Hierarchical Control Strategy Research Articles

Hierarchical optimization control strategy for intelligent fuel cell hybrid electric vehicles platoon in complex operation conditions

Encountering complex traffic conditions such as the dynamic interference of preceding and rear vehicles and gradients, a control strategy that simultaneously considers inter-vehicle cooperative control and energy economy is one of the key technologies, that improves traffic efficiency and exploits the energy-saving potential of platoon vehicles. In this paper, a hierarchical optimization control strategy is proposed for the intelligent fuel cell hybrid electric vehicles (FCHEV) platoon in a network-connected environment. The hierarchical control framework consists of upper speed control and lower power distribution. In the upper layer, the improved particle swarm optimization (PSO) algorithm is applied to calculate the global optimal speed trajectory, and then the model predictive control (MPC) is adopted for global speed trajectory tracking and self-adaptation, which can ensure the ego vehicle tracks the pre-calculated speed trajectory, and can re-plan the vehicle speed under the condition of safety priority when sudden disturbances occur in the foreground. The lower layer utilizes Q-learning (QL) to achieve power distribution between hybrid power sources, reducing the number of fuel cell starts and stops, slowing battery degradation and improving vehicle economy. Simulation results based on complex road conditions show that the proposed controller has good energy economy and tracking performance. Under the condition of the slope, the total platoon cost of the proposed strategy is reduced by 3.99% compared with the constant speed cruise strategy, and under the interference condition, the total platoon consumption cost of the proposed strategy decreased by 6.79% compared with adaptive cruise control (ACC).

The coordinated fuzzy control of hierarchical power balance of new energy microgrid

In order to improve the coordinated control effect of hierarchical power balance of new energy microgrid, this paper applies fuzzy control method to this system, and proposes a hierarchical control strategy based on event-triggered communication. Each DG is regarded as a proxy, and the continuous actual value of output is replaced by the state prediction value. Moreover, two different event trigger condition functions for frequency and voltage are designed based on Lyapunov method respectively. At the same time, each DG only communicates with its neighbor DG aperiodic at the event trigger time, and finally all DG are restored to the reference value provided by the virtual leader. Finally, this paper constructs a coordinated fuzzy control simulation system for hierarchical power balance of new energy microgrid. Combined with the simulation results, the method proposed in this paper is feasible.

Hierarchical critic learning optimal control for lower limb exoskeleton robots with prescribed constraints

AbstractThis paper presents a hierarchical critic learning optimal control (H‐CLOC) strategy for a lower limb exoskeleton system with prescribed state constraints. The prescribed state constraints of the exoskeleton are mathematically modeled by means of a state error transition function, which transforms the constrained state into an equivalent new unconstrained state. A hierarchical framework including a high‐level motion trajectory planner and a low‐level critic learning (CL) controller is proposed for optimal control of the exoskeleton. The estimation of human motion intentions is achieved using dynamic movement primitives (DMPs), which are capable of accurately replicating the original training trajectories during motion. The CL algorithm is implemented on the transformed unconstrained system while enhancing the learning capability of the proposed algorithm for the exoskeleton. The optimal control with prescribed constraints is analyzed for stability using the Lyapunov theorem. Finally, simulation results validate the effectiveness of the proposed H‐CLOC strategy for lower limb exoskeletons.

Model predictive control of heating in a low energy single-family house

This paper presents a control scheme that uses a Model Predictive Control (MPC) approach to manage the heating system of a low-energy single-family house. The house is equipped with an air-to-water Heat Pump (HP) and individually controlled hydronic underfloor heating circuits for each of its 11 heating zones. The MPC scheme is designed to maintain individual room comfort levels in each zone, while incorporating weather forecasts and following a heating reference to allow for load shifting for periods with low energy prices and high Photovoltaic (PV) production calculated by an upper level in a hierarchical control scheme. The focus of the design has been the model structure that allows for fast solutions to the MPC optimisation problem, while still capturing the high complexity, non-linear dynamics of the building. The controller is tested on a high-fidelity simulation model of the house, achieving disturbance rejection and system stabilisation. The rapid solving time makes repeated experiments and longer simulations feasible.

Hierarchical Multiagent Formation Control Scheme via Actor-Critic Learning.

