Control Scheme Research Articles

Event-driven intelligent fault-tolerant containment control for nonlinear multiagent systems with unknown disturbances

This paper investigates the event-driven intelligent fault-tolerant containment control problem for nonlinear multiagent systems affected by unknown disturbances, and conducts a comprehensive analysis of the dynamic stability of the system. Reduced-order modelling is employed for system modelling and identification, while radial basis function neural networks are utilised to estimate the nonlinear functions of the system model. An event-driven control scheme is designed based on the backstepping method and Lyapunov functional approach. In contrast to existing consensus control schemes, the proposed event-driven control schemes are specifically tailored to improve the energy efficiency and endurance of nonlinear multiagent systems operating in complex environments. Under the proposed control law, the output of each follower is converge to the convex hull spanned by the outputs of the leaders. The effectiveness of the proposed method is rigorously validated through numerical simulations. Furthermore, a practical example involving the design of a marine surface vehicle using advanced robotics technology is presented to further substantiate the efficacy of the proposed method.

Miyazaki Vermin Repulsion Robot and Its Adjustable Acousto-Optic Stimulus Generation Scheme

One of the most pressing issues in livestock farming is the protection of economically valuable livestock. The prevention and the treatment of infectious diseases are directly related to maintaining stable livestock output. Vermin is a primary source of livestock infection, resulting in the occurrence and expansion of epidemic diseases. To protect livestock against infections caused by epidemic diseases, this study proposes a vermin repulsion system called the Miyazaki Vermin Repulsion Robot (MiVeReR). Different from existing vermin repulsion systems, the development objective of MiVeReR is to repel vermin rather than kill them. In particular, MiVeReR generates changeable acousto-optic signals as repulsion signals for wild animals. Furthermore, MiVeReR employs image data to monitor the invasion of wild animals and their location data to track them, and accurately focuses the generated signals on them. These acousto-optic stimuli can be changed based on the reactions of the intruder through the feedback of the image data to ensure the effectiveness of the repulsion motions for vermin. Details on the hardware configuration of MiVeReR and its control scheme are explained. As a first step to develop MiVeReR, we attempted to repel vermin such as mice and wild cats from farm environments. Extensive experiments were conducted to verify the effectiveness of MiVeReR and the proposed control solution. Through experiments in wild environments, the feasibility of MiVeReR was inspected. The results of this study are concretely described.

Advanced frequency control schemes and technical analysis for large-scale PEM and Alkaline electrolyzer plants in renewable-based power systems

Integrating electrolyzers into power systems can significantly contribute to sustainable energy via the generation of green hydrogen while also enhancing frequency stability through effective regulation of the electrolyzers’ operating power. This study gives a comprehensive analysis of large-scale electrolyzer plants when providing frequency support to power systems. First, the authors present a model predictive control (MPC)-based secondary frequency controller, combined with a droop controller as the primary frequency controller and a virtual inertia controller. Additionally, the study introduces a universal system frequency response (U-SFR) modeling approach that enables high accuracy, low computation burden, and reduced initial parameters as a testbed. Finally, an in-depth analysis is conducted, focusing on different technical aspects of large-scale electrolyzer plants when providing frequency support services. Case studies integrating PEM and Alkaline electrolyzers into the modified IEEE 39-bus system with over 50% wind power penetration are conducted. It is found that the proposed U-SFR model achieves high accuracy with lower computational time compared to detailed physical models. Additionally, model predictive controllers improve frequency quality more effectively than PID and PID-FLC methods. PEM electrolyzers are found to be more efficient in providing grid frequency support than alkaline electrolyzers due to their technical characteristics. Finally, smaller hydrogen tanks may frequently breach storage constraints, negatively impacting the system’s frequency response capability.

