Hierarchical Control Strategy Research Articles

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.

Enhancing high-speed steering stability of wheel-legged vehicles by active roll control

Wheel-legged vehicles (WLVs) combine the speed of wheels with the active control of legs to traverse challenging terrain, which presents a new development possibility for enhancing the system’s mobility and stability. Most of the existing studies mainly focus on the stability of low-speed trajectory optimization or obstacle-surmounting by hybrid walking-driving. Without considering the stability of high-speed driving. To enhance the vehicle stability at high-speed steering, with the additional roll moment generated by the active roll motion taken into account, a 15-degree-of-freedom nonlinear yaw-roll coupled vehicle model is developed. Specifically, a fusion dynamic stability factor for skid steering is presented as the rollover threshold to determine the three-dimensional stability region of longitudinal speed, yaw rate and roll angle, based on which the vehicle’s ideal roll angle is obtained. Subsequently, a hierarchical parallel control scheme is proposed to decouple the yaw and roll motions of the wheel-legged vehicle. The fusion dynamic stability factor is regarded as the switching threshold of the upper-level controller, while the lower-level controller adopts the linear quadratic regulator and the sliding mode control to actively control additional roll moment and direct yaw moment, respectively. Furthermore, the studies for the dynamic model and the proposed controller are conducted through vehicle tests. Corresponding test results validate the advantages of the proposed control scheme over conventional schemes without active roll control, in which vehicle stability is effectively improved, thereby preventing vehicle rollover in the case of high-speed steering.

Finite-Time Convergence Control for a Quadrotor Unmanned Aerial Vehicle With a Slung Load

This work investigates the control technique for a quadrotor unmanned aerial vehicle with a slung load. By taking the advantage of the cascade property, a new hierarchical control scheme is designed that divides the control problem into three parts. For the attitude motion control of the quadrotor, the non-singularity terminal sliding mode based control law is proposed. For the translation motion control of the quadrotor, the super-twisting based control law is designed. Additional, for the swing attenuation of the slung load, a nonlinear anti-swing control design is provided. A Lyapunov stability analysis is presented to show the stability of the closed loop system, together with the fast convergence of the quadrotor's translational and attitude tracking, and exponential convergence of the slung load's swing motion. The real time experiments verifies the well performance of the proposed controller.

A Decentralized Power Management and Sliding Mode Control Strategy for Hybrid AC/DC Microgrids including Renewable Energy Resources

This paper presents a new decentralized robust strategy to improve small and large-signal stability and power-sharing of hybrid AC/DC microgrids and improve its performance for nonlinear and unbalanced loads. In addition to the sliding mode controller for DC/DC converters, for the sake of improving power sharing and regulating active and reactive powers injected by distributed energy resources, and moreover, controlling harmonic and negative-sequence current in the presence of nonlinear and unbalanced loads, two separate controllers for positive sequence power control and negative sequence current control are designed based on the sliding mode control and Lyapunov function theory, respectively. The theoretical concept of the proposed robust control strategy, including mathematical modeling of microgrid components, basic theorems, controller design procedure, and robustness and closed loop stability analysis are outlined. Also, this direct power/current/voltage control scheme is governed by a new hybrid AC/DC hierarchical control scheme that exploits a harmonic virtual impedance loop and voltage compensation scheme. To show the effectiveness of the proposed robust control scheme, offline time-domain simulations are done on a hybrid AC/DC wind/photovoltaic/fuel-cell microgrid with nonlinear and unbalanced loads in MATLAB/Simulink environment, and the results are experimentally verified by OPAL-RT real-time digital simulator.