This article presents a nearly optimal solution to the cooperative formation control problem for large-scale multiagent system (MAS). First, multigroup technique is widely used for the decomposition of the large-scale problem, but there is no consensus between different subgroups. Inspired by the hierarchical structure applied in the MAS, a hierarchical leader-following formation control structure with multigroup technique is constructed, where two layers and three types of agents are designed. Second, adaptive dynamic programming technique is conformed to the optimal formation control problem by the establishment of performance index function. Based on the traditional generalized policy iteration (PI) algorithm, the multistep generalized policy iteration (MsGPI) is developed with the modification of policy evaluation. The novel algorithm not only inherits the advantages of high convergence speed and low computational complexity in the generalized PI algorithm but also further accelerates the convergence speed and reduces run time. Besides, the stability analysis, convergence analysis, and optimality analysis are given for the proposed multistep PI algorithm. Afterward, a neural network-based actor-critic structure is built for approximating the iterative control policies and value functions. Finally, a large-scale formation control problem is provided to demonstrate the performance of our developed hierarchical leader-following formation control structure and MsGPI algorithm.

A hierarchical optimization strategy in the intelligent ecological control of the greenhouse downy mildew

In a low-cost greenhouse, where there are few actuators to regulate the climate, increasing yield and preventing crop diseases are contradictory processes because pathogens and hosts stay in a similar ecological niche. Under given weather conditions, the environmental range for crop growth and disease control must be carefully balanced. The aim of this paper is to develop an accurate optimal control algorithm for disease ecological control and explore its contribution in the application of intelligent downy mildew prevention in a low-cost solar greenhouse. The proposed hierarchical optimization control strategy is compared with four common management methods in order to evaluate its impact on crop growth and disease prevention. The results show that the hierarchical optimization control strategy reaches the best balance between disease prevention, accumulated temperature promotion, and low temperature stress risk reduction without using an additional heating system. Moreover, an event-based PID controller is applied to the system considering the energy conservation aspect, which decreases the vents movements areas by 16%, while the control error is only increased by 1.9%. On the whole, this paper provides a solution for low-cost greenhouses about the joint use of greenhouse modelling, disease early warning and environment control, looking for promoting the green production in greenhouse environments by considerably reducing the disease occurrence and use of pesticides.

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning.

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users' services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users' services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average.

Hierarchical Morphing Control of an Ultra-Lightweight Electro-Actuated Polymer Telescope with Thin-Film Actuators.

This paper explores advanced shape control techniques for ultra-lightweight electro-actuated polymers with composite ferroelectric thin films. It begins with an overview of PVDF-TrFE film actuators used in the development of thin-shell composites, emphasizing the need to overcome constraints related to the electrode size for successful scalability. Strain generation in thin-film actuators is investigated, including conventional electrode-based methods and non-contact electron flux excitation. Numerical studies incorporate experimentally calibrated ferroelectric parameters, modeling non-contact actuation with an equivalent circuit representation. The potential distribution generated by electron flux injection highlights its potential for reducing print-through actuation issues. Additionally, the paper outlines a vision for the future of large thin-shell reflectors by integrating the discussed methods for charging ferroelectric polymer films. A hierarchical control strategy is proposed, combining macro- and micro-scale techniques to rectify shape errors in lightweight reflectors. These strategies offer the potential to enhance precision and performance in future spaceborne observation systems, benefiting space exploration and communication technologies.

Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

Robust Collision-Free Guidance and Control for Underactuated Multirotor Aerial Vehicles

This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using a hierarchical flight control architecture composed of a supervisory outer-loop guidance module and an inner-loop stabilizing control one. The inner loop is designed using a typical hierarchical control scheme that nests the attitude control loop inside the position one. The effectiveness of this scheme relies on proper time-scale separation (TSS) between the closed-loop (faster) rotational and (slower) translational dynamics, which is not straightforward to enforce in practice. However, by combining an integral sliding mode attitude control law, which guarantees instantaneous tracking of the attitude commands, with a smooth and robust position control one, we enforce, by construction, the satisfaction of the TSS, thus avoiding the loss of robustness and use of a dull trial-and-error tweak of gains. On the other hand, the outer-loop guidance is built upon the continuous-control-obstacles method, which is incremented to respect the velocity and actuator constraints and avoid multiple moving obstacles that can accelerate. The overall method is evaluated using a numerical Monte Carlo simulation and is shown to be effective in providing satisfactory tracking performance, collision-free guidance, and the satisfaction of linear velocity and actuator constraints.

Hierarchical Nash equilibrium seeking strategies of quadratic time‐varying games with Euler–Lagrange players

AbstractThis article investigates the distributed Nash equilibrium seeking problem of quadratic time‐varying games with Euler–Lagrange (EL) players, where external disturbances and parametric uncertainties are involved. A gradient‐based hierarchical algorithm consisting of a game layer and a control layer is proposed. Specifically, in the game layer, EL players communicate with neighbors through a graph to reach the consensus on potential aggregate values, which will be employed to calculate the gradient of each player's objective function, and then, a gradient‐based sliding mode controller is developed to track time‐varying gradient in the control layer. Thus, the convergence results are hierarchically obtained through the Lyapunov stability method. In addition, the hierarchical control strategy is extended to address the constrained problems through the utilization of a smooth penalty function. By appropriately choosing control parameters, the Nash equilibrium seeking errors can be arbitrarily small. The relation between the optimal solutions of the original problem and the dual one is further discussed. Finally, the proposed methods are numerically verified.

