Hierarchical Control Strategy Research Articles

A novel control scheme for load current components sharing improvement, and unbalanced and harmonic voltage compensation, in islanded provisional-microgrids

Microgrids (MGs) are the building blocks of future smart distribution grids, bringing several benefits to both utilities and consumers. In recent years a new class of MGs has been introduced, called provisional-microgrid (P-MG), consisting of master-MG and slave-MG. Slave-MG cannot operate isolated from master-MG. However, power quality is of the major challenges in MG investigations. In this study, a novel hierarchical and distributed control scheme is proposed for the improvement of unbalanced and harmonic load current components sharing among the distributed generation (DG) units as well as voltage quality (VQ) enhancement of sensitive load bus (SLB) in provisional-microgrid. Compared to the previous works, the proposed control strategy takes the instantaneous free capacity of DGs and multi-voltage-quality level (MVQL) indices of different buses into account for both load current sharing and voltage quality improvement functions. Furthermore, it considers the nature of variable power generation-renewable energy resources (VPG-RERs) and does not need any information about the loads or lines impedances. The efficiency of the proposed control scheme is evaluated by simulation of a typical islanded P-MG under several cases. Simulations are carried out in MATLAB/Simulink environment, and their results demonstrate the appropriate speed and accuracy of the presented method.

On a hierarchical adaptive and robust inverse dynamic control strategy with experiment for robot manipulators under uncertainties

This paper proposes a hierarchical robot manipulation control scheme for the manipulator pushing task. Considering the contact surface and the object mass parameters are generally unknown to the pushing task, a hierarchical control strategy is designed that enables the robot manipulator to perform the manipulation pushing task without any prior knowledge of the objects mass and friction. Specifically, we first present an adaptive controller to achieve the desired interaction force exerted on the end of the manipulator even without knowing the object mass and friction contact surface. We then introduce a unified robust adaptive controller that takes into account the desired interaction manipulation force in our robot manipulator dynamics. A robust inverse dynamic controller combining a barrier Lyapunov function is included to prevent constraint violation, which can effectively keep the object motion along the desired trajectory while regulating the interaction force between the robot manipulator and the object. Furthermore, an adaptive unit control gain is designed to make the manipulator adapt to follow the perturbation variations without being overestimated. An experiment has been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

Physics-informed hierarchical data-driven predictive control for building HVAC systems to achieve energy and health nexus

Buildings consume 74% of US electricity and 40% of primary energy use. However, 15% of the energy was wasted due to bad controls. Many research studies have demonstrated that model predictive control strategies could provide significant energy savings, but the lack of a scalable building dynamic model impeded the large-scale implementation of predictive control strategies. Moreover, many predictive control strategies lacked the consideration of indoor air quality. Therefore, in this research study, we proposed a novel physics-informed input convex neural network (PINN) to predict indoor environmental dynamics with 6 h ahead. The model sanity check results showed that the proposed PINN had physically consistent behavior to different control inputs. Then, the PINN is used to design a hierarchical data-driven predictive control (HDDPC) strategy to minimize both the space cooling load and airside coil load. Three different case studies were simulated to evaluate the proposed control strategy comprehensively: a) maintaining a similar indoor temperature profile as the measurement to evaluate energy reduction potential by the HDDPC; b) evaluating the impact of the proposed weather forecast models on the HDDPC performance by comparing the case with perfectly accurate weather information and the case with forecasted weather information. Results indicated that the hierarchical data-driven predictive control strategy with weather forecasting had a similar performance with measured weather inputs. On average, the HDDPC strategy could reduce more than 35% of total cooling energy and 70% of total airside coil energy with the guarantee of indoor thermal comfort and air quality.

