Hierarchical Control Method Research Articles

The coordinated control strategy of DC microgrid based on multiple energy storage units

Recently, the DC microgrid (MG) has caught people’s attention because of its simpler control system than the AC microgrid. In this paper, the bus voltage layering control method based on droop control is used for DC microgrid coordination control. According to the working characteristics of the DC bus, the voltage of the DC bus is divided into five sections, corresponding to 5 operating modes respectively. Inside the DC microgrid, each unit converts the operation mode by local control according to voltage amplitude. The photovoltaic power generation unit adopts MPPT control and constant voltage control to adjust the running point tracking, so as to realize changing-over smoothly between voltage regulator mode and MPPT. The battery system adopts a hierarchical operation control method of several batteries on the basis of the state of charge (SOC). To realize the target, which means getting rational active power allocation and SOC equilibrium of each battery unit, as well as different batteries changing-over the charge and discharge modes on the basis of SOC size, so as to adjust the droop curve according to the power size. Finally, the balance between the three objectives of the highest photovoltaic energy utilization rate, energy storage SOC balance, and stable bus voltage is achieved.

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

For integrated energy systems (IES) composed of a set of energy hubs (EHs), a consensus control method is usually adopted to achieve accurate sharing of electrical and thermal composite energies. To solve the communication redundancy problem of the consensus control method, a hierarchical optimization and distributed control scheme based on a dynamic event-triggered mechanism of EHs is proposed to realize stable operation of IES. An economic optimization strategy based on equal increment principle is improved to minimize the operation costs of IES in the second layer. Due to consensus control being integrated into the supply-demand power deviation calculations of EHs, the desired electrical and thermal power trajectories are accurately determined. To improve dynamic response performances in the presence of uncertain disturbances, an event-triggered communication mechanism is designed in the primary layer. The triggering threshold can be adjusted dynamically according to changes of electrical and thermal power outputs, and the redundant communication requirement in the electrical branches is reduced. Considering the coupling characteristics of IES energy networks, a consensus control method is promoted to synchronously track the desired electric and thermal power trajectories of EHs, and the goal of accurate power sharing is achieved. The frequency and pipeline pressure fluctuations are also limited within the allowable range. The economic optimization and coordinated operation of electrical and thermal composite energies in IES are guaranteed by the proposed hierarchical control structure. Additionally, only information from neighboring EHs at the event-triggered time is involved, so the computation simplicity and control performance can be obtained simultaneously. The hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving

For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.

Hierarchical control of differential steering for four-in-wheel-motor electric vehicle.

The purpose of this paper is to study the control of differential steering for four-in-wheel-motor electric vehicles. The so-called differential steering means that the front wheel steering is realized through the differential driving torque between the left and right front wheels. With the consideration of tire friction circle, a hierarchical control method is proposed to realize the differential steering and the constant longitudinal speed simultaneously. Firstly, the dynamic models of the front wheel differential steering vehicle, the front wheel differential steering system and the reference vehicle are established. Secondly, the hierarchical controller is designed. The upper controller is to obtain the resultant forces and resultant torque required by the front wheel differential steering vehicle tracking the reference model through the sliding mode controller. In the middle controller, the minimum tire load ratio is selected as the objective function. Combined with the constraints, the resultant forces and resultant torque are decomposed into the longitudinal and lateral forces of four wheels by the quadratic programming method. The lower controller provides the required longitudinal forces and tire sideslip angles for the front wheel differential steering vehicle model through the tire inverse model and the longitudinal force superposition scheme. Simulation results show that the hierarchical controller can guarantee the vehicle to track the reference model well on both of the high and low adhesion coefficient road with all of the tire load ratios smaller than 1. It can be drawn that the control strategy proposed in this paper is effective.

Hierarchical Central-Local Inverter-Based Voltage Control in Distribution Networks Considering Stochastic PV Power Admissible Range

This paper proposes a hierarchical distribution network voltage control method considering active and reactive power coordination of PV units in both central and local control stages. In contrast to the traditional local control methods, the proposed method defines the admissible range (AR) of PV power and determines it via centralized optimization to realize active PV power curtailments in the local control stage. The affine decision rule (ADR) is adopted to control the reactive power with respect to active power within AR. A distributionally robust chance constraint is designed, based on statistical indices of active PV power, to assess the probability that active PV power generation would fall inside AR. A two-stage optimization problem that can simultaneously provide central and local control strategies is proposed, which is transformed into tractable formulas based on the Gauss Inequality as well as ADR and binary expansion techniques. The proposed method solutions are compared with those of the theoretically optimal method, a robust central-local control method, and a local control method to show the value of hierarchical inverter control with AR in reducing PV power curtailment and ensuring nodal voltages within limits.

