Hybrid Control Research Articles

Adaptive Fast-Optimization Predictive Control of MMC With Improved Hybrid Control Framework

Adaptive Fast-Optimization Predictive Control of MMC With Improved Hybrid Control Framework

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid

This study relates to the enhancement in performance of a Hybrid Energy Storage System (HESS) over a DC microgrid, particularly toward a fast-accurate controller of the DC bus voltage. Conventional control methods are particularly difficult to implement in order to reduce start-up time with minimum overshoot, resulting in a significant disturbance of the DC bus voltage. To circumvent these problems, we propose an innovative hybrid controller method that combines the Integral Super-Twisting Algorithm (ISTA) controller technique with adaptive gain in regulating DC bus voltage and current. The capacity of ISTA to greatly enhance the system's dynamic responsiveness, guaranteeing steady operation with effective output voltage control, is by far its greatest benefit. Comparative testing found that the conventional PI controller had a rise time of 0.0666 s, a settling time of 0.165 s, and a 17 % overshoot with a steady-state error of 0.02 %. The proposed ISTA controller had a rise time of 0.0016 s, a settling time of 0.0667 s, zero percentage overshoot, and a minimal steady-state error of only 0.0003 %. The above-mentioned issues mostly focused on better sharing power in HESS and a more effective controller of the DC bus voltage. Furthermore, we evaluate the practical feasibility of the suggested controller through hardware-in-the-loop testing. This research lays a foundation for advancing renewable energy integration in DC microgrid, offering a promising avenue for sustainable and resilient power systems. Finally, the effectiveness and applicability of the proposed ISTA controller for DC microgrid use are proven with Controller Hardware-in-the-Loop (C-HIL) simulations.

Asymptotic Synchronization for Caputo Fractional-Order Time-Delayed Cellar Neural Networks with Multiple Fuzzy Operators and Partial Uncertainties via Mixed Impulsive Feedback Control

To construct Caputo fractional-order time-delayed cellar neural networks (FOTDCNNs) that characterize real environments, this article introduces partial uncertainties, fuzzy operators, and nonlinear activation functions into the network models. Specifically, both the fuzzy AND operator and the fuzzy OR operator are contemplated in the master–slave systems. In response to the properties of the considered cellar neural networks (NNs), this article designs a new class of mixed control protocols that utilize both the error feedback information of systems and the sampling information of impulse moments to achieve network synchronization tasks. This approach overcomes the interference of time delays and uncertainties on network stability. By integrating the fractional-order comparison principle, fractional-order stability theory, and hybrid control schemes, readily verifiable asymptotic synchronization conditions for the studied fuzzy cellar NNs are established, and the range of system parameters is determined. Unlike previous results, the impulse gain spectrum considered in this study is no longer confined to a local interval (−2,0) and can be extended to almost the entire real number domain. This spectrum extension relaxes the synchronization conditions, ensuring a broader applicability of the proposed control schemes.

Efficient control scheme development for a highly integrated multi-component distillation process

This research aims to address intricate control challenges in a highly integrated multi-component distillation process within a polyolefin elastomers plant. The studied process incorporates various process intensification techniques into a unified unit, resulting in strong interactivity and presenting unique control challenges. To tackle these challenges, a rigorous mathematical model of the distillation process is established, and index reduction is employed to handle the high-index DAE system. Various control structures and algorithms are utilized to comprehensively assess their feasibility and effectiveness in disturbance rejection, setpoint tracking, and handling inaccurate measurements. The control structures include temperature inferential control, temperature-composition hybrid control, and composition control. The control algorithms encompass PI control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC). Results indicate that both temperature-composition hybrid control and composition control exhibit comparable and superior performance. This suggests that temperature-composition hybrid control is an effective and cost-efficient configuration for complex distillation systems. The dynamic responses of MPC outperform those of PI control, as evidenced by a reduction in the integral of the absolute error. NMPC exhibits the most outstanding performance in all scenarios, showcasing the smallest maximum deviations and shortest settling times.

A Feedback Control Strategy to Regulate the Temperature in a Nonlinear Solar Receiver

Abstract The substantial energy provided by the sun is a promising substitute for traditional heat sources in various industrial applications. However, the transient nature of solar energy still poses a significant challenge to its widespread utilization. This work presents a methodology for regulating the temperature within a solar receiver by dynamically adjusting incoming sunlight through the aperture using a controlled iris mechanism. The performance of this technique is experimentally compared with the gas flowrate control method, which is typically used in industry. The proposed control system, grounded in the physical model of the solar receiver, underwent experimental testing under varying conditions, including different gas flowrates, simulator power levels, and aperture sizes. The collected data were then analyzed to estimate a simplified model of the solar receiver. A model predictive controller (MPC) is implemented using the model estimations, and its performance was assessed by tracking two set points (335 and 325 °C) over a period of 2 h. The experimental testing of both control systems indicates the superiority of iris mechanism over gas flowrate controller in terms of robustness, settling time, and smoothness. A hybrid control system utilizing both aperture size and gas flowrate is also developed and tested during the operation of the solar receiver via computer simulations.

