Hierarchical Control Strategy Research Articles

A hierarchical LFFC control scheme for underactuated unmanned surface vessels based on unreliable communication interaction

A hierarchical LFFC control scheme for underactuated unmanned surface vessels based on unreliable communication interaction

Secondary Control Strategy without Communications for Unbalanced Isolated Microgrids

In the context of the energy transition, electrical microgrids have become a key energy solution for isolated zones. For this type of application, low-voltage networks with unbalanced loads are commonly connected. Despite this, many of the hierarchical control strategies presented in the literature have been designed to operate properly only in the presence of balanced loads. For this reason, it is relevant to study how control strategies can be adapted to this scenario, especially those that reduce the dependence on communications to enhance flexibility and reliability. In this sense, this paper presents a secondary layer control strategy that does not require the use of communications to operate in isolated microgrids with unbalanced loads. The strategy guarantees proper performance in terms of power-sharing between the distributed generators of the microgrid. Simulations on Matlab/Simulink are presented to validate the response of proposal.

A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.

Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control. Nevertheless, determining the distribution of control between the brain and muscles is a complex task, particularly when applied to exoskeletons with multiple degrees of freedom (DoFs). Here we present a feasibility, usability and functionality study of a bio-inspired hybrid brain-muscle machine interface to continuously control an upper limb exoskeleton with 7 DoFs. Methods: The system implements a hierarchical control strategy that follows the biologically natural motor command pathway from the brain to the muscles. Additionally, it employs an innovative mirror myoelectric decoder, offering patients a reference model to assist them in relearning healthy muscle activation patterns during training. Furthermore, the multi-DoF exoskeleton enables the practice of coordinated arm and hand movements, which may facilitate the early use of the affected arm in daily life activities. In this pilot trial six chronic and severely paralyzed patients controlled the multi-DoF exoskeleton using their brain and muscle activity. The intervention consisted of 2 weeks of hBMI training of functional tasks with the system followed by physiotherapy. Patients' feedback was collected during and after the trial by means of several feedback questionnaires. Assessment sessions comprised clinical scales and neurophysiological measurements, conducted prior to, immediately following the intervention, and at a 2-week follow-up. Results: Patients' feedback indicates a great adoption of the technology and their confidence in its rehabilitation potential. Half of the patients showed improvements in their arm function and 83% improved their hand function. Furthermore, we found improved patterns of muscle activation as well as increased motor evoked potentials after the intervention. Discussion: This underscores the significant potential of bio-inspired interfaces that engage the entire nervous system, spanning from the brain to the muscles, for the rehabilitation of stroke patients, even those who are severely paralyzed and in the chronic phase.

Hierarchical-power-flow-based energy management for alternative/direct current hybrid microgrids

Modern microgrids are systems comprising both Alternative Current (AC) and Direct Current (DC) subgrids, integrated with Distributed Generations (DGs), storage systems, and Electric Vehicles (EVs) parking facilities. Achieving stable and reliable load flow control amidst varying load, generation, and charging/discharging strategies requires a hierarchical control scheme. This paper proposes an hourly power flow (PF) analysis within an Energy Management System (EMS) for AC/DC Hybrid Microgrids interconnected via an Interlinking Converter (IC) in both grid-connected and islanded modes. The framework operates within a two-level hierarchically controlled platform. Tertiary control at the top level optimizes DGs' reference power for generation and consumption, minimizing power purchase costs and load shedding in grid-connected and islanded modes, respectively. DG converters employ current control mode to share their power references as the primary controller. While no secondary controller is adopted in this scheme, the Battery Energy Storage System (BESS) in islanded mode utilizes P/Q droop control to maintain voltage and frequency in the AC subsystem. Power sharing between AC and DC subgrids through IC is determined by the difference between AC grid frequency and DC link voltage. Integration of controlled converters’ buses into PF equations enables solving the unified system using the traditional Newton-Raphson (NR) method. A segment of a real distribution grid planned for installation in Italy under the HYPERRIDE project serves as a case study. Comparison with MATLAB/Simulink results confirms the effectiveness, precision, and convergence speed of the proposed model and control schemes, demonstrating efficient load distribution and voltage/frequency restoration in islanded mode.

