometric Review of Greenhouse Systems and Solar Technologies

This study addresses solar energy applications in protected agriculture, focusing on greenhouses and related technologies. A bibliometric and technical analysis is developed, covering research published between 1976 and 2024, to identify the main trends and challenges in the use of solar energy in controlled environments. The methodology was based on the PRISMA approach, using the Scopus database to retrieve relevant documents. From an initial total of 221 documents, 216 were selected after a filtering and debugging process, ensuring the relevance of the final set. In the analytical phase, the results showed a moderate growth of 3.68% in the annual publication rate, highlighting the impact of research on solar energy’s application to air conditioning and energy efficiency in greenhouses. Most of the studies reviewed feature hybrid systems that combine solar energy with other resources, and we highlight both advances in climate control through artificial intelligence and the implementation of photovoltaic and thermal technologies to improve the energy efficiency of agricultural systems. The results also underline the importance of tomato cultivation in the selected studies, reflecting its global economic impact. The conclusions highlight the need for the further integration of energy storage and desalination technologies, especially in arid regions with high solar irradiation, to ensure the sustainability of greenhouses. It is proposed that future research should address the wider implementation of hybrid systems and advanced climate control technologies, optimizing both the use of energy resources and the performance of crops under cover. In addition, it is recommended that international collaboration be strengthened to address technical and climatic challenges in protected agriculture and to expand the adoption of innovative solutions in different geographical contexts.

Biofuels and Their Blends—A Review of the Effect of Low Carbon Fuels on Engine Performance

Energy is an important aspect concerning global economic development and environmental conservation. Economic growth has been accompanied by extensive use of fossil fuels, resulting in significant emissions of greenhouse gases and other pollutants. Therefore, researchers have turned their attention to low/zero carbon fuels. Among these, biofuels have attracted wide attention due to their relatively low cost, clean combustion products and renewability. This article reviews the combustion, performance and emission characteristics of internal combustion (IC) engines fueled with biofuels categorized into three generations by their raw material sources. According to most research findings, biofuels generally exhibit poorer combustion performance in IC engines compared to fossil fuels due to their high viscosity and low lower heating value. However, these biofuels, characterized by a high oxygen content, facilitate more complete combustion and reduce emissions of CO, UHC and smoke, albeit increasing NOx emission and fuel consumption. Both thermal efficiency and brake power also tend to decrease, but various optimization strategies such as advanced combustion modes or injection control methods can partially compensate for these drawbacks. In conclusion, biofuels should be a promising low-carbon fuel for IC engines in the future.

Energy Management in a Renewable-Based Microgrid Using a Model Predictive Control Method for Electrical Energy Storage Devices

In this paper, an energy management strategy is developed in a renewable energy-based microgrid composed of a wind farm, a battery energy storage system, and an electolyzer unit. The main objective of energy management in the studied microgrid is to guarantee a stable supply of electrical energy to local consumers. In addition, it encompasses hydrogen gas production by using part of the available excess energy in the system, which has some economic benefits. Also, energy management can protect the battery bank from damage by preventing the possibility of it being overcharged. These objectives should be achieved by developing a robust and effective control technique for DC-DC converters that are connected to energy storage devices. For this purpose, an advanced control technique based on Model Predictive Control, which is recognized as a popular control technique for industrial and process applications, is developed. This technique has a fast dynamic response and good tracking features and is simple to implement. The simulation results prove the effectiveness of the proposed control strategy and control technique for energy management in the studied renewable energy-based microgrid.

