Hierarchical Control Framework Research Articles

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

Distributed time‐varying optimization control for multirobot systems with collision avoidance by hierarchical approach

AbstractIn this article, a distributed time‐varying optimization problem for multirobot systems with collision avoidance is studied. A hierarchical control framework is employed to simplify the complex problem into two subproblems in two layers. One is the network layer where a novel distributed time‐varying finite‐time optimal control protocol as a signal generator is proposed for each robot. The signal generator ignores the model constraint and uncertainty. The other is the real‐time layer, which mainly contains a sampling module and a robust nonlinear model predictive control (NMPC) module. The former samples the optimal signal from the signal generator as a series of sampling tracking points with proper distance to improve tracking performance, and the latter searches for the optimal control protocol for the robot system to track these sampling tracking points in sequence with the model constraint and uncertainty. Furthermore, considering the collision between each robot, a give‐way strategy is designed that the robot with a low priority has to stop to wait for the other with a high priority to move first. And a primary‐secondary judgment technology determines the priorities of robots adaptively according to the current state of the robots. If the way forward of the moving robot is blocked by the stopping one, an auxiliary point is introduced as a temporary transfer station to avoid collision further. Finally, the effectiveness of the proposed methods is verified by a simulation example.

Real-time nonlinear model predictive control for optimizing the ammonia coverage ratio of an SCR system based on TC-QPSO

SCR systems with complex chemical reaction dynamics are important components of emission aftertreatment systems. Their contradictory emission requirements of higher NOx conversion efficiency and lower NH3 slip can be simultaneously achieved by optimizing the ammonia coverage ratio, which can be accomplished with a nonlinear model predictive control (NMPC) framework. However, how to find a reasonable NMPC approach for optimizing the ammonia coverage ratio and utilize an appropriate method to solve it remain a formidable challenge. In this paper, a two-layer hierarchical control framework for an SCR system is developed to decouple the optimization and tracking control of the ammonia coverage ratio. In the upper layer, an NMPC controller with a long prediction horizon is designed to account for the emission objectives and constraints and generate optimal trajectories for the ammonia coverage ratio. In the lower layer, a robust observing and tracking controller with a short horizon is employed which takes the planned trajectories as the tracking target. Moreover, an improved thermonuclear-initiation and cross-boundary quantum-behaved particle swarm optimization (TC-QPSO) method for optimizing the ammonia coverage ratio based on its dynamic characteristics is proposed to solve the NMPC problem. The simulation results show that the proposed TC-QPSO emission optimization effects of NOx and NH3 are closest to those of the SQP method (the benchmark controller) with a slight computation time penalty. The experimental results under the three transient cycles show that the average NOx conversion efficiency of the proposed two-layer hierarchical control framework with the TC-QPSO algorithm can reach more than 98% with an average NH3 slip of less than 11 ppm, which is able to satisfy emission requirements, and that compared to the traditional QPSO algorithm, the average NH3 slip of the proposed TC-QPSO algorithm is improved by 45.6% with an average NOx conversion efficiency penalty of 1.09% and its emission optimization effects have better robustness for uncertainties in transient cycles.

A Consensus-Based Distributed Temperature Priority Control of Air Conditioner Clusters for Voltage Regulation in Distribution Networks

High penetration of Photovoltaic (PV) to the distribution network may bring under-voltage and over-voltage issues, limiting the PV hosting capacity. Air conditioners (AC) in grid-interactive buildings can support voltage regulation by manipulating flexible energy consumption. This paper developed a novel voltage control strategy to regulate the AC clusters’ on/off states for distribution network voltage regulation under high PV penetrations. The novelty lies in the distributed formulation of temperature priority-based on/off control (TPC) of AC clusters and the strategic selection and permutation of demand response technologies, including the real-time optimal demand response resources dispatch, distributed sensing of ACs based on average consensus algorithm, and the local implementation of TPC strategy and trial calculation scheme for flexibility capacity estimation. Finally, the distributed TPC is validated to be effective for system rebalancing with no comfort violations and an acceptable ON/OFF switching frequency. The theoretical and numerical analysis also proves its scalability and robustness to communication delays and link failures. It is then incorporated into a novel hierarchical control framework for smart grid voltage control in a four-bus three-phase test grid, considering the voltage sensitivities to power injections in different locations and phases.