Hierarchical control strategy of thermostatically controlled load considering multiple factors

In this article, a distributed hierarchical control strategy of thermostatically controlled loads (TCLs) is proposed, which can effectively utilize TCLs to provide power dispatching service in a microgrid composed of buildings. Firstly, the distributed consensus protocol of TCLs is studied, and the state space of a TCL is established. The optimal control performance is achieved by selecting coupling strength and control gain. Then, the dispatching power of each building is reasonably distributed through pinning control, and consensus coefficient and proportional coefficient are determined according to the heterogeneity of building parameters. Finally, the case studies were conducted to validate the proposed novel distributed hierarchical control scheme, which can provide effective power dispatching services and realize the fair distribution of the dispatching requirements among all TCLs. The proposed strategy also takes into account the multiple factors of each TCL such as heterogeneous parameters, communication burden and user's actual conditions, which improves the feasibility and expansibility of the dispatching process.

Distributed MPC of vehicle platoons with guaranteed consensus and string stability

Control of vehicle platoon can effectively reduce the traffic accidents caused by fatigue driving and misoperation, reduce air resistance by eliminating the inter-vehicle gap which will effectively reduce fuel consumption and exhaust emissions. A hierarchical control scheme for vehicle platoons is proposed in this paper. Considering safety, consistency, and passengers’ comfort, a synchronous distributed model predictive controller is designed as an upper-level controller, in which a constraint guaranteeing string stability is introduced into the involved local optimization problem so as to guarantee that the inter-vehicle gap error gradually attenuates as it propagates downstream. A terminal equality constraint is added to guarantee asymptotic consensus of vehicle platoons. By constructing the vehicle inverse longitudinal dynamics model, a lower-level control scheme with feedforward and feedback controllers is designed to adjust the throttle angle and brake pressure of vehicles. A PID is used as the feedback controller to eliminate the influence of unmodeled dynamics and uncertainties. Finally, the performance of longitudinal tracking with the proposed control scheme is validated by joint simulations with PreScan, CarSim, and Simulink.

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

Tracking performance of the coaxial counter-paddle flight system using fractional active disturbance rejection controller

Stable aerial flight capability is crucial for amphibious vehicles to perform cross-domain motion, yet it is often influenced by unpredictable cross-domain environments and model uncertainties. A fractional-order active disturbance rejection controller based on finite-time convergent extended state observer is proposed for the anti-interference control problem in the attitude trajectory tracking of bullet-shaped trans-domain amphibious vehicle in complex environments. Firstly, a complete dynamic model of the airborne flight system of the bullet-shaped trans-domain amphibious vehicle is established. A hierarchical control scheme based on the proposed controller is designed assuming that all states of the system are available, in order to improve the control reliability and robustness of the flight system in multi-source interference environments. Then, based on a novel hybrid particle swarm search technique called the Particle Swarm Optimization with Lévy Flight and Wavelet Mutation algorithm, which combines Lévy flight and wavelet mutation, a new parameter identification and tuning algorithm is proposed for the proposed controller to obtain optimal control parameters for attitude and position. To provide a comprehensive comparison, the stability of the optimization algorithm and the robustness performance of the controller were evaluated. Finally, all controllers were tested under three different disturbance scenarios. The results show that the proposed controller can achieve better stability in flight attitude and trajectory tracking tasks compared to Proportion Integration Differentiation controller, Fractional-order Proportion Integration Differentiation controller, and Active Disturbance Rejection controller in all scenarios.

Virtual angle‐based adaptive control for trajectory tracking and balancing of ball‐balancing robots without velocity measurements

SummaryThis article proposes a virtual angle‐based adaptive control method for trajectory tracking and balancing of ball‐balancing robots without velocity measurements. The trajectory tracking and balancing control of ball‐balancing robots is challenging due to underactuation and uncertain nonlinearities. The hierarchical control strategy, which designs the control system using a linear combination of trajectory tracking and balancing errors, can be a solution. However, it has a local minimum problem where the convergence of tracking and balancing errors is not guaranteed even though the linear combination error is zero. Therefore, a virtual angle‐based control method is presented, which can solve the underactuation problem without the local minimum issue. In addition, a neural network‐based observer is developed to estimate the velocity information of ball‐balancing robots with uncertainties, and the input saturation problem is considered. It is proven that all tracking and balancing errors are bounded and can be made arbitrarily small in the Lyapunov sense. Finally, simulation results are provided to verify the effectiveness of the proposed control system.