NSTTF Heliocon Wireless Closed-Loop Controls Test Bed Development

A closed loop controls test bed is in development at the Sandia National Laboratories (SNL), National Solar Thermal Test Facility (NSTTF) as part of the U.S. DOE SETO sponsored Heliostat Consortium (HelioCON) program. This work pertains to preliminary development of advanced feedback controls for a concentrating solar power (CSP) field of 218 heliostats, and the development of a baseline closed-loop controls extremum seeking control (ESC) algorithm. This algorithm utilizes a batch least squares (BLS) technique to facilitate feedback control automation for heliostat pointing. This allows the determination of the optimal highest flux within a Gaussian profile, for both a four-point (QuadCell) aim point strategy, and a concentric aim point strategy. The results of this work determined that both approaches using the ESC BLS were able to reduce pointing errors to zero for both azimuth and elevation heliostat position movements. This work also reviews progress of the test bed which will allow flexible employment of controls and sensors which will be communicating with both wired and wireless protocols. The solar field distributed control system (DCS) will manage the flux distribution of energy across test articles and solar receivers using real-time programmable logic controllers (PLC) at each heliostat, for aiming and closed-loop feedback. Feedback control will be facilitated with a variety of sensors, located: 1. On the heliostat, 2. On the tower or 3. At an ancillary field tower station. The system is also developed to incorporate environmental information to provide real-time feedback into advanced algorithms for solar field management.

Optimizing spectral phase transfer in four-wave mixing with gas-filled capillaries

Four-wave mixing (FWM) in gas-filled hollow-core capillaries, a nonlinear optical process that mixes signal and pump photon frequencies to generate idler frequency photons, offers a method for precise spectral phase transfer from signal to idler at ultrashort timescales and extreme powers. However, this regime is challenged by competing linear and nonlinear dynamics, leading to significant trade-offs between spectral phase transfer and conversion efficiency. Our computational investigation focuses on the upconversion of femtosecond pulses from the infrared (IR) to the ultraviolet (UV), a range notoriously difficult to manipulate. We explore an intermediate energy regime that strikes an optimal balance between FWM-mediated phase-transfer fidelity and nonlinear conversion efficiency. By adjusting the energy ratios and spectral phase profiles of the input signal, we achieve conversion efficiencies of approximately 5-15% while maintaining an effective quasi-linear spectral phase transfer. These findings will contribute to establishing first-principles and scaling laws essential for applications such as high-precision imaging, spectroscopy, quantum transduction, and distributed entangled interconnects, facilitating advanced control of ultrafast photonic and electronic wavepackets in quantum materials with unprecedented spatial and temporal precision.

Flood Risk Analysis of Urban Agglomerations in the Yangtze River Basin Under Extreme Precipitation Based on Remote Sensing Technology

Flooding is the most pervasive hydrological disaster globally. This study presents a comprehensive analysis of torrential rain and flood characteristics across three major urban agglomerations (CY, MRYR, and YRD) in the Yangtze River Basin from 1991 to 2020. Utilizing satellite-derived microwave SSM/I data and CHIRPS precipitation datasets, this study examines the impacts of urbanization and climate change on flood risk patterns. The results showed: (1) In 1998, the MRYR had the highest flood risk due to heavy rainfall and poor flood control, but by 2020, risk shifted to the CY with rapid urbanization and more rainfall, while the YRD maintained the lowest risk due to advanced flood control. (2) The relationship between impervious surface area and flood risk varied by region. The CY showed a negative correlation (−0.41), suggesting effective flood mitigation through topography and infrastructure; the MRYR had a slight positive correlation (0.12), indicating increased risks from urban expansion; and the YRD’s weak negative correlation (−0.18) reflected strong flood control systems. This research underscores the imperative of strategic urban planning and effective water resource management to mitigate future flood risks and contributes valuable insights to ongoing efforts in flood disaster prevention and control within the Yangtze River Basin.

