Traditional Control Strategy Research Articles

Adaptive event-triggering mechanism based Takagi-Sugeno fuzzy automatic generation controller design for offshore wind power system

Interconnected offshore wind power system (OWPS) attracts widespread attention due to its special advantage. With the development of wind power technology, higher requirements are put forward for the reliability of networked OWPS. Considering the nonlinear characteristics of OWPS and limited network bandwidth resource, an adaptive multi-event triggering mechanism based distributed Takagi-Sugeno fuzzy robust H∞ control scheme is investigated in this paper. A modeling method for OWPS integrating communication constraints is proposed. By Lyapunov stability theory, the sufficient condition for the asymptotical stability of OWPS has been derived. Compared with traditional control scheme, the proposed Takagi-Sugeno fuzzy controller provides smoother output power, smaller torque oscillation and higher wind energy utilization. Furthermore, the co-design strategy increases the utilization rate of network bandwidth. The feasibility of adaptive multi-event triggering mechanism based Takagi-Sugeno fuzzy controller is demonstrated through a simulation example.

Research on strategy of load-side resonant soft-switching inverter based on interconnection and damping assignment-passivity based control

Soft-switching technologies can effectively solve the problem of switching losses caused by increasing switching frequency of grid-connected inverters. As a branch of soft-switching technologies, load-side resonant soft-switching is a hotspot for applications of high-frequency inverters, because it has the advantage of achieving soft-switching without using additional components. However, the traditional PI control strategy based on the linear model is prone to destabilization and non-robust dynamic performance when large signal perturbation occurs. In this paper, a novel Passivity-Based Control (PBC) method is proposed to improve the dynamic performance of load-side resonant soft-switching grid-connected inverter. Besides, the model based on the Port Controlled Hamiltonian (PCH) model of the soft switching inverter is carried out, and the passivity-based controller is designed based on the established model using the way of interconnection and damping assignmentpassivity based control (IDA-PBC). Both stable performance and dynamic performance of the load-side resonant soft-switching inverter can be improved over the whole operating range. Finally, a 750 W load-side resonant soft-switching inverter simulation model is built and the output performance is compared with the traditional PI control strategy under stable and dynamic conditions. The simulation results show that the proposed control strategy reduces the harmonic distortion rate and improves the quality of the output waveforms.

Thermal Management of Proton‐Exchange Membrane Fuel Cells with Enhanced Fuzzy Control via an Improved Whale Optimization Algorithm

Proton exchange membrane fuel cells (PEMFCs) require an appropriate operating temperature to achieve high performance and longevity. In addressing the critical need for effective thermal management in PEMFCs for vehicle propulsion systems, this study has developed and introduced a novel thermal management system model equipped with dual‐cooling loops. Addressing the issues of slow response and poor dynamic performance inherent in traditional control strategies, as well as the computational complexity that hinders the practical application of existing advanced control strategies, this research proposes a fuzzy proportional‐integral‐derivative (PID) controller optimizing the improved whale optimization algorithm (IWOA‐FPID) aimed at enhancing fan side control. By simulating step changes in current load and designing dynamic operating conditions based on the most basic and simple energy management system, the performance of IWOA‐FPID in temperature control is validated. The final results indicate that the IWOA‐FPID strategy significantly outperforms existing control methods, notably reducing adjustment time and minimizing temperature overshoot to ≈0.5 K. The proposed framework achieves efficient temperature control, highlighting its potential for broader application in vehicular fuel cells and offering a promising solution to the challenges of PEMFC thermal management.

Control and Analysis of a Hybrid-Rotor Bearingless Switched Reluctance Motor with One-Phase Full-Period Suspension

In the traditional control scheme of a 12/8-pole bearingless switched reluctance motor (BSRM), radial force and torque are usually controlled as a compromise due to the conflict between their effective output areas. Additionally, each phase requires individual power circuits and is excited in turn to produce a continuous levitation force, resulting in high power device requirements and high controller costs. This paper discusses a 12/8-pole single-winding hybrid-rotor bearingless switched reluctance motor (HBSRM) with a hybrid rotor consisting of cylindrical and salient-pole lamination segments. The asymmetric rotor of the HBSRM slightly increases the complexity of its structure and magnetic circuit, but makes it possible to generate the desired radial force at any rotor angular position. A control scheme for the HBSRM is developed to utilize the independent excitation of the four windings in one phase to generate the desired levitation force at any rotor angular position, and it requires only half the number of power circuits used in the conventional control scheme of a 12/8-pole single-winding BSRM. Different from the average torque chosen to be controlled in traditional methods, this scheme directly regulates the instantaneous total torque produced by all excited phases together and presents a current algorithm to optimize the torque contribution of each phase so as to reduce torque pulsation, and the improved performance of this bearingless motor is finally validated by simulation analysis.