Hierarchical Control Strategy with Battery Dynamic Consideration for a Dual Fuel Cell/Battery Tramway

This paper proposes a hierarchical energy control strategy for a hybrid dual fuel cell/battery tramway, combining online and offline optimization methods while considering the battery’s dynamic behavior. In the upper layer, an online band-pass filter-based extremum-seeking control (BFESC) is employed to estimate the reference power between the dual fuel cell system and battery. In addition, the battery’s dynamic behavior is considered a penalty function of the BFESC to maintain its parameters within the desired boundaries. In the middle layer, the power requirement for each fuel cell system is calculated by using an offline method called the map search method. Finally, the fuel cell and battery provide the required power to the DC bus through DC/DC converters, which are controlled by PID controllers in the lower layer. To verify the effectiveness of the proposed control strategy, a simulation model is built in Matlab/Simulink. The results demonstrate that the dual fuel cell/battery system under the control of the proposed energy management strategy (EMS) can operate efficiently while improving the battery’s durability. The efficiency of the fuel cell system when using the proposed EMS was lower than 4% compared with the non-constraint EMS. However, the capacity loss of the battery could improve up to 25.9% in high-current and high-SOC cases.

Hierarchical energy management control based on different communication topologies for hybrid electric vehicle platoon

To address the problem of longitudinal control and energy management of nonlinear hybrid electric vehicle platoon with different unidirectional communication topologies, a novel hierarchical energy management control strategy based on different communication topologies for platoon is proposed. Firstly, the upper platoon controller is based on the established nonlinear platoon longitudinal dynamics model. The vehicle-to-vehicle communication is applied to obtain the preceding vehicle information under different unidirectional communication topologies. The distributed model predictive control is used to realize the platoon longitudinal control and obtain the desired demand torque. Secondly, the lower energy management controller is based on desired demand torque and combines with current battery state of charge, the equivalent factor-based Deep Q-Learning is adopted to reasonably allocate the power component torque. Then, the platoon control effects of different unidirectional communication topologies are compared and verified under the given driving cycle. The preceding and leading following topology is chosen that satisfies real-time and optimality. The topology is also used to verify the effectiveness and working condition adaptability for this strategy. Finally, the simulation results under Chongqing actual working condition show that the strategy can well meet the requirements of platoon following, safety and comfort. The average fuel economy is improved by 2.61% and 7.58% compared with traditional deep Q-learning and Q-Learning, respectively, which can achieve global optimal similar to dynamic programming.

Improved design of transient power sharing strategy for AES multi-gensets in response to high power load mutation

The Medium Voltage Direct Current (MVDC) Integrated Power System (IPS) represents the developing trend of marine ship power. It facilitates the provision of high quality energy for weaponry, however, high power load switching will have a severe impact on power system as well as on gas turbine gensets, which is a critical problem needs to be solved urgently. In this paper, based on the traditional IPS hierarchical control strategy, an improved design method of transient power sharing is proposed for multi-gensets operation. Firstly, by analyzing the transient fluctuation process of voltage and speed, the law of system energy coupling transfer is illustrated and a strategy of equalizing torque impact is proposed. Then, a set of speed compensation factors is introduced to achieve the equalization of transient speed regulation (TSR). Finally, the effectiveness of the proposed improved control strategy is verified by MATLAB/Simulink simulation. Compared to before the improvement, the gensets operate away from the limiting boundary of bearing torque impact and speed deviation, and the bus voltage stability is also enhanced.

Distributed Hierarchical Shared Control for Flexible Multirobot Maneuver Through Dense Undetectable Obstacles.