Hierarchical Planar Platoon Control Toward Tracking Safety for Distributed-Driven Vehicles

This paper deals with the planar platoon control problem of distributed-driven electric vehicles considering modeling deviations and driving disturbances. The follower vehicle is arranged to track the leader vehicle on curved roads with high tracking accuracy and driving security. A hierarchical integrated control framework is proposed based on disturbance-rejection robust model predictive control (DRRMPC) strategy and optimal control allocation (OCA) approach. The upper layer is a feedforward control layer, where a linear time-varying (LTV) model predictive controller (MPC) produces constrained predictive control towards a nominal model. The time-interval-based reference states are obtained from a “shadow following” trajectory generator through vehicle-to-vehicle (V2V) communication. In the middle layer, the feedback control consisting of a robust controller (RC) and the disturbance compensation out of an extended state observer (ESO), take effect to reconfigure the LTV vehicle model into the nominal model with an off-line calculated constraint tube. The bottom layer performs optimal control allocation among decoupled motor torques and wheel angles towards minimal tire force utilization. Hardware-in-the-Loop (HIL) experiment validates the effectiveness and practicability of the proposed platoon control strategy.

A Learning Based Hierarchical Control Framework for Human–Robot Collaboration

In this paper, using the ball and beam system as an illustration, a control scheme is developed on human-robot collaboration, i.e., a two-level hierarchical framework is proposed to establish a robust human-robot collaboration (HRC) policy. On the high level, a deep reinforcement learning (DRL) algorithm is presented to plan the desired beam rotational velocity. The low level is constructed by a human-intention perception module and a robust collaboration policy design module. For the first module, a probabilistic model is fitted by using the Gaussian process regression (GPR) approach to predict human-hand velocities, and prediction results follow Gaussian distributions where mean values and variances represent predicted human-hand velocities and corresponding prediction confidences, respectively. For the second module, a robust collaboration policy is established by fusing a proactive policy and a conservative policy, where the proactive policy is used to control the robot to achieve the desired beam rotational velocity by using the predicted human-hand velocities. The conservative policy is designed to ensure the collaboration safety. The weighted parameters for fusion are adaptively tuned based on the prediction precision and confidence. Experiments are conducted on controlling ball position on a beam jointly by a human and a robot with vision data, and experimental results show the effectiveness of the designed robust collaboration policy. Note to Practitioners—Predicting human future behaviors and moderating robot behaviors accordingly is a long-standing problem for human-robot collaboration (HRC) tasks, such as assembling, transporting, etc. Existing approaches generally regard human behaviors as noises or only build simple human models without prediction confidence. This paper proposes a learning-based hierarchical framework that will derive a robust and safe HRC policy considering human behaviors, prediction confidence, and task-related optimality. The framework is validated by a representative experiment where human and robot are asked to jointly control a ball and beam system.

Hierarchical Prescribed-Time Coordination for Multiple Lagrangian Systems With Input-to-Output Redundancy and Matrix-Weighted Networks

This paper investigates the prescribed-time coordination problem for multiple Lagrangian systems (MLSs) in the presence of input-to-state redundancy, uncertain dynamic terms, and external disturbances. Moreover, the interdependencies among multiple Lagrangian plants with multi-dimensional states are characterized by matrix-weighted networks. Then, a novel hierarchical prescribed-time control (HPTC) algorithm comprising two parts as well as a hierarchical control framework consisting of two layers are provided to address the aforementioned problem. By virtue of the Lyapunov stability and the nearest neighbor-interaction rules, several sufficient conditions for achieving prescribed-time coordination of the MLSs with input-to-state redundancy and matrix-weighted networks are obtained. Additionally, the designed HPTC algorithm is extended to the case of directed matrix-weighted communication networks in the presence of a directed spanning tree with the leader as the root. Eventually, numerical simulations are provided to demonstrate the validity of the obtained theoretical results.

Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles

A reliable energy optimization strategy incorporating vehicle connectivity is of great importance for the performance enhancement of fuel cell electric vehicles. In this paper, a multihorizon hierarchical model predictive control framework is proposed, which reduces energy consumption while incorporating fuel cell lifetime management though real-time speed preview. Specifically, the trajectories of battery state of charge are explored via convex optimization in the upper layer to provide a suboptimal reference for real-time optimization, and the concept of multihorizon is introduced into convex optimization for the first time. At the lower level, an equivalent consumption minimum strategy-based model predictive control is designed, which improves energy utilization efficiency and prolongs the lifetime of fuel cells. The main contribution of this paper is to use multihorizon optimization to solve the energy optimization and lifetime management of fuel cell electric vehicles over different timescales. Experimental results show that the proposed strategy has great potential in cost saving, which can reduce 10.10% to 16.95% of the total cost in real driving conditions compared with the rule-based strategy.

A Reinforcement Learning-Based Strategy of Path Following for Snake Robots with an Onboard Camera.

For path following of snake robots, many model-based controllers have demonstrated strong tracking abilities. However, a satisfactory performance often relies on precise modelling and simplified assumptions. In addition, visual perception is also essential for autonomous closed-loop control, which renders the path following of snake robots even more challenging. Hence, a novel reinforcement learning-based hierarchical control framework is designed to enable a snake robot with an onboard camera to realize autonomous self-localization and path following. Specifically, firstly, a path following policy is trained in a hierarchical manner, in which the RL algorithm and gait knowledge are well combined. On this basis, the training efficiency is sufficiently optimized, and the path following performance of the control policy is greatly improved, which can then be implemented on a practical snake robot without any additional training. Subsequently, in order to promote visual self-localization during path following, a visual localization stabilization item is added to the reward function that trains the path following strategy, which endows a snake robot with smooth steering ability during locomotion, thereby guaranteeing the accuracy of visual localization and facilitating practical applications. Comparative simulations and experimental results are illustrated to exhibit the superior performance of the proposed hierarchical path following the control method in terms of convergence speed and tracking accuracy.

Hierarchical control for stochastic network traffic with reinforcement learning

This study proposes a hierarchical control framework to maximize the throughput of a road network driven by travel demand with uncertainties. In the upper level, a perimeter controller regulates the traffic influx into the core road network. The upper level uses a reinforcement learning algorithm that learns and responds to the traffic dynamics in the core road network without the need for an underlying system model and macroscopic fundamental diagram. The lower level is a local signal control system that regulates the spatial distribution of traffic flow within the core network. The results show that the hierarchical control framework can improve road network throughput by coordinating control actions conducted at the two levels. The improvement in system-wide performance is validated by a range of performance metrics and macroscopic flow-accumulation patterns realized under different control settings. The study contributes to the management of urban road networks with advanced computing technologies.

A Fast Optimal Speed Planning System in Arterial Roads for Intelligent and Connected Vehicles

Speed planning system is generally equipped for intelligent and connected vehicles (ICVs). Under the circumstances of autonomous driving, an energy-optimal speed trajectory is usually desired, particularly on urban arterial roads with complex traffic conditions involved. However, the existing speed planning solutions in the literature have not dealt with the problem of time consuming. Also, the ego vehicle could not perfectly track a precalculated speed reference because of the dynamically varying traffic. Thus, optimal speed planning cannot always be guaranteed. In this article, a fast optimal speed planning system for complex urban driving situations is established through an adaptive hierarchical control framework. In the planning layer, dynamic programming (DP) and the interior-point optimizer are jointly used to compute the global speed trajectory with access to signal phase and timing (SPaT) information. The computational burden is greatly alleviated based on a weighted orientation graph assumption and problem decomposition. The following layer utilizes the Informer, which is a transformer-based model, to predict preceding vehicle speed. Then, a target-switching model-predictive controller (MPC) is adopted for global speed trajectory following and adaption. The proposed approach significantly reduces speed planning computation time compared to previous solutions. Simulation results based on real road traffic scenes manifest that 22.0% of energy is saved compared with human driving.