Divergent visual ecology of Drosophila species drives object-tracking strategies matched to landscape sparsity

Maintaining stable gaze while tracking moving objects is commonplace across animal taxa, yet how diverse ecological needs impact these processes is poorly understood. During flight, the fruit-eating fly Drosophila melanogaster maintains course by making smooth steering adjustments to fixate the image of the distant visual background on the retina, while executing body saccades to investigate nearby objects such as food sources. Cactophilic Drosophila mojavensis live where there is no canopy; rather, the flora forming visual "background" and "objects" are one and the same. We tested whether D.mojavensis have adapted their flight control strategies for a visually sparse landscape. We used a magnetic tether that allows free movement in the yaw axis. In response to a textured bar moving across a similarly textured stationary background, D.melanogaster fixates the background, thereby stabilizing gaze while integrating bar dynamics to trigger tracking saccades. By contrast, two mojavensis subspecies in the repleta subgroup and one species in the melanogaster subgroup steer to smoothly fixate the bar, seemingly ignoring the stationary surround. Desert flies execute frequent bar-tracking saccades, but theirs are triggered when rotational velocity lags the bar. Thus, D.melanogaster, which lives in visually cluttered cosmopolitan habitats, leverages the optical disparities between nearby objects and distant foliage for a hybrid control strategy: "ground-fixate, object-saccade." Flies in distant phylogenetic subgroups with similar visual ecology use a "fixate-and-saccade" strategy, which would be adaptive in a visually sparse environment where individual landscape features are both approached and used to maintain a straight course.

Uniform exponential stability approximations of semi‐discretization schemes for two hybrid systems

The uniform exponential stabilities (UESs) of two hybrid control systems comprised of a wave equation and a second‐order ordinary differential equation are investigated in this study. Linear feedback law and local viscosity are considered, as are nonlinear feedback law and internal anti‐damping. The hybrid system is first reduced to a first‐order port‐Hamiltonian system with dynamical boundary conditions, and the resulting system is discretized using the average central‐difference scheme. Second, the UES of the discrete system is obtained without prior knowledge of the exponential stability of the continuous system. The frequency domain characterization of UES for a family of contractive semigroups and the discrete multiplier approach are used to validate the main conclusions. Finally, the Trotter–Kato theorem is used to perform a convergence study on the numerical approximation approach. Most notably, the exponential stability of the continuous system is derived by the convergence of energy and UES, which is a novel approach to studying the exponential stability of some complex systems. Numerical simulation is used to validate the effectiveness of the numerical approximating strategy.

Collaborative human and computer controls of smart machines – A proposed hybrid control

Human-Machine Interaction (HMI) and Brain-Computer Interface (BCI) are evolving technologies that show the great potentials to extract and utilize humans’ intents in controlling smart machines. However, existing HMI and BCI technologies are limited in terms of (1) the number of Degrees- of-Freedom (DoF) to be controlled and (2) the ways the performance of BCI-enabled control systems are verified and validated. This study aimed to explore the solutions to addree both of above concerns; we proposed a hybrid control system that is capable of training, detecting, and interpreting humans’ intents, and utilizing humans’ intents in real-time controls of smart machines. More specifically, the system acquired brain signals in the form of Electroencephalography (EEG) by an Emotiv Epoc X and processed these signals to detect and extract humans’ intents in real-time machine controls. To cope with the frequency difference of humans’ thinking and machine motion controls, we developed a hybrid control module to fuse humans’ and machine's intelligence so that low-frequency humans’ intents could be used in real-time machine controls. The system was prototyped and verified experimentally. The system was verified to achieve the accuracy of over 90 % in recognizing humans’ intents and controlling a robot by the operator's intents with a satisfactory responding time and accuracy.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Rational Design of Thick Electrodes in Lithium‐Ion Batteries by Re‐Understanding the Relationship Between Thermodynamics and Kinetics