Cooperative optimization of speed planning and energy management for hybrid electric vehicles based on Nash equilibrium

The longitudinal automatic driving of hybrid electric vehicles (HEVs) is a complex multi-objective problem, and the coupling relationship between speed planning and energy management is difficult to reveal using the commonly used hierarchical control algorithm. Thus, in this paper, a collaborative control strategy is proposed for cruise speed optimization and energy management of HEVs based on Nash equilibrium. The strategy in this paper can optimize the control of vehicle-level and hybrid-system-level at the same time. First, this paper builds the key component model of HEV under the Simulink environment, which lays the foundation for vehicle control. Then, a multi-objective problem-solving framework based on game theory is proposed aiming at the economy and safety of HEV cruising. Afterward, a weight factor adjustment method is proposed based on fuzzy logic to judge the brake/drive logic. Finally, the utility functions are used for coordinated control based on Nash equilibrium. The simulation results show that the control method proposed in this paper has improved in various aspects as compared with hierarchical model predictive control, including vehicle following, safety, and comfort. In addition, this strategy can significantly reduce energy consumption costs while meeting the cruise speed requirements of vehicles. In the whole driving cycle, the strategy proposed in this paper has a high termination of SOC and a fuel consumption reduction of 15.33%. It can better deal with the relationship between speed planning and torque distribution than the traditional hierarchical control method.

Study on the Hierarchical Predictive Control of Semiconductor Silicon Single Crystal Quality Based on the Soft Sensor Model

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid-liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified.

A novel hierarchical temperature control method of an electric oven and its experimental evaluation

PurposeOvens are semi-industrial multipurpose equipment that are used to provide a desired temperature for specific chemical processes. Temperature regulation in the presence of different type of disturbances and dealing with nonlinear dynamics with large dead time (up to a few minutes) are some undesirable factors that have to be considered in the controller design procedure of the oven systems. Due to these factors, the classical PID controller tuned using Cohen-Coon or Ziegler–Nichol’s tuning methods often fails to meet satisfactory closed-loop performance.Design/methodology/approachIn this paper, to deal with the limitations on the oven system due to the undesirable factors, a hierarchical automaton-guided form controller has been designed. The proposed controller includes several discrete PI controllers, each of which operates locally in the defined operating regions whose separation idea is specific to this paper. Based on the idea proposed in the separation of regions, the controller’s coefficients tuning rules are extracted prior to any determination. Then, a supervisor controller has assumed the task of switching between local controllers. In the next step, by considering a conceptual model for the oven system and using a candidate Lyapunov function, the stability conditions of closed-loop system are discussed and the necessary conditions for the asymptotic stability are derived. The proposed controller is practically implemented with the help of the Arduino Nano platform.FindingsUsing several experiments, the superiority of the proposed hierarchical controller in terms of performance and energy consumption has been demonstrated.Originality/valueThe proposed hierarchical controller has been implemented practically and an acceptable closed-loop performance has been achieved. To illustrate the efficiency of the proposed method, the closed-loop stability of this method is shown using the Lyapunov theory.

Research on Active Collision Avoidance and Hysteresis Reduction of Intelligent Vehicle Based on Multi-Agent Coordinated Control System

This paper provides a multi-agent coordinated control system to improve the real-time performance of intelligent vehicle active collision avoidance. At first, the functions and characteristics of longitudinal and lateral collision avoidance agents are analyzed, which are the main components of the multi-agent. Then, a coordinated solution mechanism of an intelligent vehicle collision avoidance system is established based on hierarchical control and blackboard model methods to provide a reasonable way to avoid collision in complex situations. The multi-agent coordinated control system can handle the conflict between the decisions of different agents according to the rules. Comparing with existing control strategies, the proposed system can realize multi decisions and planning at the same time; thus, it will reduce the operation time lag during active collision avoidance. Additionally, fuzzy sliding mode control theory is introduced to guarantee accurate path tracking in lateral collision avoidance. Finally, co-simulation of Carsim and Simulink are taken, and the results show that the real-time behavior of intelligent vehicle collision avoidance can be improved by 25% through the system proposed.

Voltage control and power sharing in DC Microgrids based on voltage-shifting and droop slope-adjusting strategy

• Designed a dynamic droop controller based on average of instantaneous power, droop control and voltage. • Proposed a new distributed control algorithm for DC microgrids. • Accurate power sharing and fast voltage recovery. To overcome the disadvantages of droop control, this paper proposes a novel method of hierarchical distributed secondary control for DC microgrids. With the load changes, distributed energy resources can achieve power sharing and restore the DC bus voltage. Based on the rated voltage and instantaneous power of the DC grid (DG), a hierarchical distributed control method including shift voltage and droop slope adjustment has been developed. By increasing the shifted voltage and adjusting the droop gain, it can effectively compensate the voltage drop caused by the droop controller. In this way, the DC bus voltage can always be adjusted automatically, regardless the load changes. In addition, this paper introduces the design process of the controller in detail and proves the stability of the system. Finally, simulations and experiments have verified the effectiveness and superior performance of the proposed control method.