Hierarchical Control Strategy for Effective Virtual Frequency Responses of Multiple WPPs

When renewable energy-based generations are increasing, they are subject to cause frequency stability problems in a power system. To solve this problem, various virtual inertial control methods (VICMs) have been recently developed. However, they all have dealt not with the steady-state frequency stability, but only with dynamic frequency stability issues. In particular, the wind power plants (WPPs) operated by these VICMs can only provide a similar inertial response (IR) to that by conventional synchronous generators without providing the primary frequency response (PFR). This results in lowering the settling frequency (SF) below the maximum steady-state frequency deviation (MSSD) band.This paper proposes the new hierarchical control strategy for the WPPs, which are operated by three virtual frequency response control methods (VFRCMs) to provide both IR and PFR. Firstly, the VFRCM-1 provides virtual IR. Next, the VFRCM-2 increases the output power from the WPPs to prevent the frequency drop, which can occur after the frequency nadir is arrested. Finally, the VFRCM-3 makes the WPPs possible to raise the SF above the MSSD band by providing the virtual PFR. The effectiveness of proposed hierarchical control strategy is verified by several case studies on the IEEE 39-bus test system with the high penetration of renewables.

Hierarchical Control of Connected Vehicle Platoon by Simultaneously Considering the Vehicle Kinematics and Dynamics

This article develops a hierarchical control strategy for connected vehicle (CV) platoon. To this end, the article first characterizes the communication connectivity between CVs using the predecessor-leader following (PLF) topology. Then, a hierarchical control strategy, consisting of first-level and second-level controllers, is proposed by simultaneously considering the vehicle kinematics and dynamics. In particular, the vehicle kinematic model-based controller is proposed in first-level by incorporating the nonlinear coupling and interaction between the CVs and the heterogeneous communication delays; and the vehicle dynamic model-based adaptive integral sliding-mode controller is designed in second-level according to the vehicle desired states obtained by the first-level controller and external disturbance. In addition, the delay-dependent convergence of the first-level controller and finite-time tracking of the second-level controller are rigorously analyzed, respectively. Further, the infinity-norm method is used to investigate the string stability. Eventually, the performance of the developed hierarchical control strategy is verified by extensive simulations, co-simulations and compared with existing methods.

A Deep Reinforcement Learning-Based Decentralized Hierarchical Motion Control Strategy for Multiple Amphibious Spherical Robot Systems With Tilting Thrusters

A Deep Reinforcement Learning-Based Decentralized Hierarchical Motion Control Strategy for Multiple Amphibious Spherical Robot Systems With Tilting Thrusters

Slip ratio control based on adaptive fuzzy sliding mode for vehicle with an electromechanical brake system

The development of vehicle intelligence has driven the evolution of brake-by-wire systems, with electromechanical braking (EMB) emerging as a crucial development in intelligent vehicle braking. To enhance the braking safety of EMB-equipped vehicles, this paper proposes a slip ratio control method based on adaptive fuzzy sliding mode control (AFSMC) to more effectively achieve wheel slip ratio tracking during braking. The approach involves establishing the mathematical model of the planetary gear-type EMB system and the vehicle’s longitudinal dynamics model. Additionally, a hierarchical collaborative control strategy is introduced, where the bottom layer employs the EMB clamping force control algorithm based on cascaded proportional-integral (PI), and the top layer integrates AFSMC to regulate the slip ratio. The simulation results, validated using the joint simulation platform of Simulink and Carsim under various conditions, illustrate that the AFSMC, compared to the conventional sliding mode controller (SMC), attains more precise control of wheel slip ratio while mitigating the chattering phenomenon. These findings suggest the potential of AFSMC for practical engineering applications.

A Hierarchical Control Scheme for Trajectory Tracking USVs in Environments with Disturbances and Dynamic Obstacles

A Hierarchical Control Scheme for Trajectory Tracking USVs in Environments with Disturbances and Dynamic Obstacles

Distributed Event-Triggered Control for Power Sharing in Grid-Connected AC Microgrid

The traditional communication method will bring a heavy burden to the grid system in the distributed collaborative control of microgrids. In grid-connected ac microgrid, this paper adopts an event-triggered control method to resolve the power sharing problem and reduce the communication cost. To begin, the problem is stated as the control problem of a leader-follower multiagent system and uses a distributed hierarchical control strategy to solve it. The primary controller uses the internal model principle to control the voltage output. To optimize the secondary controller, this paper not only adopts an event-triggered control strategy for the microgrid based on the Lyapunov principle to control the output of the compensator state, but also carries out relevant theoretical verification and analysis to ensure that each agent can exclude Zeno behavior. Finally, simulation instances are used to verify the usefulness of the studied control algorithm.