Feedforward decoupling control of indoor temperature and humidity using a single direct expansion air conditioning unit

Achieving simultaneous control over temperature and humidity through a single direct expansion air conditioner would be advantageous, especially considering its widespread use in residential buildings. Traditional control strategies fail to achieve satisfactory control performance due to the nature of complexity and cross-decoupling of the system. Therefore, this research paper presents the advancement of a feedforward decoupling control scheme designed for a direct expansion air conditioning unit to effectively control both temperature and humidity. To facilitate the controller design, a dynamic model considering both sensible and latent heat transfers was developed and verified. The thermal responses of the coupled system were analyzed and their correlation coefficients also examined. Through identifying the transfer function model of the coupled system, a feedforward decoupling controller with two compensators was designed. The proposed decoupling control scheme was tested using the developed model as the plant to be controlled. Test results showed that, in comparison to the transitional PID based dual single-input and single-output controller, the proposed decoupling control scheme was capable of eliminating the interactions between indoor air temperature and humidity under variable speed operation, and realizing the simultaneous control over indoor air temperature and humidity with a desirable control performance.

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field.

Enhancing grid-connected inverter performance under non-ideal grid conditions: a multi-functional multiplexing control strategy

Compared to the traditional thermal power generation, new energy sources, such as wind and photovoltaic systems, are more vulnerable to the effects of non-ideal power grids due to their limited capacity. This susceptibility can jeopardize the safe operation of power equipment, degrade power output quality, and lead to non-compliance with grid-connected specifications. The LCL-type grid-connected inverter is a typical nonlinear system that weakens the controllability of the grid-connected energy. To address these challenges, this study employs feedback linearization theory to transform the inverter into a standard linear system. Subsequently, it utilizes linear system methodologies to develop robust control laws, ultimately introducing a multi-functional multiplexing control strategy for grid-connected inverters based on feedback linearization and Hamilton-Jacobi-Issacs inequality. Simulation results demonstrate that this multi-functional strategy outperforms traditional grid-connected inverter control schemes, effectively mitigating issues related to low short-circuit ratios, voltage fluctuations, imbalances, harmonics, and other non-ideal grid conditions. Furthermore, it significantly expands the system’s adaptability to varying weak grid impedances.

Super-Twisting Extended State Observer-Based Quasi-Proportional-Resonant Controller for Permanent Magnet Synchronous Motor Drive System

For the purpose of realizing permanent magnet synchronous motor (PMSM) systems with strong robustness and smooth speed, the quasi-proportional-resonant (QPR) controller is combined with super-twisting extended state observer (STESO) in this paper. Although traditional active disturbance rejection control scheme generally selects a sufficiently large bandwidth of linear extended state observer (LESO) to enhance the time-varying disturbance rejection capacity, the bandwidth of LESO is limited by system stiffness. To solve this tough issue, the STESO using generalized super-twisting technique is developed to optimize the stability and anti-disturbance capacity of motor drive system. In addition, the QPR controller is combined with the proposed STESO to suppress the motor speed fluctuation. Experimental results show that the proposed STESO-based QPR control algorithm has satisfactory characteristics.

Reinforcement learning-based control for waste biorefining processes under uncertainty

Waste biorefining processes face significant challenges related to the variability of feedstocks. The supply and composition of multiple feedstocks in these processes can be uncertain, making it difficult to achieve economically feasible and sustainable waste valorization for large-scale production. Here, we introduce a reinforcement learning-based framework that aims to control these uncertainties and improve the efficiency of the process. The framework is tested on an anaerobic digestion process and is found to perform better than traditional control strategies. In the short term, it achieves faster target tracking with increased precision and accuracy, while in the long term, it shows adaptive and robust behavior even under additional seasonal supply variability, meeting downstream demand with high probability. This reinforcement learning-based framework offers a promising and scalable solution to address uncertainty issues in real-world biorefining processes. If implemented, this framework could contribute to sustainable waste management practices globally, making waste biorefining processes more economically viable and environmentally friendly.