Analysis and Design of Adaptive Cruise Control for Smart Electric Vehicle With Domain-Based Poly-Service Loop Delay

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches.

Cooperative Control of Tire Longitudinal and Lateral Forces of Distributed Drive Virtual Rail Train

Hub motors are distributed on different wheels of the virtual rail train (VRT), forming a distributed drive mode, which is conducive to simplifying the transmission system and improving the stability and mobility of VRT. How to coordinate the relationship between the longitudinal and lateral forces of tires is a problem that must be solved. This paper proposes a hierarchical cooperative control method of the longitudinal and lateral forces of tires, in which the control decision-making layer ensures the steering stability and path following performance of VRT, and establishes an objective function based on the tire load rate at the distribution layer to distribute the longitudinal and lateral forces of each tire. The co-simulation results based on MATLAB / Simulink and multi-body dynamics software UM show that the cooperative control method not only ensures the specific track requirements of the train, but also reduces the peak load rate of the tire, optimizes the distribution of the load rate, and greatly improves the stability of the train in the steering process.

Accurate Peer-to-Peer Hierarchical Control Method for Hybrid DC Microgrid Clusters

Hybrid DC microgrid clusters contain various types of converters such as BOOST, BUCK, and bidirectional DC/DC converters, making the control strategy complex and difficult to achieve plug-and-play. The common master–slave hierarchical control strategy makes it difficult to achieve accurate and stable system control. This paper proposes an accurate peer-to-peer hierarchical control method for the hybrid DC microgrid cluster, and the working principle of this hierarchical control method is analyzed in detail. The microgrid cluster consists of three sub-microgrids, where sub-microgrid A consists of three BUCK converters, sub-microgrid B consists of three BOOST converters, and sub-microgrid C consists of two bidirectional DC/DC converters. According to all possible operations of various sub-microgrids in the microgrid cluster, the top-, mid-, and bottom-level controls are designed to solve the coordination control problem among different types of sub-microgrids. In this paper, a hybrid microgrid cluster simulation model is built in the PLECS simulation environment, and an experimental hardware platform is designed. The simulation and experiment results verified the accuracy of the proposed control strategy and its fast plug-and-play regulation ability for the system.

Sensory augmentation for subsea robot teleoperation

Subsea construction operations heavily rely on remotely operated vehicles (ROV). Due to the dynamics of the subsea environment such as uncertain turbulences, affected visibility and interference with subsea ecosystems, control of subsea ROV is challenging for human operators, especially for construction professionals who have never been exposed to such an environment. The traditional visual feedback method can hardly provide direct and intuitive cues about workplace uncertainties, especially the flow conditions, which makes the ROV operation a work with a high mental load and high training barrier. To augment human sensation of the ROV and workplace status, this research proposes a hierarchical intuitive control method based on Virtual Reality (VR) and haptic simulators. A distributed sensor system that allows a flexible add-on sensor package for subsea environmental data collection is applied to the ROVs to collect hydrodynamic data of subsea workplaces. Then, a Digital Twins (DT) module receives and integrates all data to drive a simulation approach for data augmentation. Last, multi-level sensory feedback methods, including far-field augmented 3D visual feedback, near-field haptic suit tactile feedback, and micro-field haptic suit turbulence feedback are generated and sent to human operators through haptic devices. Compared to previous studies, this system reconstructs the realistic subsea environment with detailed hydrodynamic features in VR digital twin. Whole-body level haptic feedback is realized based on hydrodynamic information. This proposed system ensures an immersive awareness of the proximity conditions and predictions of potential damage and status changes during ROV operations. As a result, a less-trained operator can pilot the ROV based on intuition to maximize the performance and avoid potential mistakes.

Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales

To improve the control of active power in wind power clusters, an active power hierarchical predictive control method with multiple temporal and spatial scales is proposed. First, the method from the spatial scale divides the wind power clusters into the cluster control layer, sub-cluster coordination layer and single wind farm power regulation layer. Simultaneously, from the temporal scale, the predicted data are divided layer by layer: the 15 min power prediction is deployed for the first layer; the 5 min power prediction is employed for the second layer; the 1 min power prediction is adopted for the third layer. Secondly, the prediction model was developed, and each hierarchical prediction was optimized using MPC. Thirdly, wind farms are dynamically clustered, and then the output power priority of wind farms is established. In addition, the active power of each wind farm is controlled according to the error between the dispatch value and the real-time power with feedback correction so that each wind farm achieves cooperative control with optimal power output. Finally, combined with the simulation of practical wind power clusters, the results show that the wind abandonment rate was reduced by 2.13%, and the dispatch of the blindness was overcome compared with the fixed proportional strategy. Therefore, this method can improve the efficiency of cooperative power generation.