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car with Engine Start-Stop System and Dynamic Coordination

Hierarchical Control Strategy for the Hybrid Electric Propulsion System of a Flying Car with Engine Start-Stop System and Dynamic Coordination

Hierarchical Control Strategy of Constant Power Load-based DC Micrograds using a New Distributed Averaging Proportional Integral Secondary Controller

Hierarchical Control Strategy of Constant Power Load-based DC Micrograds using a New Distributed Averaging Proportional Integral Secondary Controller

Mini-review: Biological Movements as Objects of Natural Science

BACKGROUND: Two approaches to biological movement have dominated the field, the computational approach (internal models) and the approach based on laws of nature pioneered by several researchers including Michael Turvey. AIM AND METHOD: We review a body of literature exploring and expanding the approach based on laws of nature. We show, in particular, how this approach fits the philosophical traditions of Merleau-Ponty, the theory of control with spatial referent coordinates, the principle of abundance, and the concept of performance-stabilizing synergies using the uncontrolled manifold hypothesis. RESULTS: Currently, this scheme of motor control has been applied successfully in studies of a variety of effectors, from single motor units to the whole body, a variety of tasks, and various populations. It led to the discovery of new phenomena and new interpretations of known phenomena. It has been productive in studies of neurological patients offering new understanding of some of the common pathologies (e.g., spasticity) and new tools for early diagnosis of various subcortical disorders and monitoring treatment effects. CONCLUSION: Currently, this field faces a number of challenges including the following: Mapping steps in the hierarchical control scheme on neurophysiological structures and circuits, understanding the relative role of intra-muscle (spinal) and multi-effector (supraspinal) synergies, expanding studies of movements in intact animals and animal preparation, and moving beyond the current scheme to fields traditionally associated with psychology. Just like the current scheme represents moving “outside the box” of classical mechanics, a step outside the current thinking is needed.

Trajectory Tracking Control of a Skid-Steer Mobile Robot Based on Nonlinear Model Predictive Control with a Hydraulic Motor Velocity Mapping

In this study, we address the trajectory tracking control problem of a hydraulic-driven skid-steer mobile robot. A hierarchical control strategy is proposed to simultaneously consider the robot’s position control and the velocity control of the hydraulic motors. At the upper level, a nonlinear model predictive control (NMPC) method is employed to control the position and heading of the mobile robot. The NMPC controller takes into account the robot’s physical constraints and generates the desired robot motion velocity. Then, to control the hydraulic drive system, a current–velocity mapping-based control method is introduced. By establishing the mapping relationship between the control current applied to the hydraulic motor and its corresponding output velocity, the dynamics of the hydraulic motors are characterized. Consequently, the lower-level controller can directly obtain the control signal for the hydraulic actuator through lookup mappings. Additionally, PID controllers are adopted to compensate for velocity tracking errors. The proposed hierarchical control strategy decouples the robot’s position control and the hydraulic system control, simplifying the overall controller design, leading to improved control performance. To validate the effectiveness of the proposed control strategy, several experiments were conducted on a hydraulic-driven skid-steer mobile robot, and the results demonstrate the effectiveness of the proposed approach.

PSO and NNPC-based integrative control allocation for dynamic positioning ships with thruster constraints

In order to overcome the issues of thrust saturation, uneven energy consumption and suboptimal control associated with the traditional hierarchical control strategy employed in ship dynamic positioning (DP) systems, an integrative control allocation strategy is proposed by combining the low-level thrust allocation into the high-level motion controller. The proposed scheme is implemented via the neural network-based nonlinear predictive control (NNPC) and solved via an improved particle swarm optimization (PSO) algorithm. A data-driven recurrent neural network (RNN) ship motion and thrust allocation model is off-line identified based only on the current and historical measured output and thrust input data. Utilizing the established RNN model to predict the future multi-step outputs, and combining the motion control, the thruster energy consumption and the thruster wear and tear as an integrative control objective, the integrated NNPC online optimization problem with thruster constraints is established and solved by an improved evolution algorithm of PSO. Simulations are respectively carried out for setpoint regulation and trajectory tracking. Simulation results well demonstrate the feasibility and superiority of the proposed control strategy.