A dynamic data access control scheme for hierarchical structures in big data

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising solution to address the issues of data leakage and sharing in big data. When multiple files are shared, they often have hierarchical access relationships. However, existing hierarchical data CP-ABE schemes can only support simple hierarchical access structures. The limitation arises, necessitating the generation of multiple access structures to meet hierarchical access requirements, resulting in the wastage of computational and storage resources. Therefore, in this paper, we carefully design a dynamic hierarchy access control that allows its level nodes to flexible participate in the decryption of other nodes, enabling the realization of complex hierarchical data relationships. Building upon this, we construct the Attribute-based Dynamic Hierarchical data Access Control (ADHAC) scheme, achieving efficient and dynamic access control for multiple datasets. Subsequently, we apply our scheme in distributed computing, which enables efficient access control for individuals and institutions over multiple data sets and fine-grained computation. Security analysis indicates that our scheme can resist chosen plaintext attack (CPA). Both theoretical and experimental analyses demonstrate that our scheme has impressive advantages in terms of computational efficiency and storage costs compared to existing schemes.

Real time aggregation control of P2H loads in a virtual power plant based on a multi-period Stackelberg game

Power to Heat (P2H) technology is seen as an effective solution for integrating stochastic wind power. Particularly in real-time electricity markets, high-precision wind power forecasts enable the load to better accommodate its fluctuations. Nonetheless, the shorter time scales of real-time trading pose challenges for efficiently aggregating a large, heterogeneous, and dispersed loads and fully harnessing the thermal storage capabilities of loads to follow wind power fluctuations across multiple periods. Against this backdrop, this paper proposes an aggregation control approach for P2H loads with considering multi-period Stackelberg game strategy within the framework of a load-oriented virtual power plant (VPP). For multi-period real-time trading, a Stackelberg game model based on a dynamic price ceiling is established with considering dynamic supply-demand balance between wind power and P2H loads; To enhance load aggregation effectiveness, a dynamically updated temperature-power aggregation model is developed by employing model order reduction, variables continuousization, and linear superposition on second-order equivalent thermodynamic model; To improve load decomposition efficiency, a hierarchical and multi-step control strategy encompassing the continuousization of discrete variables is utilized to simplify the problem. Finally, a simulation example is presented using actual data from a power grid in northern China to validate the effectiveness of the proposed method.

Cascade NMPC-PID control strategy of active heave compensation system for ship-mounted offshore crane

In this paper, the problem associated with active heave motion compensation during heavy-lift operations performed by ship-mounted offshore cranes beneath rough marine environment is investigated. To effectively decouple the correlated motion of the suspended payload from the support vessel via the secondary regulated active heave compensation (SRAHC) system, a novel hierarchical control strategy is proposed by integrating a cascade control structure with an adaptive robust control scheme that incorporates a vertical motion forecast algorithm based on autoregressive integrated moving average (ARIMA) model, a conventional proportional-integral-derivative (PID) framework, and a nonlinear model predictive control (NMPC) method. The introduction of the cascade structure is motivated by its prompt error response against control lag and efficient system order reduction for computational burden alleviation, which forms a crucial foundation for the NMPC-based real-time regulation of PID gains, ensuring optimal evolution of the SRAHC system while improving its practical feasibility. Furthermore, the enhancements in heave compensation and trajectory tracking performance, noise resistance, constraint satisfaction, and engineering application potential of the proposal are demonstrated through a thorough comparative analysis conducted in a practical co-simulation research system.