Robust Hierarchical Pinning Control for Nonlinear Heterogeneous Multiagent System With Uncertainties and Disturbances

This paper investigates the coordination control problem for a special nonlinear heterogeneous multi-agent system consisting of tail-sitter unmanned aerial vehicles and unmanned ground vehicles with uncertainties and disturbances. A robust hierarchical pinning control scheme is proposed for the heterogeneous multi-agent system to restrain the uncertainties and disturbances and achieve coordination scenarios. The heterogeneous multi-agent system can realize coordination tasks by selecting proper pinning nodes and estimating coupling strength. The robustness of the whole system is proven utilizing the Lyapunov stability theorem. The effectiveness of the robust hierarchical pining control method is validated by simulation scenarios.

A Time-Varying Hierarchical Load Frequency Control Method for Multi-area Power System with Wind Power Participation and HVDC Tie-Line based on MAS Structure

A Time-Varying Hierarchical Load Frequency Control Method for Multi-area Power System with Wind Power Participation and HVDC Tie-Line based on MAS Structure

Cooperative Fault-Tolerant Containment Control for Nonlinear Multiagent Systems With Switching Directed Topologies Based on Hierarchical Mechanism

In this article, the cooperative fuzzy hierarchical fault-tolerant containment control (FTCC) method is proposed for nonlinear multiagent systems (MASs) over switching directed topologies. Based on the hierarchical mechanism, the problem of the cooperative FTCC for nonlinear MASs under switching directed topologies is decoupled into the cooperative containment control problem of the virtual reference system (upper layer) under switching directed topologies and the tracking control problem of the actual system (lower layer). Due to the existence of switching topologies, the state of the virtual reference system may jump, which further affects the transient performance of the lower tracking system; moreover, the actuator faults may also cause a sudden change in actual system. In order to mitigate the influence of switching topologies and actuator faults on the lower tracking system, the generalized restricted potential function is introduced to ensure the norm of the lower tracking error within the predefined performance function. Finally, a simulation example is introduced to illustrate the theoretical results obtained.

Nonlinear backstepping hierarchical control of shake table using high‐gain observer

AbstractShake table testing is a common technique used to examine the responses of structures under dynamic loads. Shake table is often regulated using linear controller, such as proportional‐integral‐derivative (PID) controller. However, traditional PID control cannot consider inherent nonlinearities in the structural and control systems. In this paper, a series of novel backstepping control methods, which consider the nonlinearities in the structural and control systems, have been developed. In addition, high‐gain observers, which can provide highly accurate estimation of the shake table displacement, velocity and acceleration, are also developed. The proposed backstepping control methods and high‐gain observers are implemented in a hierarchical framework, where the high‐level backstepping controller generates the command signal for the low‐level controller to execute. A total of four hierarchical backstepping control methods, including the acceleration‐based backstepping hierarchical control (ABHC), the ABHC with high‐gain observer (ABHCO), the displacement‐based backstepping hierarchical control (DBHC) and the DBHC with high‐gain observer (DBHCO), have been implemented. Detailed parameter studies have been conducted to identify the optimized parameters for the proposed hierarchical backstepping control methods. The proposed control method is verified through a series of shake table tests. The experimental results show the ABHC, ABHCO, DBHC and DBHCO can all achieve high‐performance shake table control, especially with superior acceleration tracking over the traditional PID control. Overall, ABHCO achieves the best tracking performance for displacement, velocity and acceleration.

Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment

Distributed energy sources including renewable energy (RE) sources and electric vehicle (EV) discharging offer opportunities to improve grid performance. Although uncoordinated EV charging can perfectly meet users’ driving needs, it brings great challenges in maintaining the quality of low voltage (LV) distribution grids. Most existing EV control strategies do not take into account the heterogeneous nature of EV charging, including the various users’ travel needs and the intermittent nature of RE sources. This article proposes a hierarchical control method that simultaneously coordinates EV charging by considering a dynamic energy tariff, RE sources, EV hardware characteristics, and particularly the EV’s driving needs. The proposed local controller sends the EV’s charging priority and required energy to the central controller based on the EV’s hardware characteristics and users’ driving needs. The central controller then uses this information along with the energy tariff from the retailer and the present grid performance to control EV’s charging or discharging power. The efficacy of this hierarchical control method is evaluated using an Australian LV grid. The obtained results show that this method can reduce the neutral current and voltage imbalance with the maximized usage of renewable energy resources while the EV users’ driving needs are met.