Enhancing Power Quality with PDO-FOPID Controller in Unified Power Quality Conditioner for Grid-Connected Hybrid Renewables

Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation, load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems, thereby enhancing overall grid performance and the efficacy of renewable energy integration.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Stability Analysis of Buck Converter Based on Passivity-Based Stability Criterion

Recently, the stability of DC microgrids has attracted increasing attention. The traditional stability analysis method cannot not meet the requirements for the complexity and bidirectional energy flow of the system. In this paper, a passivity-based stability criterion (PBSC) is proposed to analyze the stability of the cascade system. In order to realize the passivity of the system, an improved feedback control method based on the traditional double-loop control strategy is proposed, which will improve the stability region and guarantee the passivity of the system. Moreover, a Buck-CPL simulation model is established based on MATLAB/Simulink R2008, and the correctness of the theoretical analysis is verified by experiments.

Performance analysis of optimized differential power processing converter with switched inductor using enhanced fuzzy logic control for solar photovoltaic systems

In the current landscape of renewable energy systems, optimizing the performance of power electronic converters is crucial for ensuring reliability. Within the realm of direct current (DC)–DC power converter systems, the differential power processing (DPP) converter holds promise. However, realizing its full potential requires meticulous system design, especially in response to varying solar irradiation levels. Poor design can result in performance degradation, leading to system damage and voltage instability due to sudden irradiation fluctuations. To address these challenges, this study investigates the performance optimization of a DPP converter enhanced with a modified switched inductor, tailored for solar photovoltaic applications. To overcome traditional control strategy limitations, we propose an innovative enhanced fuzzy logic controller (E-FLC). This controller’s strength lies in its dynamic adaptability, achieved through variable duty cycle control, input parameters, membership functions, and output responses. This paper emphasizes methodological precision, particularly in applying the E-FLC to the modified switched inductor. The use of this controller significantly improves both steady-state and transient response performance compared to traditional switched inductors. It rigorously analyzes the responses of the DPP converter under steady-state and transient conditions, with and without the modified switched inductor. This analytical approach sheds light on a critical yet often overlooked aspect of photovoltaic systems. The core innovation driving this research is the adoption of the E-FLC, which outperforms the commonly used proportional–integral (PI) controller in steady-state performance and transient response characteristics. This paper goes beyond conventional converter optimization studies by introducing a holistic approach encompassing system dynamics, control strategy innovation, and performance evaluation. The proposed E-FLC represents a methodological breakthrough and a substantial improvement in converter efficiency and reliability. As renewable energy continues to reshape the global energy landscape, this research sets a new standard for harnessing the true capabilities of power electronic converters in solar photovoltaic systems.

Force Tracking Control Method for Robotic Ultrasound Scanning System under Soft Uncertain Environment

Robotic ultrasound scanning has excellent potential to reduce physician workload, obtain higher-quality imaging, and reduce costs. However, the traditional admittance control strategy for robotics cannot meet the high-precision force control requirements for robots, which are critical for improving image quality and ensuring patient safety. In this study, an integral adaptive admittance control strategy is proposed for contact force control between an ultrasound probe and human skin to enhance the accuracy of force tracking. First, a robotic ultrasound scanning system is proposed, and the system’s overall workflow is introduced. Second, an adaptive admittance control strategy is designed to estimate the uncertain environmental information online, and the estimated parameters are used to modify the reference trajectory. On the basis of ensuring the stability of the system, an integral controller is then introduced to improve the steady-state response. Subsequently, the stability of the proposed strategy is analysed. In addition, a gravity compensation process is proposed to obtain the actual contact force. Finally, through a simulation analysis, the effectiveness of the strategy is discussed. Simultaneously, a series of experiments are carried out on the robotic ultrasound scanning system, and the results show that the strategy can successfully maintain a constant contact force under soft uncertain environments, which effectively improves the efficiency of scanning.

Study on CLLC-SMES System based on the Passivity Control Strategy

Background: SMES systems as power compensation devices can effectively improve the transient stability of the power system. Due to the nonlinear and strongly coupled characteristics of the compensation device, an effective transfer function cannot be established, such that the traditional PI control by the linearization cannot accurately describe the complex nonlinear system. Objective: In this paper, a passive control strategy is introduced for the SMES System based on CLLC resonant converter to solve the problems that the traditional PI control cannot accurately describe the complex nonlinear system and the parameters’ settings are complicated. Methods: First, according to KVL and KCL, the mathematical model of the SMES system based on the CLLC resonant converter in the (d, q) coordinates is derived and established. Second, based on passive control theory, the port-controlled dissipation Hamiltonian model of CLLC-SMES is given. Third, combined with the passivity of SMES, the energy equation is established and the active and reactive power are analyzed respectively for the balanceable expectation, and then the energy equation is solved to obtain the drive signal of the switch tube. Fourth, the stability of the passive controller is verified by the Lyapunov equation, and the feasibility of the passive control strategy of CLLC-SMES is verified by simulation. Results: The results show that compared with the traditional PI control strategy, the power compensation system based on the passive control strategy does not need to establish the transfer function and the parameters are simple to adjust. Conclusion: It can not only track the active and reactive power commands quickly and accurately but also improve the transient state of the power system effectively.