When teleoperating a multirobot system (MRS) in outdoor environments, human operators can often detect obstacles that are not detected by robots and spot emergencies faster than robots do. However, the lack of efficient methods for operators to manipulate an MRS has limited the number of robots in a human-robot team. To handle this problem, a distributed hierarchical shared control scheme is proposed, aiming to provide a safe and flexible control interface for a few human operators to interact with a large MRS. The proposed hierarchical control scheme employs a two-layered structure. In the upper layer, intention field networks are designed to generate virtual human control signals. Two functionalities for human teleoperation, called: 1) group management and 2) motion intervention, are realized using intention fields, allowing the operators to split the robot formation into different groups and steer individual robots away from immediate danger. In parallel, a blending-based shared control algorithm is designed in the lower layer to resolve the conflict between human intervention inputs and autonomous formation control signals. The input-to-output stability (IOS) of the proposed distributed hierarchical shared control scheme is proved by exploiting the properties of weighting functions. Results from a usability testing experiment and a physical experiment are also presented to validate the effectiveness and practicability of the proposed method.

Coordinated Longitudinal and Lateral Stability Improvement for Electric Vehicles Based on a Real-Time NMPC Strategy

Under extreme conditions, such as low friction surfaces, violent steering and urgent acceleration/deceleration, vehicle states change rapidly and are influenced by nonlinear and coupled vehicle dynamics. To improve vehicle stability under extreme conditions, a hierarchical control strategy is proposed for DDEVs. In the upper layer controller, a combined-slip tire model is adopted to improve the model accuracy under extreme conditions. A nonlinear model predictive control based controller is then designed to generate the desired tire slip ratios with the main objectives of tracking the desired yaw rate and suppressing the lateral velocity and tire slip ratios. In the lower layer controller, the disturbance on the driver’s torque requirement, which is disregarded by existing studies, is taken into account. Next, a linear predictive controller is designed to track the desired tire slip ratios by adjusting the motor torques. To improve the computational efficiency of the nonlinear predictive controller, a PMP-based, fast solving algorithm is proposed. The effectiveness of the proposed solving algorithm is checked by comparing the control performance with IPOPT. The proposed control strategy is evaluated by a series of HIL experiments. The HIL results show better performance in overall stability improvement and minimize the disturbance on the driver’s torque requirement.

Hierarchical Predictive Control of an Unmanned Aerial Vehicle Integrated Power, Propulsion, and Thermal Management System

Increasing electrification of air vehicles presents challenges to the energy management of their coupled electric power, propulsion, and thermal systems. Notably, the complexity and multi-timescale nature of this class of systems make it difficult to design control algorithms that can simultaneously optimize dynamics across each energy domain. The unique contribution of this work is the design and experimental validation of a hierarchical model predictive controller that coordinates the electro-mechano-thermal dynamics of an unmanned aerial vehicle (UAV) integrated power, propulsion, and thermal management system. To support this contribution, a novel UAV energy system testbed is introduced. A graph-based framework is used to model the multi-domain dynamics of the UAV. To estimate the states of the experimental platform, a decentralized observer is described. When compared to a baseline approach, the hierarchical control strategy results in a higher performing and more reliable closed-loop system while decreasing fuel utilization by approximately 16%.

Inverse-Dynamics- and disturbance-Observer-Based tube model predictive tracking control of uncertain robotic manipulator

A novel robust hierarchical multi-loop composite control scheme is proposed for the trajectory tracking control of robotic manipulators subject to constraints and disturbances. The inner loop based on inverse dynamics control is used to reduce the nonlinear tracking error system to a set of decoupled linear subsystems to alleviate the computational effort during the sequel optimization. The feasible regions of the equivalent state and control input of each subsystem can be computed efficiently by choosing an appropriate inertia matrix estimate. The external loop, relying on a set of separate disturbance-observer-based tube model predictive composite controllers, is used to robustly stabilize the decoupled subsystems. In particular, the disturbance observers are designed to compensate for the disturbances actively, while the tube model predictive controllers are used to reject the residual disturbances. The robust tightened constraints are obtained by calculating the outer-bounding-tube-type residual disturbance invariant sets of the closed-loop subsystems. Furthermore, the recursive feasibility and input-to-state stability of the closed-loop system are investigated. The effectiveness of the proposed control scheme is verified by the simulation experiment on a PUMA 560 robotic manipulator.