Aerial transportation control of suspended payloads with multiple agents

In this paper we address the control problem of aerial cable suspended load transportation, using multiple Unmanned Aerial Vehicles (UAVs). First, the dynamical model of the coupled system is obtained using the Newton–Euler formalism, for n UAVs transporting a load, where the cables are supposed to be rigid and mass-less. The control problem is stated as a trajectory tracking directly on the load. To do so, a hierarchical control scheme is proposed based on the attractive ellipsoid method, where a virtual controller is calculated for tracking the position of the load, with this, the desired position for each vehicle along with their desired cable tensions are estimated, and used to compute the virtual controller for the position of each vehicle. This results in an underdetermined system, where an infinite number of drones’ configurations comply with the desired load position, thus additional constrains can be imposed to obtain a unique solution. Furthermore, this information is used to compute the attitude reference for the vehicles, which are feed to a quaternion based attitude control. The stability analysis, using an energy-like function, demonstrated the practical stability of the system, it is that all the error signals are attracted and contained in an invariant set. Hence, the proposed scheme assures that, given well posed initial conditions, the closed-loop system guarantees the trajectory tracking of the desired position on the load with bounded errors. The proposed control strategy is evaluated in numerical simulations for 3 and 4 agents following a smooth desired trajectory on the load, showing good performance. Preliminary experimental results with 2 UAVs are provided to further validate the proposed transportation system.

Numerical investigation on cooling performance and control effect of a novel evaporation-driven cooling unit for Mars extravehicular space suit application

The special environment of Mars brings new challenges to the thermal control of spacecraft on Mars missions. The outstanding advantages of water evaporation cooling technology make it promising for use in the thermal control system of the Mars spacecraft. This paper proposes a novel concept of evaporation cooling hardware that integrates heat collection, heat transfer and heat rejection, namely evaporation-driven cooling unit. The evaporation-driven cooling unit has a simple structure, high evaporation efficiency and the potential to simplify the system topology. Furthermore, an expert hierarchical coordination control strategy applied to the Mars extravehicular space suit cooling loop with an evaporation-driven cooling unit as its heat sink is proposed. The dynamics model of this cooling loop is also constructed using the lumped parameter method. The numerical results have shown that the expert hierarchical coordination control strategy can achieve precise temperature control and reduce the water consumption of the system. The average water consumption rate during extravehicular activity is 0.6975 kg/h when the mean heat load is 572 W (including member metabolic heat and electronic waste heat). Compared to the open-loop case with the same input heat load, a typical extravehicular activity lasting 8 h will save about 11.5 g water if the expert hierarchical coordination control is applied. The proposed evaporation-driven cooling unit will provide more choices for heat rejection of extravehicular space suits. It is expected that the evaporation-driven cooling unit and expert hierarchical coordination control have potential applications in thermal control systems for other space vehicles in near-Earth orbit, on the surface of the Moon and Mars.

Enhancing voltage profile in islanded microgrids through hierarchical control strategies

This paper presents a novel approach aimed at enhancing the voltage profile of an islanded microgrid (MG) through the utilization of Phasor Measurement Units (PMUs) and a hierarchical control system for Distributed Generators (DGs). Recognizing the criticality of maintaining an optimal voltage profile for essential loads within an MG, we assume that PMUs are strategically placed across the MG based on existing placement methodologies to ensure the observability of load buses. The synchrophasor measurements captured by these PMUs are leveraged within an offline-designed tertiary control layer of the hierarchical framework, which in turn sets the voltage reference for the secondary control layer of DGs across the MG. This approach effectively enhances the voltage profile of vital load buses within the MG. The proposed hierarchical strategy encompasses primary, secondary, and tertiary levels of control, responsible for stabilizing voltage and frequency, computing power references, and managing power flow, respectively. PMU measurements play a crucial role in the secondary control level by continuously monitoring deviations. By ensuring the effective placement of PMUs, the proposed framework enables efficient control, leading to notable improvements in the voltage profile and power management. To evaluate the effectiveness of our approach, five test networks were used: 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus test networks. Simulation results demonstrate an impressive over 80 % enhancement in the MG's voltage profile achieved by our proposed method. Additionally, our heuristic strategy efficiently assigns the closest load bus to each DG while aggregating equidistant bus deviations. Collectively, our research underscores the significant impact of PMU measurements and a well-structured hierarchical voltage control system in substantially improving the voltage profile of MGs.