Energy management strategy of urban rail hybrid energy storage system based on fuzzy logic system

Energy management is an important link in the effective functioning of hybrid energy storage systems (HESS) within urban rail trains. This factor significantly impacts the operational stability and economic efficiency of urban rail systems. Safety issues arise from DC bus voltage fluctuations due to varying train conditions. To address these issues, this paper proposes an energy management strategy for the urban rail HESS, which builds upon a traditional double closed-loop control strategy. This strategy incorporates a fuzzy logic system to regulate power distribution parameters and discharge thresholds dynamically. Real-time monitoring of the state of charge (SOC) of the energy storage device informs the decision-making process, utilizing a fuzzy inference system to effectively regulate power and energy distribution in response to the distinct characteristics of batteries and super-capacitors. This approach aims to mitigate power fluctuations within the traction network. To validate the effectiveness of the proposed energy management strategy, the study conducts a comprehensive simulation experiment using MATLAB/Simulink. The simulation results confirm the efficacy of the proposed strategy, highlighting its potential to enhance the stability and overall performance of urban rail train operations.

Low voltage ride through control strategy of permanent magnet wind turbine based on improved virtual synchronous generator control

Aiming at the shortcomings of traditional low voltage control strategies for permanent magnet wind turbines in supporting microgrid stability and the inability of traditional virtual synchronous generator (VSG) control to ride through voltage drops, this paper proposes an improved control low voltage ride through (LVRT) strategy based on traditional low voltage control and VSG control methods. This strategy switches to reactive priority mode during the LVRT period, enabling VSG control to have LVRT function and meet the LVRT requirements standards. The simulation results demonstrate the effectiveness of the strategy.

A 4-Phase Combined Adhesion Threshold Algorithm for Wheel Slide Protection Systems in Rail Vehicles

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption.

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

HRL-Based Access Control for Wireless Communications With Energy Harvesting

This paper studies the access control problem of long-term throughput maximization in wireless communication systems with Energy Harvesting (EH). In the existing research, many access schemes based on accurate environmental information have been proposed, such as channel information and the EH process. However, access to environmental information is costly, and traditional access control frameworks are expensive to explore in high-dimensional spaces. Thus, an access control framework based on hierarchical reinforcement learning (HRL) is proposed in this paper. In HRL, the control problem in the Markov decision process (MDP) form is decomposed into a multilevel sequential control problem. It includes high-level channel number selection, mid-level channel selection, and low-level channel matching subproblems. The scheme is obtained by combining the solutions of subproblems at different level which are solved in sequence. In addition, to improve learning efficiency, the deterministic action (DA) module and the prior knowledge (PK) module are put forward. The DA module solves the channel matching problem under the additional guidance given by the previous subproblem, which selects definite good low-level actions. The PK module provides the framework with the common knowledge of the system structure learned from the hypothetical environment, so as to obtain better initial performance. Experimental results show that our framework achieves better performance and better learning efficiency compared with several recent transmission schemes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Access control is an important issue in wireless communication systems, and users need to be scheduled to solve the constraint of limited resources, such as energy usually provided by batteries. In recent years, in order to overcome the energy limitation, energy harvesting devices have been developed and applied to wireless communication systems. However, the energy collection ability of the system is greatly influenced by the environment, which leads to the poor performance of most traditional control schemes that rely on the prior knowledge of the environment. Therefore, this paper proposes a novel hierarchical reinforcement learning (HRL)-based model-free access control framework for wireless communication system to maximize the system throughput without any prior environmental knowledge. The scheme abstracts the original control problem into three sub-control sub control problems according to tasks and solves them sequentially, thus simplifying the original control problem. This scheme can not only learn independently, but also does not depend on the prior knowledge of the environment. Moreover, this method is also suitable for the large-scale environment while the conventional end-to-end reinforcement learning is not suitable for. Compared with traditional algorithms, our method has better performance and higher learning efficiency.