Finite-Time Dynamic Event-Triggered Full-State Constraints Consensus Control for Multiagent Systems with Switching Topologies and Mismatched Disturbances

This paper investigates finite-time dynamic event-triggered full-state constraints consensus control for nonlinear multiagent systems with switching topologies and unknown disturbances. First, all the system states will not exceed the predefined range, which has guiding significance for the constraint control of velocity and position in multiagent systems. Second, to balance the control errors and event-triggered control, a novel finite-time dynamic event-triggered scheme is developed for multiagent systems, which can achieve fast convergence rates and save communication resources. Third, to improve the system performances, a finite-time command filter is designed to remove transient errors resulting from filtering errors and explosion of complexity. All closed-loop signals are proved to be bounded, and the tracking errors quickly converge into a narrow neighborhood of zero in finite-time. To sum up, the proposed control scheme balances the effects of unknown disturbances, unknown nonlinear dynamics, and limited communications bandwidth, which can improve the control performance for the multiagent systems under time-varying switching topologies. Finally, simulation results validate the suggested approach using a mechanical platform model.

A dual-standby power source configuration scheme for micro grid in Pelagic Island

Realizing clean energy supply is important for the sustainable development of pelagic island. The volatility and randomness features of renewable energy generation may reduce power supply reliability, thus backup units is necessary in micro-grid in pelagic island. This paper proposed a dual-standby configuration scheme based on Sequence Operation Theory to achieve reliable power standby. Firstly, characteristics of standby units, micro gas turbine and diesel generator, are analyzed. Next, the probability distribution function models of wind, photovoltaic and load demand in pelagic island are established, and the reliability index of power shortage probability is calculated by Sequence Operation Theory. Then, the two-stage dual-standby control scheme to make full use of the complementary standby units is introduced and object function to minimize equivalent annual cost on the premise of satisfying probabilistic reliability index is modeled. Hybrid integer particle swarm algorithm is used to solve the problem. Finally, the proposed model for dual-standby configuration is applied to a pelagic island. The simulation results show that the proposed method can meet the load demand in both daily and extreme weather and achieve superior economy.

Spatial adiabatic passage of ultracold atoms in optical tweezers.

Coherent manipulation of matter waves, a distinctive hallmark of quantum mechanics, is fundamental to modern quantum technologies. Spatial adiabatic passage (SAP) is a prime example of this phenomenon, where a wave packet is transferred between two uncoupled localized modes by adjusting the tunneling coupling to an intermediate third mode in a counterintuitive sequence. Although this concept was introduced over two decades ago, its observation was previously limited to electromagnetic waves. In this study, we demonstrate this quantum interference effect using massive particles that tunnel between three micro-optical traps ("optical tweezers"). We begin by preparing ultracold fermionic atoms in low vibrational eigenstates of one trap, followed by manipulating the distance between the traps to execute the SAP protocol. We observe a smooth and high-efficiency transfer of atoms between the two outer traps, with a very low population remaining in the central trap. These findings open possibilities for advanced control schemes in optical tweezer array platforms.

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

A sensorless efficiency-optimizing vector control scheme for an induction motor drive

A sensorless efficiency-optimizing vector control scheme for an induction motor drive

Fault Isolation and Fault‐Tolerant Control Design for Non‐Gaussian Stochastic Distribution Control Systems With Multiple Sensor Faults

ABSTRACTA fault isolation, estimation and fault‐tolerant control algorithm is proposed for non‐Gaussian stochastic distribution control systems with disturbance and multiple sensor faults. Sensor faults are represented as actuator faults virtually, and an observer is devised to detect the sensor fault occurrence time. Then two subsystems are separated by the expanded system through introducing the coordinate transformation matrices. One subsystem contains only sensor faults and does not contain disturbance and the other contains sensor faults and disturbance, which provides convenience for fault isolation. The faults are estimated respectively by the multiple fault isolation observers with the same number of sensors. A fault‐tolerant control scheme is proposed after getting the fault information to compensated sensor faults and track the desired probability density function. Finally, a MATLAB simulation example is used to verify the feasibility of the algorithm.