AbstractTremendous efforts are made to enhance the energy density of lithium‐ion batteries, among which designing thick electrodes is a promising approach. Traditionally, kinetic effects are considered in constructing thick electrodes, such as decreasing the tortuosity to facilitate ion transport. This work innovatively investigates the coupling effect of kinetics and thermodynamics on electrode processes and conducts a competitive analysis between them by visualizing electrode processes. The results indicate that a sloping equilibrium potential curve facilitates the uniform utilization of electrodes, but severe kinetic constraints render the thermodynamic regulation ineffective. Thus, modifying the thermodynamic properties of the electrode to strengthen the regulatory effect is a promising approach. Kinetic constraints are the inherent factors limiting the capacity release of batteries. An in‐depth analysis reveals that ensuring the hybrid control of ions and electrons can significantly alleviate kinetic reaction heterogeneity. As a proof‐of‐concept, thick electrodes with vertical channels are constructed, enabling thermodynamics to regain dominance in the electrode process, ultimately achieving outstanding capacity retention of 63% at 4C. This work provides a more comprehensive perspective and represents a significant breakthrough in guiding electrode design to achieve a higher energy density.

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

Automatic Aircraft Landing System: A Review

The aircraft landing phase, marking the finishing of a flight plane, is a critical yet dangerous aerial maneuver. Even though it might seem simple, predicting the complexities of performance during landing presents a big challenge due to the dynamic characteristics of the phase, interaction with piloting methods, as well as inherent uncertainties in aerodynamics. Landing is executed in close proximity to the ground at reduced airspeed, the landing phase entails an escalated safety risk. Notably, incidents and accidents, particularly overruns where aircraft fail to slow sufficiently on the runway before landing, underscore the imperative for advanced landing technologies. This paper presents a comprehensive review of research spanning from 2000 to the present exploring smart techniques like fuzzy logic and machine learning, an array of control strategies ranging from classic PID control to sophisticated hybrid control approaches, and optimization procedures utilizing diverse optimization algorithms. The primary objective is to provide a comprehensive evaluation of the existing research landscape, offering insights that can propel the evolution of strategies for safer and more efficient landings and making sure the plane touches down safely.

Spatially hybrid control of entanglement between atom and photon

In this study, we introduced a model utilizing a plasmonic nanostructure to modulate the entanglement between atoms and photons within a V-type atomic medium. This system is influenced by composite optical vortex light (COVL) and two incoherent pumping fields. The plasmonic nanostructure comprises dielectric nanospheres, which have been thoroughly investigated (Phys. Rev. B 106, 035419, 2022). By adjusting the orbital angular momentum (OAM) of COVL, we demonstrate that the nanostructure induces significant spatial variations in the degree of entanglement (DEM). Additionally, incorporating a weak incoherent pumping field affects the DEM profile within the hybrid system. Our findings reveal that the OAM of structured light can govern the spatial dependence of DEM, particularly when quantum interference from the nanostructure and pumping fields surpasses a specific threshold. Furthermore, our results suggest that analyzing the DEM profile's spatial regions can be employed to determine the OAM number of the structured light.

Ultrasonic Obstacle Avoidance and Full-Speed-Range Hybrid Control for Intelligent Garages.

In the current study, which focuses on the operational safety problem in intelligent three-dimensional garages, an obstacle avoidance measurement and control scheme for the AGV parking robot is proposed. Under the premise of high-precision distance detection using Kalman filtering, a mathematical model of a brushless DC (BLDC) motor with full-speed range hybrid control is established. MATLAB/Simulink (R2022a) is used to build the control model, which has dual closed-loop vector-controlled motors in the low- to medium-speed range, with photoelectric encoders for speed feedback. The simulation results show that, at lower to medium speeds, the maximum overshoot of the output response curve is 1.5%, and the response time is 0.01 s. However, at higher speeds, there is significant jitter in the speed output waveform. Therefore, the speed feedback is switched to a sliding mode observer (SMO) instead of the original speed sensor at high speeds. Experiments show that, based on the SMO, the problem of speed waveform jitter at high motor speeds can be significantly improved, and the BLDC motor system has strong robustness. The above shows that the motor speed under the full-speed range hybrid control system can meet the AGV control and safety requirements.