Quantized-data resource allocation for reinforcement learning cooperative control of networked Euler-Lagrange agents with input saturation

This paper investigates the resource allocation problem of networked Euler-Lagrange agents (NELAs) with quantized-data interactions and input saturation in the framework of reinforcement learning (RL). We propose a hierarchical control strategy that includes a distributed resource allocation estimator (DRAE) and a local RL linear sliding mode controller (RL-LSMC). Specifically, the DRAE based on the gradient descent and state feedback is proposed, which aims to achieve optimal resource allocation by estimated states. The local RL-LSMC is designed through utilizing the feedback of critic neural network and the approximation capacity of actor neural network, which prompts the states of the NELAs to track the optimal estimated states. Several sufficient conditions are established with the help of Lyapunov stability argument. Finally, the effectiveness of the proposed hierarchical control algorithm is verified by the two simulation examples.

Two layer control strategy of an island DC microgrid with hydrogen storage system

In this paper, a two-layer hierarchical control strategy for an isolated DC microgrid with a hybrid energy storage system is considered. The DC microgrid studied is composed of a short-term battery energy storage system, long-term hydrogen-based energy storage system and renewable energy resources. The proposed control strategy, combines concepts of Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) as a primary level and Model Predictive Control (MPC) as a supervisory level. The IDA-PBC layer guarantees a correct power balance and ensures global stability according to Lyapunov theory, considering the non-linear behavior of the physical system. On the other hand, the MPC layer calculates the reference signals for the primary level, ensuring null stationary error in the desired variables. As known, it is important to keep the electrolyzer, the fuel cell and the batteries in a safe operating mode to increase their useful life and guarantee the quality of the hydrogen produced. In this sense, the benefits of MPC are exploited by considering system constraints in the controller design. The main objective of the two-layer control strategy is to ensure the supply of electrical energy to the loads and quality in the electrical variables of the network. Representative simulation results under demanding conditions verify the effectiveness of combining IDA-PBC and MPC for this class of problems.

A Hierarchical Control Strategy Based on Dual-Vector Model Predictive Current Control for Railway Energy Router

The multiport and multidirectional energy flow of railway energy routers (RERs) poses a significant challenge when integrating photovoltaic (PV) systems and energy storage systems (ESSs). To address this issue, this paper proposes an improved hierarchical control strategy for RERs with a reference signal generation layer and an inverter control layer. In the reference signal generation layer, a time-segmentation energy allocation strategy based on a state machine is proposed to manage the multidirectional energy flow in RERs resulting from PV systems and ESSs while minimizing peak power demand. In the inverter control layer, a dual-vector model predictive current control (MPCC) strategy is designed for back-to-back inverters. The dual-vector MPCC strategy eliminates the need for individual PWM blocks, thereby enhancing RER current-tracking accuracy and efficiency. The prominent advantage of the dual-vector MPCC strategy is its ability to achieve high current-tracking accuracy while minimizing active power losses. Simulations and hardware-in-the-loop experiments are conducted to validate the feasibility and effectiveness of the proposed method.

Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles

The connected vehicle technique has offered great opportunities to improve further plug-in hybrid electric vehicles (PHEVs) fuel economy. In this context, a predictive hierarchical eco-driving control scheme is proposed for connected PHEVs under a car-following scenario containing a cloud-layer speed planner and vehicle-layer energy management. In the cloud layer, based on the traffic data separately processed by the dynamic programming (DP) algorithm and k-means clustering method, the reference state of charge (SOC) model, the energy consumption model, the equivalent factor model and the variable-horizon speed predictor can be constructed, respectively. And for ensuring safety, comfort and fuel economy in the car-following process, the energy and SOC models are used separately as the index and state equation in the data-driven speed planning problem. Then, with the driving pattern recognition technique, the power allocation under the planned speed can be conducted by integrating the adaptive equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC), thus guaranteeing fuel economy, adaptability and near-global optimality with high computational efficiency. Compared with other strategies, the effectiveness and advantages of the proposed scheme are validated in the joint simulation platform of MATLAB/Simulink and GT-SUITE.