LIDAR adaptive parameters selection and target pointing control for close-proximity space operations

This paper deals with advanced Guidance, Navigation and Control (GNC) functions required to enable autonomous and safe operations of a chaser spacecraft in close-proximity to an uncooperative space target, as in Active Debris Removal or On-Orbit servicing scenarios. Specifically, it presents an original approach to autonomously and adaptively select the field of view and resolution of a scanning LIDAR to improve both state estimation accuracy and computational efficiency of a LIDAR-based relative navigation system. In general, the correct operation of such system is also determined by the capability to keep the boresight axis of the relative navigation sensor aligned with the target geometric center. To address this task, an original control technique, based on the sliding-mode formulation and relying on a reduced attitude representation, is proposed. This control scheme is also compared to state-of-the-art PD and PID approaches in terms of pointing accuracy and control effort. The proposed techniques have been numerically validated in a simulation environment integrating the chaser attitude control and the LIDAR-based relative navigation functions in a closed-loop architecture. The simulation environment realistically reproduces the generation of LIDAR-based point clouds, and the spacecraft relative dynamics in close proximity. Results show that the adaptive selection of the LIDAR operational parameters improves the relative navigation performance, while the proposed sliding-mode control guarantees higher pointing accuracy than PD and PID control approaches.

Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

AbstractThis paper proposes an adaptive control scheme with finite‐time convergent property based on integral sliding mode to achieve trajectory tracking control of rigid robotic manipulators. The novel adaptive gain can be adjusted automatically with the system disturbance, as long as the disturbance and its derivative are bounded without requiring more information. Therefore, the control chattering effect can be mitigated obviously. The global robustness of system is guaranteed by Lyapunov stability analysis. Finally, the simulation of a two‐link manipulator is given to show that the desired trajectory tracking performance is obtained with the proposed adaptive sliding mode approach.

Finite-time output-feedback fault tolerant adaptive fuzzy control framework for a class of MIMO saturated nonlinear systems

This paper presents a finite-time fault-tolerant fuzzy adaptive controller for uncertain interconnected nonlinear systems in strict-feedback form. The controller addresses input saturation, state-dependent actuator faults, external disturbances, and unavailable states for measurement. To avoid any unexpected alteration due to failures, the proposed control scheme design addresses all types of actuator faults that are bias, drift, and loss of accuracy as additive faults, as well as a multiplicative fault that results in loss of effectiveness with nonaffine state-dependency. Restrictions on control gains and unmatched interconnections are eliminated, and the explosion complexity caused by recursive back-stepping designs is avoided. Fuzzy Systems approximate the unknown ideal control laws along side the acutator faults and disturbances. The stability analysis guarantees that tracking errors converge to a small compact set around the origin in finite time. Simulation results on a quad-rotor UAV and a mathematical system confirm the effectiveness of the proposed approach.

Resonance Suppression Method Based on Hybrid Damping Linear Active Disturbance Rejection Control for Multi-Parallel Converters

The parallel operation of multiple LCL-type converters will result in a deviation of the resonant frequency and resonance phenomena. The occurrence of harmonic resonance can cause problems such as an increase in harmonic voltage and current. This can lead to the malfunction of relay protection and automatic devices, causing damage to system equipment. In severe cases, it can cause accidents and threaten the safe operation of the power system. A hybrid damping active disturbance rejection control (HD-ADRC) method is proposed in this paper to suppress the harmonic resonance of parallel LCL-type converters. First, a third-order linear disturbance rejection controller (LADRC) including the linear extended-state observer and the error-feedback control rate is designed based on LCL-type converter model analysis. The proposed method considers the resonance couplings caused by both internal and external disturbances as the total disturbance, thus improving the anti-disturbance capabilities as well as the operational stability of converters in parallel. Then, a hybrid damping control is proposed to reconstruct the damping characteristics of converters to suppress the parallel resonance spike and reduce the resonance frequency offset. And the parameter selection of the control system is optimized through a stability analysis of the tracking performance and anti-disturbance performance of the HD-ADRC controller. Finally, all the theoretical considerations are verified by simulation and experimental results based on the Matlab/Simulink 2018B and dSpace platform. The simulation and experimental results show that the PI controller gives a THD of 5.33%, which is reduced to 4.66% by employing the HD-LADRC, indicating an improved decoupling between the converters working in parallel with the proposed control scheme.