Fast hierarchical coordinated controller for distributed battery energy storage systems to mitigate voltage and frequency deviations

This paper proposes a novel hierarchical optimal control framework to support frequency and voltage in multi-area transmission systems, integrating battery energy storage systems (BESSs). The design is based on the coordinated active and reactive power injection from the BESSs over conventional synchronous generator-based control for fast and timely mitigation of voltage and frequency deviations. The principle of this new idea is to use two hierarchical schemes, one physical and one logical. The objective of the first scheme prioritises the power injection from the BESSs installed in the area where a contingency occurs, consequently reducing the disturbance of the dynamics in the neighbouring areas. In the second scheme, operational rules for aggregated BESSs in each are incorporated, increasing the safety of the asset. The proposed approach exploits the advantages of time-synchronised measurements, the eigensystem realisation algorithm (ERA) identification technique, the optimal linear quadratic Gaussian (LQG) controllers and a new aggregating agent that coordinates the power injection of BESSs in a hierarchical and scalable scheme to precisely regulate frequency and voltage of modern transmission grids, increasing their reliability and stability. The feasibility and robustness of the proposal is demonstrated using simulated scenarios with significant load changes and three-phase, three-cycle faults on a modified Kundur-system with four interconnected areas, mitigating frequency and voltage contingencies in less than 450 ms.

Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles

Energy management is one of the key technologies to improve the energy efficiency of electrified vehicles. In the existing real-time powertrain control strategies, most studies focus on improving fuel economy based on high-fidelity powertrain models without adequately exploring the impact of model accuracy on computational efficiency and energy saving. To address this research gap, this paper proposes a hierarchical control framework to minimize the fuel consumption of a plug-in hybrid electric vehicle. Specifically, three main contributions are presented to distinguish our efforts from existing research. First, two types of powertrain models are used in the optimization framework. In the upper control layer, an approximated model is employed to generate the optimal reference state of charge trajectory using convex optimization. Then in the lower control layer, by integrating the equivalent consumption minimize strategy into the model predictive control framework, the fuel consumption is minimized in real-time by using a high-fidelity powertrain model. Second, optimization results from the other three real-time control strategies and two predictive energy management strategies are presented and analyzed to verify the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are discussed.

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Due to the proliferation of requests in heterogeneous resources in edge computing, the existence of a large number of tasks and workloads in virtual machines in the edge computing environment is inevitable. Thus, load balancing strives to facilitate an even distribution of workload across available resources. Its purpose is to provide continuous service and to ensure fair load distribution among resources. Load balancing, with the aim of minimizing response time for tasks and improving resource efficiency, tries to do the proper mapping of tasks among virtual machines at a lower cost. Flow scheduling, on the other hand, assigns a task (group of tasks) to computational resources by prioritizing tasks, so that the relationship between them is maintained. Therefore, in this research, a hierarchical control framework for load balancing and assignment of tasks in edge computing services is presented in order to create load balancing. In the proposed method, in the first level, the genetic algorithm receives a set of tasks in a workflow. Genetic algorithm prioritizes and assigns tasks to resources according to time constraints, resource processing power, resource availability, and task cost. For this purpose, the integer linear programming optimization in the evaluation function of the genetic algorithm will be utilized. In the second level, considering the past load distribution in edge resources, we estimate the probability of load distribution among sources according to hidden Markov model (HMM). Finally, in order to optimally map tasks to the virtual machines in each host, we will use game theory with service quality factors as an evaluation function. Previous methods have provided a hierarchical control framework that aims to achieve conflicting goals within a data center, but does not use linear programming. Considering the use of service quality criteria as evaluation function parameters in heuristic and optimization methods in this research, it is expected that the results of this research will improve compared to previous methods.

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.

An admittance based hierarchical control framework for dual-arm cobots

Dual-arm robotic platforms have solid arguments to match the growing need for versatility in the industry. Coupling the control of two manipulators for cooperative purposes enlarges the scope of feasible operations, while adding perception capabilities allows to navigate in dynamic environments. In this respect, we propose a complete online kinematic control framework for dual-arm robots operating in unstructured industrial settings. We base our approach on admittance control in the cooperative task space. Regulating internal and external efforts offer safe bimanual task execution and enables physical interaction. We implement a hierarchical quadratic programming architecture that applies a prioritization of tasks: most efforts are concentrated on the proper tracking of relative motions of the arms, which is the most critical for safety reasons. We demonstrate the performance of our framework through a “teaching-by-demonstration” experiment on the dual-arm mobile cobot BAZAR.