Integration and Optimization of Multisource Electric Vehicles: A Critical Review of Hybrid Energy Systems, Topologies, and Control Algorithms

Electric vehicles (EVs) are pivotal in addressing the escalating environmental crisis. While EV drivetrains excel compared to those of vehicles with internal combustion engines (ICEs), their energy storage systems are hampered by limited range, lifespan, and lengthy charging times. Hybrid energy storage systems (HESSs) present a viable current solution to these issues. This review thoroughly explores the state of the art in the emerging field of multisource EVs that utilize HESSs, incorporating any combination of batteries (BTs), supercapacitors (SCs), flywheels (FWs), fuel cells (FCs), and/or transmotors. In addition, the paper systematically categorizes and evaluates different hybrid configurations, detailing potential topologies and their respective advantages and limitations. Moreover, the paper examines diverse control algorithms used to manage these complex systems, focusing on their effectiveness and operational efficiency. By identifying current research gaps and technological challenges, this study aims to delineate future research directions that could enhance the deployment and optimization of multisource EVs, thereby addressing critical challenges such as energy density, system reliability, and cost-effectiveness.

A novel weighted exponential sliding mode controller with a modified reaching law for the frequency regulation of a renewable integrated isolated AC microgrid

This paper investigates the development and application of a novel hybrid controller through novel sliding surface and novel reaching law, Weighted Exponential Sliding Mode Control with Modified Reaching Law (WESMC-MRL), for frequency regulation in an isolated Microgrid (MG) with intermittent Renewable Energy Sources (RESs) such as solar and wind. Major contributions include the analysis of frequency regulation of isolated MG controlled by WESMC-MRL under various load perturbations. The performance of WESMC-MRL is compared with conventional Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Integral Sliding Mode Control (ISMC) controllers. Outcomes indicate that the WESMC-MRL effectively regulates the MG frequency with minimal chattering, and outperforms LQR SMC, and ISMC in terms of settling time, overshooting, and undershooting in frequency deviation. With the WESMC-MRL the maximum frequency deviation is found only 0.00724 Hz and undershoot is negligible. The proposed controller is also robust against RES integration variability, disturbances, and nonlinearities making it a promising solution for reliable and stable MG operation.

Hybrid Control Policy for Artificial Pancreas Via Ensemble Deep Reinforcement Learning.

The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.

A composite motion control method for quadruped robots in trot gait

This work aims to address the issues of instability in trot gait walking and poor terrain adaptability in rugged environments under conventional control strategies for quadruped robots by proposing a novel composite control strategy. During the support phase, a separated force-position mixing control method is employed, utilizing gravity-compensated PD (Proportional Derivative) control at the lateral swing and knee joints while employing position control at the hip joint. During the swing phase, VMC (Virtual Model Control) is used to construct three sets of virtual spring-damper components, guiding the foot to move along a predetermined trajectory and converting virtual forces into joint torques for the swinging leg. At the body, IMU (Inertial Measurement Unit) feedback data combined with PI (Proportional Integral) control is used to adjust leg length and maintain the robot’s posture stability. Finally, the proposed separated force-position hybrid control method is theoretically validated using the Lyapunov stability criterion, and simulation tests on flat and rugged terrains under the quadruped robot’s composite control method are conducted using MATLAB/Simulink, comparing it with the VMC and position control methods. The findings indicate that the composite control strategy leads to smaller changes in the attitude angles and reduced impact forces at the feet during the quadruped robot’s walk on flat ground while also demonstrating strong adaptability to rugged terrains.

A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

2DOF-PID-TD: A new hybrid control approach of load frequency control in an interconnected thermal-hydro power system

The Load Frequency Control (LFC) scheme, with its primary aim being the maintenance of uniform frequency, has been a heavily researched topic for decades. Achieving a consistent frequency necessitates a delicate balance between load demand and power generation. Researchers strive to find an optimal solution within the LFC domain—one that can effectively withstand drastic load fluctuations. Despite a plethora of efforts, the LFC dilemma remains unresolved, complicated by factors such as dwindling demand-supply and the rapid integration of renewables. Furthermore, the lack of innovation in controller structure design exacerbates the complexity of solving modern LFC problems. Consequently, a robust control approach capable of handling uncertainties while simultaneously regulating system frequency becomes crucial. In light of this, we propose a novel hybrid control architecture called 2DOF-PID-TD. This architecture combines Two Degrees Of Freedom Proportional-Integral-Derivative (2DOF-PID) and Tilt-Derivative (TD) controllers. To optimize the proposed controller, we employ a metaheuristic called the Artificial Gorilla Troops Optimizer (AGTO), which mimics the social behavior and intelligence of gorilla troops. The proposed approach is analyzed in a realistic multi-area multi-source hydro-thermal system, accounting for nonlinearities, random load perturbations, and system parametric uncertainties. Experimental results, when compared with current state-of-the-art optimization algorithms and traditional controller structures, demonstrate the prowess of our approach in terms of precision, robustness, and resilience.