Systematic Design and Implementation Method of Battery-Energy Comprehensive Management Platform in Charging and Swapping Scenarios

Batteries are one of the most crucial energy storage devices today, and battery-energy management technology has an extremely significant impact on the performance and lifespan of batteries. The traditional design approach for battery-energy management platforms often neglects considerations for charging and discharging scenarios. Additionally, functional modules are designed independently, leading to incompatibility issues between hardware and control units, thereby limiting the system’s performance. To address these challenges and enhance system coordination, this paper proposes a systematic design and implementation method for a battery-energy comprehensive management platform applied in charging and swapping scenarios. The method consists of four parts: hardware design, a dynamic load charging-balance control strategy, a composite micro-source hierarchical coordination control strategy, and a system emergency-response and protection strategy. The proposed method has been successfully applied to a design and has been used to build a battery-energy comprehensive management platform. Finally, through experiments, it has been demonstrated that this system can achieve energy scheduling, battery-energy balance, mode switching, and fault protection in a stable and reliable manner.

Analysis and Control of Cascaded Energy Storage System for Energy Efficiency and Power Quality Improvement in Electrified Railways

Energy-efficient and grid-friendly railway power system is critical for the sustainable development of electrified railways. In this paper, a cascaded energy storage system (CESS) is investigated for energy efficiency and power quality improvement of the railway power system. First, the detailed operation principles of the CESS for multiple control objectives, including regenerative braking energy (RBE) utilization, reactive power compensation, and negative sequence current suppression, are analyzed. On this basis, a hierarchical power flow control strategy is developed for achieving the power flow management and control of multiple objectives for CESS. Specifically, a multi-objective power flow management strategy is designed in the system layer to allocate the power references for the CESS under sufficient and insufficient capacity scenarios. In the sufficient capacity scenario, the power references are directly obtained by the operation principles. In the insufficient capacity scenario, the power references are allocated through a three-stage optimization method considering the power shaving rate, grid power factor, and grid imbalance degree. Subsequently, the power references from the system layer are tracked in the converter layer to achieve power flow control. Finally, the effectiveness of the proposed hierarchical power flow control strategy is verified through experimental results. The results show that the proposed CESS can achieve superior performance on RBE utilization and power quality improvement under complex operating conditions in electrified railways.

Deep Reinforcement Learning-Based Hierarchical Energy Control Strategy of a Platoon of Connected Hybrid Electric Vehicles Through Cloud Platform

Due to the features of strong non-linearity and hybrid driving with multi-power sources, a novel deep reinforcement learning(DRL)-based hierarchical energy control architecture is presented for a group of connected hybrid electric vehicles (HEVs) in the platoon through cloud platform. Firstly, a higher-level model predictive control (MPC) law is designed to determine the desired acceleration of connected HEVs in a platoon. Secondly,a reward function with the change of state of charge (SOC) and instantaneous fuel consumption as independent variables is constructed, and the expert knowledge-based deep deterministic policy gradient (DDPG) method with prioritized experience replay (PER) is used for designing the lower-level energy management strategy of connected HEVs platooning. Then, to illustrate the rationality of decision-making by the presented DDPG strategy, the influence of state variables such as vehicle speed, desired acceleration and battery SOC on the agent action values of presented DDPG method is discussed. Finally, the test results show that the proposed control scheme can reasonably allocate the engine and motor powers in real time, and finally achieve the safe and energy-saving driving of connected HEVs in the platoon.

Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

With the increase in the number of stroke patients, there is a growing demand for rehabilitation training. Robot-assisted training is expected to play a crucial role in meeting this demand. To ensure the safety and comfort of patients during rehabilitation training, it is important to have a patient-cooperative compliant control system for rehabilitation robots. In order to enhance the motion compliance of patients during rehabilitation training, a hierarchical adaptive patient-cooperative compliant control strategy that includes patient-passive exercise and patient-cooperative exercise is proposed. A low-level adaptive backstepping position controller is selected to ensure accurate tracking of the desired trajectory. At the high-level, an adaptive admittance controller is employed to plan the desired trajectory based on the interaction force between the patient and the robot. The results of the patient–robot cooperation experiment on a rehabilitation robot show a significant improvement in tracking trajectory, with a decrease of 76.45% in the dimensionless squared jerk (DSJ) and a decrease of 15.38% in the normalized root mean square deviation (NRMSD) when using the adaptive admittance controller. The proposed adaptive patient-cooperative control strategy effectively enhances the compliance of robot movements, thereby ensuring the safety and comfort of patients during rehabilitation training.

Distributed Cooperative Control Strategy for Stable Voltage Restoration and Optimal Power Sharing in Islanded DC Microgrids

Conventionally, voltage regulation and economic dispatch in DC microgrid are performed at different time scales, which cannot keep the distributed generators (DGs) at the optimal operating point in real time. In this paper, a novel distributed cooperative control strategy is proposed to synchronously achieve stable bus voltage restoration and optimal power sharing in islanded DC microgrids. The proposed strategy breaks the constraints of hierarchical control strategies, and incorporates the objectives of voltage regulation and economic dispatch into a distributed cooperative control framework to eliminate average bus voltage deviation and minimize total power generation costs in real time. The stability of the proposed strategy is verified by eigenvalue analysis and steady-state analysis. Furthermore, the concept of “incremental cost (IC) margin” is introduced in cooperative control considering capacity constraints to avoid the converter output power violation and performance degradation. Finally, the MATLAB/Simulink simulation and StarSim HIL experimental results verify the effectiveness and robustness of the proposed strategy in an islanded DC microgrid under various testing scenarios.

Low Frequency Hierarchical Cooperative Impulse Control for Gravitational Wave Detector Formation Keeping

Configuration stability is crucial for the detection accuracy of space-based gravitational wave (GW) detectors. This paper proposes a low-frequency hierarchical cooperative impulse control strategy and a high-fidelity orbit optimization method to achieve a formation of GW detectors with high detection performance. The control strategy employs a hierarchical structure to divide the configuration control into position and shape layers. In the upper layer, the position layer, a state transition tensor method is employed to analyze the sensitivity and feasible range of the formation center position on the configuration stability. In the lower layer, the shape layer, a novel consistency protocol cooperatively uses the states of all individuals in the formation to determine the control expectations. The constraint on formation position is relaxed to reduce the number of impulses, achieving a low-frequency impulse control. The optimization method, characterized by a careful selection of the optimization variables and a design of adaptive model continuation from a low-fidelity model to a high-fidelity model, is proposed to reduce the problem-solving difficulty. The proposed strategy and method are applied to the Laser Interferometer Space Antenna (LISA) mission, and the results demonstrate that the configuration stability is effectively enhanced with several low-frequency impulses.

Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation

A reliable and real-time eco-driving control strategy that incorporates vehicle connectivity is crucial for enhancing the fuel economy and mobility of fuel cell hybrid vehicles (FCHVs). However, the strong coupling of dynamic traffic, vehicle dynamics, and powertrain characteristics makes eco-driving challenging in terms of effectiveness and computational efficiency for co-optimizing velocity planning and energy management. This study proposes a hierarchical eco-driving predictive control framework for connected automated FCHVs, which improves comprehensive performance and is real-time applicable by incorporating an explicit dynamic traffic model (EDTM)-based velocity planning and an equivalent consumption minimization strategy (ECMS). The EDTM predicts the ego vehicle’s state of proceeding through signalized intersections in dynamic traffic scenarios. The model predictive control is employed for multi-objective velocity planning, which balances energy savings, comfort, and traffic efficiency. At the powertrain level, a multi-horizon predictive ECMS (MhP-ECMS) is designed to incorporate both the optimized velocity (short-horizon) and the EDTM-based predictive velocity (mid-horizon). Dynamic traffic Scenario- and Hardware-in-the-Loop (S/HiL) validations show that the proposed strategy can make driving smoother in vehicle-following and through signalized intersections, and markedly improve fuel economy by 5.41% compared to the baseline controller. This study helps provide valuable insight into improving the efficiency and mobility of connected vehicles.