Coordination of Public Service Facilities and Greening in Residential Communities in Beijing

The coordination of green space and public service facilities is of great importance in improving the spatial quality of existing residential districts. This paper adopts the method of a questionnaire survey to investigate the current cooperation between green space and service facilities in residential communities in urban areas in Beijing, China, explores the possibility of their coordinated development in residential areas, and puts forward countermeasures and suggestions for their collaborative transformation. The results show that the three types of facilities that residents thought should be coordinated most with green space were cultural and sports, educational, and social welfare facilities, while commercial service facilities were the most frequently used. Based on the survey results and considering the difficult degree of collaborative transformation of different service facilities and practical problems in implementation, three suggestions were put forward: (a) Differentiated hierarchical control strategies should be put forward for the collaborative transformation of service facilities and green space for different types of communities. (b) Urban planners should optimize the spatial layout relationship between green space and service facility land and pay attention to their functional combination, considering both spatial and functional coordination. (c) In the actual reconstruction, the residents' willingness to transform different public service facilities, the frequency of use, and the objective economic conditions of the community should be comprehensively considered, and the reconstruction should be carried out in stages according to the order of priority.

Hierarchical control of island microgrid based on consensus algorithm

When the load inside the microgrid changes, droop control maintains a stable power supply cycle of the microgrid by controlling the voltage and frequency at the parallel network of the distributed generation. Since the traditional microgrid droop control usually uses a fixed droop coefficient to adjust the output active power and reactive power, the frequency and voltage of the inverter parallel network will deviate from the system frequency and voltage rating to varying degrees, so a new layer of control link is needed to restore the target value. Moreover, most of the hierarchical control methods adopt centralized control methods, which have limitations such as high dependence on communication. This paper combines a hierarchical control framework and a consistency algorithm to propose a distributed sag control strategy for islanded microgrids based on a multi-agent system. It achieves equal-to-ratio output when distributed generation responds to power changes while maintaining the stability of the microgrid’s main performance indicators. Since the distributed system with a consistency algorithm does not require a centralized control centre, it increases the system’s robustness and reduces the dependence of the system on the communication network. Finally, an islanded AC microgrid model is built by MATLAB/Simulink, and the effectiveness of the proposed islanded microgrid hierarchical control strategy is verified by simulation.

Plant-wide Modeling, Design Consideration, and Practical Hierarchical Control Strategy Considering Key Variable Integral Characteristics for an Industrial Alkaline Water Electrolyzer Plant

Plant-wide Modeling, Design Consideration, and Practical Hierarchical Control Strategy Considering Key Variable Integral Characteristics for an Industrial Alkaline Water Electrolyzer Plant

A Hierarchical Energy Control Strategy for Hybrid Electric Vehicle with Fuel Cell/Battery/Ultracapacitor Combining Fuzzy Controller and Status Regulator

In order to improve the fuel economy of fuel cell hybrid electric vehicles (FCHEV), a hierarchical energy management strategy (HEMS) is proposed to rationally allocate the required power to a hybrid power system with three energy sources: fuel cell, battery, and ultracapacitor. First of all, batteries and ultracapacitors are regarded as energy storage systems (ESS), which convert the distribution problem from three energy sources to two couples of energy sources. Secondly, fuzzy logic controllers are utilized in upper-layer energy management strategies (EMS) to distribute required power to fuel cell systems and the ESS. To extend the service life of the fuel cell and increase the maintenance ability of the state of charge (SOC) of the battery, a status regulation module is introduced to allocate the required power combined with fuzzy controller. Thirdly, an adaptive low-pass filter is applied to a lower-layer EMS based on the energy characteristics of the ultracapacitor, which fully utilizes the ultracapacitor. Finally, the economic and dynamic performance of the vehicle are compared between the HEMS and the power following strategy (PFS) under five typical cycle conditions: UDDS, WVUINTER, NEDC, HWFET and COMBINE. The results of the simulation show that the hydrogen consumption of the HEMS is reduced and the overall vehicle energy efficiency is increased in four operating conditions, which indicates that the proposed strategy has better economic performance. In addition, the dynamic performance of the vehicle is also improved.

Nonlinear longitudinal cooperative control of heterogeneous connected vehicle platoon considering car-following interactions and communication delay

This article designs a novel controller for heterogeneous connected vehicles (CVs) platoon subject to communication delay. Firstly, third-order vehicle dynamics is used to capture the heterogeneity of vehicles. A new nonlinear controller for the CV platoon is proposed in the presence of car-following interactions, the acceleration difference and communication delays. Then, the internal stability of the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability of the linearized CV platoon system is proved by using the infinite-norm method. Additionally, a hierarchical control strategy suitable for co-simulation is designed to overcome the nonlinearity of vehicles and achieve consistency between the desired and actual acceleration. Finally, the superiority and effectiveness of the developed controller are verified by extensive simulation and cosimulation.