A hierarchical control strategy for reliable lane changes considering optimal path and lane‐changing time point

AbstractImplementing reliable lane changes is crucial for reducing collisions and enhancing traffic safety. However, existing research lacks comprehensive investigation into the optimal path for maintaining driving quality, and little attention has been given to determining the appropriate lane changing time point. This paper addresses these gaps by presenting a novel hierarchical strategy. First, a synthesized safety distance for lane changing, which considers variable execution duration, is designed to reduce collision risk. Next, a hierarchy of optimization control strategies is proposed to obtain the optimal path. An upper neural network‐fuzzy control algorithm is established to identify an appropriate lane‐changing time point. Additionally, a lower neural network‐improved firefly algorithm is formulated to optimize the preliminary safety path based on multiple driving criteria. Furthermore, the dynamics characteristics of the vehicle are incorporated into the model predictive control algorithm to ensure the vehicle follows the optimal path. Finally, the feasibility of the proposed hierarchical control strategy is validated through typical lane‐changing scenarios conducted on the Carsim–Simulink platform.

A hierarchical reinforcement learning GPC for flexible operation of ultra-supercritical unit considering economy

With the growing proportion of renewable energy sources in power grid, the demands for deep peak shaving and rapid load regulation of ultra-supercritical (USC) unit to provide more flexible and stable power services is further enhanced. Simultaneously, the load tracking speed is no longer the only significant task in the flexible operation of USC unit, the operational economy is more and more important. To this end, a hierarchical reinforcement learning GPC control scheme with a better economy is proposed and apply to USC unit. Firstly, the model mismatch problem in control process is taken into account, and an integral mode is added into the GPC to eliminate the steady state error during the load regulation. Secondly, the hierarchical reinforcement learning GPC scheme integrated the GPC and twin delayed deep deterministic policy gradient algorithms is presented. The complex flexibility demand problem is decomposed into rapid load regulation and economic operation and then respectively solved in the GPC-based upper layer and reinforcement learning agent-based lower layer. Thirdly, under the flexibility demand, a multi-criteria optimization function including the load tracking cost, main steam throttling loss and coal consumption rate of unit is constructed. Then, via the accurate trajectory tracking by GPC and on-line control sequence optimization by reinforcement learning agent, the operational flexibility and economy of USC unit are simultaneously improved within safe boundaries. Finally, to verify the availability of the proposed hierarchical reinforcement learning GPC strategy, large-scale load variations and disturbance rejection ability tests cover the 30%–100 % rated load of a 1000 MW USC unit are carried out, and the load regulation performance is compared to other strategies. Results confirm that the designed hierarchical reinforcement learning GPC control scheme is profit for the flexible and economic operation capability promotion of USC unit.

Spatial mismatch and the attribution analysis of carbon storage demand and supply in the Yangtze River Economic Belt, China

Regional carbon storage disparity between demand side and supply side is a noteworthy issue for achieving carbon neutrality. For better effective carbon budget management and ecological protection, it is imperative to clarify the mismatch patterns of carbon storage demand and supply as well as the underlying key drivers. From the perspective of ecosystem services, this study conducted in-depth analysis on the spatial mismatch and drivers of carbon storage demand and supply in the Yangtze River Economic Belt. The hot spot analysis results visually showed a distinct spatial mismatch characteristic between carbon storage service demand and supply both in 2000 and 2017. This characteristic was further quantified by a spatial mismatch index, suggesting an increasing spatial heterogeneity in the mismatch patterns. Additionally, the factor detection result from Geodetector model indicated that land use and socio-economic factors were the dominant drivers affecting the spatial mismatch patterns of carbon storage demand and supply. To determine the specific contribution of the drivers, K-means++ algorithm was used for zoning and a spatial decomposition analysis was further applied. The results suggested the county-level differences of spatial mismatch were mostly due to the between-group differences, which were primarily promoted by urban expansion and partly restrained by population agglomeration. As for the attribution analysis for the within-group differences, the drivers basically performed differently in each group. Based on these results, this study proposed to break down administrative divisions and implement hierarchical carbon budget control strategies.