An Orbital-attitude Coupled Control Framework for a Full-scale Flexible Electric Solar Wind Sail Spacecraft in Orbital Transformation Missions

In this work, a novel orbital-attitude coupled control framework is developed for the electric solar wind sail (E-sail) spacecraft, aiming to handle the orbital transformation and attitude maneuver simultaneously. In the framework, the desired attitude parameters and the slow-varying voltage component are provided by the orbital control strategy, while the current attitude parameters and the fast-varying voltage component are manipulated by the attitude control strategy to approach their desired values. The orbital-attitude coupled characteristics of the E-sail spacecraft, including the flexibility-induced coupling effect, are fully described by the referenced nodal coordinate formulation. Considering the input saturation conditions, the governing equation for the orbital control strategy is then derived, in which the in-plane and out-of-plane displacement and velocity errors are prescribed as the state variables to be eliminated. An integral sliding mode control (ISMC) scheme is proposed to improve the robustness against the unmeasurable disturbance term. A model predictive control (MPC) scheme is introduced to enhance the convergence efficiency, where a quadratic optimization is performed to plan the desired attitude parameters and voltage components within the prediction horizon. To evaluate the control performance in the orbital transformation and attitude maneuver missions on the displaced non-Keplerian orbit, a series of scenarios with complex initial conditions are simulated under different control schemes, including the ISMC-MPC compound scheme. The results show that the control strategy designed under the rigid-body assumptions may not be feasible for the flexible E-sail spacecraft, while the investigated control strategy realizes the accurate and efficient convergence of the orbital and attitude variables on both the rigid and flexible E-sail spacecraft with the tether deformation stabilized.

Maiden application of mountaineering team-based optimization algorithm optimized 1PD-PI controller for load frequency control in islanded microgrid with renewable energy sources

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

Trajectory Tracking Control of Unmanned Vehicles via Front-Wheel Driving

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

Adaptive Performance Optimal Control for Flexible-Joint Robots with Random Noises: Design and Experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

Dynamic event-triggered finite-horizon robust suboptimal control of multi-player systems with input disturbances

This paper presents an adaptive dynamic event-triggered robust finite-horizon suboptimal control scheme for multi-player nonzero-sum game of nonlinear systems with input disturbances based on dynamic programming (ADP) and integral sliding mode (ISM) control techniques. Firstly, dynamic event-triggered adaptive ISM controllers are designed to handle input disturbances that can force the system state to remain on the sliding manifold and relax the known upper bounds condition of input disturbances. Corresponding event-triggered condition is employed for ISM controller of each player to reduce the update frequency. Then, utilizing the obtained ISM dynamics, we employ ADP-based single critic neural networks to approximate the time-varying solution of the Hamilton–Jacobi equations. Thus, the event-triggered finite-horizon suboptimal controllers can be obtained, and corresponding state-based dynamic event-triggered condition is designed to improve the resource utilization. The stability property of the closed-loop system is demonstrated through the Lyapunov technique. Finally, the effectiveness of the proposed control scheme is confirmed through two simulation examples.

Enhanced frequency control of a hybrid microgrid using RANFIS for partially shaded photovoltaic systems under uncertainties

Nowadays environmental concerns and fossil fuel limitations, as well as economic benefits and technical issues such as reliability lead conventional distribution networks to smart microgrids, where the renewable energy resources are merged with the grids. Nevertheless, the systems face new challenges due to the intermittent nature and uncertainties in the distributed generations, which cause frequency fluctuations in the microgrid. The photovoltaic cells are the main part of the contemporary microgrids. Although the photovoltaic (PV) systems depend on solar irradiance, and temperature and are affected by the partial shading phenomenon they could contribute to improving the microgrid frequency stability with a proper control scheme. In this paper, the frequency control strategy is designed for a hybrid stand-alone microgrid, which is robust against load disturbances, variations in weather conditions, and uncertainties in the microgrid parameters. The proposed intelligent control scheme relies on the Recurrent Adaptive Neuro Fuzzy Inference System (RANFIS). The Whale Optimization Algorithm (WOA) is employed to optimize the RANFIS controller structure and generate the parameters of membership functions. In this multi-objective optimization, the objectives are settling time (ST), overshoot (Osh), and Integral Square Error (ISE). The simulation results verify the high robustness and performance of the proposed RANFIS controller, compared to other controllers, during various operational circumstances, as well as the sporadic behavior of renewable energy resources (RES) such as fluctuations in solar radiation and certain uncertainties in the microgrid parameters.

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T-S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T-S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.