Development of Grain Dryer Control Technology from the Perspective of Low Carbon and Intelligentization

The grain-drying process plays a critical role in grain storage and quality assurance. In recent years, with the advancement of low-carbon and intelligent technologies, the control technology of grain dryers has significantly improved. This paper systematically reviews the development status of grain dryer control technology from the perspective of low-carbon and intelligentization, analyzing the technological differences in control systems between domestic and international approaches. Current research challenges include the insufficient integration of control technologies with the drying process, limited control variables, the inadequate application of intelligent control strategies, and unstable sensor accuracy. To enhance the performance of grain-drying systems, this paper suggests optimizing control mechanisms, adopting efficient and environmentally friendly energy sources, improving sensor performance, introducing advanced intelligent control algorithms, and strengthening system monitoring capabilities. Looking ahead, with the further integration of AI, IoT, and green energy, grain-drying control systems are expected to evolve towards greater intelligence, remote operation, and low carbonization, providing technical support for enhancing drying efficiency and environmental performance.

Position–Force Control of a Lower-Limb Rehabilitation Robot Using a Force Feed-Forward and Compensative Gravity Proportional Derivative Method

The design and control of lower-limb rehabilitation robots for patients after a stroke has gained significant attention. This paper presents the dynamic analysis and control of a 3-degrees-of-freedom lower-limb rehabilitation robot using combined position–force control based on the force feed-forward and compensative gravity proportional derivative methods. In the lower-limb rehabilitation robot, the interaction force between the patient with the joints and links of the robot is uncertain and nonlinear due to the disturbance effect of Coriolis force, centrifugal force, gravitational force, and friction force. During recovery stages, the forces exerted by the patient’s lower limbs are also considered disturbances. Therefore, to meet the quality requirements in using the rehabilitation robot with different recovery stages of patient training, combining position control and force control is essential. In this paper, we proposed a combination of proportional–derivative gravity compensation motion control and force feed-forward control to form an advanced combined controller (position–force feed-forward control—PFFC) for a 3 DOF lower-limb functional rehabilitation robot. The forces can be sensed using a 3-axis force sensor. In addition, the robot’s position parameters are also measured by encoders. The control algorithm is implemented on the STM32F4 Discovery board. A verified test of the proposed control method is shown in the experiments, showing the good performance of the system.

Deep Reinforcement Learning for the Management of the Wall Regeneration Cycle in Wall-Bounded Turbulent Flows

AbstractThe wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforcement learning (DRL) for managing the wall regeneration cycle to achieve desired flow dynamics. To create a robust framework for DRL-based flow control, we have integrated the StableBaselines3 DRL libraries with the open-source direct numerical simulation (DNS) solver CaNS. The DRL agent interacts with the DNS environment, learning policies that modify wall boundary conditions to optimise objectives such as the reduction of the skin-friction coefficient or the enhancement of certain coherent structures’ features. The implementation makes use of the message-passing-interface (MPI) wrappers for efficient communication between the Python-based DRL agent and the DNS solver, ensuring scalability on high-performance computing architectures. Initial experiments demonstrate the capability of DRL to achieve drag reduction rates comparable with those achieved via traditional methods, although limited to short time intervals. We also propose a strategy to enhance the coherence of velocity streaks, assuming that maintaining straight streaks can inhibit instability and further reduce skin-friction. Our results highlight the promise of DRL in flow-control applications and underscore the need for more advanced control laws and objective functions. Future work will focus on optimising actuation intervals and exploring new computational architectures to extend the applicability and the efficiency of DRL in turbulent flow management.

Compact Analysis of the Necessity of Padé Approximation for Delayed Continuous-time Models in LQR, H-Infinity and Root Locus Control Strategies

This paper presents a comprehensive analysis of the need for the Padé approximation for continuous-time models with delays, focusing on its critical role in addressing the control challenges posed by time delays. Time delays, often referred to as dead times, transport delays or time lags, are inherent in a wide range of industrial and engineering processes. These delays introduce phase shifts that degrade control performance by reducing control bandwidth and threatening the stability of closed-loop systems. Accurate modelling and compensation of these delays is essential to maintain system stability and ensure effective control. This paper highlights the difficulties that arise when using advanced control techniques such as root locus (RL), linear quadratic regulator (LQR) and H-infinity (H_∞) control in systems with delays. Representing delays in exponential form leads to an infinite number of state problems, complicating the design and analysis of controllers in such systems. To address these challenges, the Padé approximation is proposed as an effective method for approximating time delays with rational polynomials of appropriate order. This approach allows for more accurate simulation, system analysis and controller design, thereby mitigating the problems caused by delays. The paper also provides a detailed comparative analysis between the Padé approximation and Taylor polynomials, demonstrating the superiority of the former in achieving accurate delay modelling and control performance. The results show that the use of Padé approximation not only improves the accuracy of system models, but also improves the robustness and stability of control strategies such as RL, LQR, and H_∞. These results highlight the importance of the Padé approximation as a valuable tool in the design of delay-affected control systems, offering significant advantages for both theoretical and practical applications.