Hierarchical motion control strategies for handling interactions of automated vehicles

The paper proposes motion control strategies for automated road vehicles to handle interactions among vehicles. The control strategies are built in a hierarchical structure, which contains a high-level learning-based control, a low-level robust control and an optimization-based supervisor. The purposes of the control strategies are to guarantee collision-free motion of the vehicles, and moreover to improve economy and traveling time performance levels. In the paper two configurations of the hierarchical structure are proposed, which are based on the centralized and independent design for automated vehicle motion. The paper presents the comparison of the two configurations through simulation examples. Moreover, the operation of the independent configuration on a small-scaled indoor test vehicle platform is also presented.

Stability control of distributed drive electric vehicle based on adaptive fuzzy sliding mode

To improve the driving stability of a distributed drive electric vehicle (DDEV), we propose a hierarchical direct yaw moment control strategy. In the upper-level controller, an adaptive fuzzy sliding mode controller (AFSMC) is designed with joint control objectives of sideslip angle and yaw rate. In the conventional controller, the weight coefficients of the joint control objectives are fixed values, which reduces the adaptability of the system. To improve this problem and achieve adaptive adjustment of the weight coefficients of the control objectives with the vehicle motion state, an adaptive fuzzy control-based target weight coefficient controller is designed. In the lower controller, a quadratic programing method is used to assign torque with the optimization objective of minimizing tire attachment utilization. A joint simulation with Carsim and Simulink software was performed to compare and analyze the AFSMC vehicle and the traditional SMC vehicle under the steering wheel turning angle sinusoidal input and double shift line conditions. The results show that AFSMC effectively reduces the magnitude of vehicle sideslip angle, yaw rate, and lateral acceleration, enhances the driving stability of the vehicle, and enables adaptive adjustment of the weight coefficients of the joint control objectives, improving the adaptability of the controller. Finally, the effectiveness of the control strategy is verified by hardware-in-the-loop (HIL) test.

Research on Hierarchical Control Strategy of Automatic Emergency Braking System

In order to improve the active safety of vehicles, the control strategy of the vehicle automatic emergency braking system is studied. The hierarchical control idea is used to model the control strategy. The upper controller is a collision time model for the decision-making of vehicle braking deceleration, and the collision time threshold is determined under the condition of considering comfort. According to the braking deceleration output by the upper controller, the lower controller controls the vehicle by adjusting the throttle opening and braking pipeline pressure through PID control. Based on the typical test conditions of C-NCAP, a joint simulation test of CarSim and Matlab/Simulink for hierarchical control strategy is carried out. In order to achieve further verification, several groups of test conditions are conducted, and finally its effectiveness is verified, which can ensure the safety of drivers.

Development of a hierarchical control strategy for a soft knee exoskeleton based on wearable multi-sensor system

Soft exoskeleton is a newly emerging approach for providing assistive forces to the movement of disabled individuals, which has aroused the interest of many researchers due to its promising prospects in rehabilitation training and motion assistance. In this article, a wearable wireless multi-sensor system and a hierarchical control architecture are developed for a soft knee exoskeleton. First, the major components of the soft knee exoskeleton are introduced, including the flexible Bowden-cable actuators, the soft wearable components, and the sensing modules and the control system. The wearable wireless multi-sensor system consists of an inertial measurement unit–based knee flexion–extension angle sensing module, a surface electromyogram–based joint torque estimation module, a miniature pulling force sensing module, and a central communication unit. And then, a three-level hierarchical control strategy—which fuses a high-level motion recognition and torque estimation algorithm, a mid-level knee trajectory generation strategy, and a low-level fuzzy impedance controller—is proposed to realize human motion assistance. Finally, experiments are carried out to verify the effectiveness of the proposed control strategy. The experimental setup is established with the soft knee exoskeleton, a treadmill, a wearable robotic rotary joint with an encoder, and an inelastic rope. The experimental result indicates that the root mean square errors of the joint angle estimation strategy and joint torque estimation strategy are less than 4.3° and 4.8 Nm, while the surface electromyogram–based assistance efficiency and torque-based assistance efficiency under different loading conditions are higher than 10.2% and 18.2%, respectively.