Preface

This Proceedings arises from 2023 6th International Conference on Power Electronics and Control Engineering (ICPECE 2023), which was held via virtual form in Nanning, China from November 10th to 12th, 2023. Meetings in the ICPECE series were and will be held every year, and ICPECE 2023 marks the sixth time that this series of conference had been held successfully.The scientific and social program for the Conference ran over three days with more than 50 delegates from about 10 countries gathering on November 10th and departing on November 12th, including keynote speeches, oral presentations, poster presentations, free discussion, academic investigation, etc.In all there were four keynote speeches by renowned professors during the meeting. Professor Xingjian Wang from Beihang University, China addressed his research to us on Health Status Assessment and Fault-tolerance Control for Aircraft Control System. According to the control command of the pilot or the autopilot, the aircraft control system drives the aircraft rudder through the servo actuator to realize the aircraft attitude and flight trajectory control. In order to realize the highly reliable and healthy service of the aircraft control system, the health status assessment and remaining useful life prediction of the key components of the aircraft control system, the key technology of fault diagnosis for the airborne hydraulic system, and the high safety hierarchical active fault-tolerance control strategy were carried out to effectively improve the healthy service capability of the aircraft control system.Topics that were the focus of invited presentations and contributed papers at ICPECE 2023 included: Motor Drive and Frequency Converter, Drive Motor and Control Technology, Power and Fluid Machinery Equipment, Automatic Control Technology, Computer Integrated Manufacturing System, etc. All submissions were gone through a rigorous review process by performing double blind review to check their quality of content, level of innovation, significance, originality and legibility.The Conference could not have occurred without the generous support of the sponsors and the hard work of all Committee members. We are most grateful for the staff of Journal of Physics: Conference Series for their help and kindness in the preparation of this volume. Finally, we would like to thank all the attendees at ICPECE 2023 for their scientific contributions to the Conference, and for their warm spirit of engagement which characterized the scientific discussions and social occasions.The Committee of ICPECE 2023List of Committee Member is available in this pdf.

An Optimal Hierarchical Control Strategy for 4WS-4WD Vehicles Using Nonlinear Model Predictive Control

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink.

Modeling and Clamping Force Tracking Control of an Integrated Electric Parking Brake System Using Sliding-Mode-Based Observer

This article proposes a hierarchical control strategy to address semi-ABS control as well as the precise clamping force control problems for an integrated electric parking brake (iEPB) system. To this end, a detailed system model, including modeling of the motor, transmission mechanism, friction and braking torque, is constructed for controller and observer design, and a sliding-mode-based observer (SMO) is proposed to estimate the load torque by using the motor rotational speed without installing a force sensor. In addition, a stable and reliable tire–road friction coefficient (TRFC) estimation method is adopted, and the desired slip ratio (DSR) is observed as the target that the rear wheels cycle around. At the upper level of the hierarchical control structure, the desired clamping forces of the rear wheels are generated using a sliding mode control (SMC) technique, and the control objective is to track the DSR to make full use of the road condition. At the lower level, the motor is controlled to track the desired clamping force generated from the upper controller. The hardware-in-the-loop (HIL) experimental results demonstrate the effectiveness and high tracking precision of the proposed strategy under different road conditions, and the estimation parameters based on the proposed observers are timely and accurate to satisfy the control requirements.