Fractional order PID controller optimised by hybrid algorithm for instant torque control of IM motor

ABSTRACT ‘Direct torque control (DTC) is an advanced motion control technique introduced to produce fast torque control in Induction motors’. However, IM has encountered faults in the performance of flux, speed and torque. For improving the performance of induction motors, several controlling techniques were developed in the literature. This paper intends to propose an ‘Optimized Fractional Order Proportional Integral Controller (FoPID)’ for induction motors to accomplish an efficient control of speed by tuning the torque. Moreover, it is planned to exploit a direct instant model, where the speed error (variation amongst estimated speed and reference speed of induction motor) is given as input to the FoPID controller. As a major contribution, the FoPID controller parameters are fine-tuned by means of an innovative hybrid algorithm known as ‘Moth Flame Integrated Dragonfly Algorithm (MFI-DA), which combines both the concepts of Moth Flame Optimization (MFO) and Dragonfly (DA) model’. At last, the efficiency of the accepted method is verified by means of various measures.

Single motor driven soft actuator design based on nonuniform sheering auxetic structure

Abstract Grasping functionality is one of the core functionalities that a manipulator needs to possess. Traditional rigid manipulators are typically composed of rigid materials. However, when dealing with objects of complex shapes, irregular surfaces, or soft materials, rigid manipulators often face challenges in effectively grasping and stably holding objects due to their rigidity, which can lead to damage or failure. To address these issues, constructing manipulators using soft actuators can be highly advantageous. In most current research, soft actuators are typically composed of flexible materials and fluids, which can result in highly complex dynamic behaviors and nonlinear characteristics. This complexity makes precise modeling and control of soft actuators more challenging, necessitating the use of advanced control algorithms and techniques to mitigate nonlinear effects. In this study, we first establish a mathematical model for the torsional and bending deformations of non-uniform shearing-auxetic structures. Subsequently, we validate the reliability of the mathematical model through simulation. Finally, we design a soft actuator that requires only single-motor control, which is based on the structural design of non-uniform shearing-auxetic structures. This type of soft actuator not only simplifies control aspects but also facilitates practical modeling and manufacturing processes. Moreover, it’s capable of achieving spiral grasping functionality.

Dual mode variable voltage converter configuration using single topology: design and analysis

ABSTRACT This paper introduces an optimised design procedure for a DC–DC buck, boost and buck-boost configurations in a single circuit with an integrated dissipative snubber, aiming to enhance performance and efficiency. The conventional buck-boost converter’s versatility in adjusting the output voltage is complemented by a dissipative snubber, mitigating hard switching challenges in power semiconductors. Utilising advanced control techniques for seamless transitions between step-down and step-up modes, the proposed circuit ensures optimal operation across varying input voltages. The research delves into the snubber’s impact on limiting the maximum voltage stress across the switch, considering power losses in the snubber resistor. Simulation, analysis and experimental results validate the design’s effectiveness in meeting voltage regulation requirements, showcasing its adaptability to diverse electronic applications. This optimised design methodology holds promise for improving DC–DC converter efficiency in practical applications where mitigating switching losses and optimising voltage stress are critical considerations. Also this proposed converter provides output simultaneously for buck and boost configurations. The simulation and mathematical analysis are validated using the experimental prototype.