HD-RMPC: A Hierarchical Distributed and Robust Model Predictive Control Framework for Urban Traffic Signal Timing

Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in urban traffic networks has always been an important challenge. In recent years, model predictive control (MPC) has shown extraordinary potential in TSC due to its outstanding ability to model dynamic systems. However, the relatively complex online computing, lack of reasonable setpoints for target solving, and uncertainty of traffic network hinder MPC from being further applied. To address these problems, we propose a hierarchical, distributed, and robust model predictive control (HD-RMPC) framework for urban TSC. At the slow-update layer, the road network is dynamically divided into several subareas according to regional attributes and real-time traffic demand. Meanwhile, the volume is coordinated in a robust way for the purpose of traffic equilibrium and overflow prevention. Then, the set-point matrix of each subarea is calculated to equalize the flow in the subarea. This distributed framework guarantees the real-time performance of MPC in urban traffic networks. At the fast-update layer, we adopt an improved prediction model by explicit modeling of the disturbance and reduce the prediction error. Finally, the objective function is reconstructed and solved at the control layer to obtain the optimal control law. Through continuous and asynchronous optimization of the set point and prediction model, the framework significantly improves the control effect. Simulation evaluation based on a real-world road network demonstrates that the proposed HD-RMPC method outperforms all baselines and maintains excellent real-time performance.

A Flexibly Assembled and Maneuverable Reconfigurable Modular Multirotor Aerial Vehicle

This article introduces a novel modular reconfigurable multirotor aerial vehicle that can flexibly configure the amount and position of rotors. According to different actual task requirements, such aerial vehicles can be flexibly assembled into bicopters, tricopters, quadcopters, and omnidirectional aerial vehicles. These multirotor aerial vehicles can independently control their position and attitude and track arbitrary trajectories in a 3-D space. Such maneuverability owes to a double-gimbal thrust module (DGTM), which can independently regulate the direction and magnitude of propeller thrust. The reconfigurable multirotor aerial vehicle’s dynamic model is established based on the analysis of the rotor distribution’s vector thrust and topological structure. Then, a hierarchical cooperative control framework is applied for the pose tracking of the vehicle. In the top level, a Lyapunov-based pose controller is proposed to calculate the desired force and torque. In the bottom level, a control allocation that maps the desired force and torque to desired input of the DGTM is used. An experimental prototype is built to verify the feasibility of the design scheme.

Distributed Optimization of Multiagent Systems Against Unmatched Disturbances: A Hierarchical Integral Control Framework

This article investigates a distributed optimization problem of double-integrator multiagent systems with unmatched constant disturbances. Instead of involving an internal model or a disturbance observer to deal with the disturbances as in existing works, a two-layer control framework is presented based on state-integral feedback control (SIFC) and adaptive control techniques. The upper layer uses a virtual system to generate a global optimal consensus trajectory which is shared by the agents via a communication network. The lower layer includes an SIFC controller to guarantee asymptotic tracking of the given trajectory. Also in this layer, a model reference adaptive controller is introduced to enhance the dynamic tracking performance of the SIFC controller. This framework enables distributed optimization with time-triggered communication and mild requirements on the team objective. The method yields an interesting co-design algorithm of the control parameters and the communication intervals, which is proved to be convergent using Lyapunov stability theory. The effectiveness and advantages of the method are illustrated by numerical simulations.

Lyapunov Redesign-Based Optimal Consensus Control for Multi-Agent Systems With Uncertain Dynamics

This brief investigates an optimal consensus problem for a set of integrator systems with dynamic uncertainties. A hierarchical distributed control framework based on partial global information is given, which can reach the optimal solution of the team objective function. Each agent has an optimal control law based on the neighbors’ information and the gradients of the local cost functions, and a compensator derived with Lyapunov redesign technique to deal with the dynamic uncertainties. Then, to remove the constraint on initial conditions and the need for global knowledge of the state, the state differences between neighbors are involved to strengthen the optimal controller while local information feedback is included in the compensator. The resulted fully distributed algorithms can guarantee asymptotic convergence of the systems in the sense of input-to-state stability (ISS). The effectiveness of the method is illustrated by numerical examples.