Hierarchical Control Structure Research Articles

An improved gain-scheduling robust MPC for path following of autonomous independent-drive electric vehicles with time-varying and uncertainties

This paper proposes a gain-scheduling robust model predictive control (GS-RMPC) algorithm for the path-following problem of autonomous independent-drive electric vehicles (AIDEVs) with consideration of time-varying and uncertainties. Firstly, the polytopic uncertainty method and norm-bounded uncertainty method are introduced to characterise the vehicle dynamics model. Secondly, the infinite predict horizon optimisation process of online GS-RMPC is transformed into a series of linear matrix inequalities (LMIs) by minimising the worst-case objective function while considering all scheduling states in the polytope. Thirdly, an offline solution is also proposed to reduce the computational burden based on asymptotically stable invariant ellipse sets. Then, a hierarchical control structure is proposed to distribute the additional yaw moment, and a multi-step predictor is designed to compensate for the actuator time delay. Finally, the hardware-in-the-loop (HIL) testing is conducted to verify the efficacy of the proposed strategy.

Hierarchical control of linear systems using extended robust approximate simulation

This paper develops a hierarchical control structure for linear systems based on extended robust approximate simulation. In numerous real-world scenarios, systems are subject to external disturbances, and there is a restriction on accessibility to all of their states. Furthermore, these systems are modeled using linear dynamics with high state dimensions. Hence, the control synthesis problem for these concrete systems is computationally challenging, and constructing an abstract model with smaller state dimensions to design the controller would be beneficial. Therefore, we introduce the notion of a extended robust approximate simulation function between the concrete system and the abstract model under a new hierarchical control structure. With this respect, a controller is designed for the abstract model and refined for the concrete system by formulating an observer-based robust interface controller based on H∞ while guaranteeing the desired performance for the concrete system. It is worth noting that an Unknown Input Observer (UIO) is employed in the controller, which circumvents the full-state accessibility of the concrete system. The applicability of the presented approach is demonstrated by controlling bus voltages on a smart grid.

Research on Yaw Stability Control of Front-Wheel Dual-Motor-Driven Driverless Formula Racing Car

In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.

Synchronously Thermochromic and Shape‐Shifting Liquid Crystal Elastomers Enabled by Hierarchical Structure Control for Multidimensional Encryption

AbstractTo date, a diverse array of smart materials, drawing inspiration from nature, is developed, exhibiting remarkable shape or color‐changing capabilities, which hold great promise in applications such as bionic soft robots and information encryption platforms. Achieving reversible shape‐shifting and color variation harmoniously within a single material is a particularly appealing prospect. Herein, a facile approach to creating such materials is presented, capable of undergoing reversible shape and color‐change synchronously. This is achieved by harnessing the stress‐free two‐way shape‐memory effect (2W‐SME) of a liquid crystal elastomer (LCE), meticulously programmed at both macro and micro scales. Specifically, the LCE with stress‐free 2W‐SME is fabricated, and permanent grating nanostructures are meticulously constructed through UV‐assisted nanoimprinting. By enabling reversible shape alterations at multiple dimensions, encompassing both macro and micro scales, the LCE material concurrently exhibits reversible changes in shape and color, triggered by heat. As a result, intelligent information encryption and decryption platforms that leverage the unique capabilities of LCE are demonstrated. The outcomes of this research endeavor are poised to pave the way for the advancement of smart materials in the realms of data storage and encryption.

Hybrid supervised hierarchical control of a biological wastewater treatment plant.

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

Study on multi-objective adaptive cruise control of intelligent vehicle based on multi-mode switching

Aiming at the problems of slow response, poor comfort and high fuel consumption of the existing adaptive cruise control system under complex operating conditions, a multi-objective adaptive cruise control method of intelligent vehicles based on multi-mode switching is proposed. Firstly, the overall scheme of the adaptive cruise control system is designed by using the hierarchical control structure, and the multi-mode switching strategy is designed by using the fuzzy control theory to realize the division and switching of the working modes during vehicle cruise. Secondly, based on the variable spacing strategy and longitudinal kinematics model, the multi-objective of safety, following performance, fuel economy and comfort is analyzed and carried out, and a quadratic multi-objective optimization function based on multi constraints is obtained. Then, the model predictive control algorithm based on particle swarm optimization (PSO) is used to transform multi-objective function into a standard form with predictive control increment as the optimization variable, and the optimal control rate is solved. Finally, the simulation experiment is carried out by setting three complex working conditions: the preceding vehicle uniform speed change, the preceding vehicle rapid speed change and the adjacent vehicle cut in. The results show that the proposed method can meet the requirements of safety, comfort and fuel economy, and can improve the adaptability and friendliness of intelligent vehicle cruise control system.

Modeling and Clamping Force Tracking Control of an Integrated Electric Parking Brake System Using Sliding-Mode-Based Observer

This article proposes a hierarchical control strategy to address semi-ABS control as well as the precise clamping force control problems for an integrated electric parking brake (iEPB) system. To this end, a detailed system model, including modeling of the motor, transmission mechanism, friction and braking torque, is constructed for controller and observer design, and a sliding-mode-based observer (SMO) is proposed to estimate the load torque by using the motor rotational speed without installing a force sensor. In addition, a stable and reliable tire–road friction coefficient (TRFC) estimation method is adopted, and the desired slip ratio (DSR) is observed as the target that the rear wheels cycle around. At the upper level of the hierarchical control structure, the desired clamping forces of the rear wheels are generated using a sliding mode control (SMC) technique, and the control objective is to track the DSR to make full use of the road condition. At the lower level, the motor is controlled to track the desired clamping force generated from the upper controller. The hardware-in-the-loop (HIL) experimental results demonstrate the effectiveness and high tracking precision of the proposed strategy under different road conditions, and the estimation parameters based on the proposed observers are timely and accurate to satisfy the control requirements.

Deep Reinforcement Learning for Integration of Train Trajectory Optimization and Timetable Rescheduling Under Disturbances.

High-speed trains are susceptible to unexpected events such as strong winds and equipment failures, which can result in deviations from the scheduled timetable. As the density of traffic increases, these delays can quickly spread to other trains, eventually leading to conflicts in the timetable. To ensure the efficiency of high-speed railways, quickly resolving potential conflicts and generating appropriate rescheduling schemes are essential. The existing hierarchical structure of train control and online rescheduling tends to be inefficient in terms of information communication and can even lead to unfeasible rescheduled timetables and trajectories. To address these issues, an integrated structure of timetable rescheduling and train trajectory optimization is proposed by introducing the train minimum running time into the process of timetable rescheduling and using the adjusted running time as the objective of trajectory optimization. The integration model is formulated by considering the constraints of timetable rescheduling such as the maximum number of trains overtaking trains, platforms at stations, and the priority of the train, as well as the constraints of trajectory optimization. A deep reinforcement learning (DRL)-based approach is proposed to solve the problem. Numerical experiments are conducted on a segment of the Beijing-Shanghai high-speed railway line, using adapted data to demonstrate the effectiveness of the proposed method in rescheduling timetables and optimizing train trajectories. The results show that the integrated rescheduled timetable and the optimized train trajectory can be generated simultaneously and the computation time exhibits a linear increase with respect to the size of the problem.

Notice of Violation of IEEE Publication Principles: Integrated Control of Braking and Steering Subsystems for Autonomous Vehicle based on an Efficient Yaw Moment Distribution

Notice of Violation of IEEE Publication Principles Control of Braking and Subsystems for Autonomous Vehicle based on an Efficient Yaw Moment by Shaobo Lu, Sheng Cen, Xiaosong Hu, Cheewah Lim and Jinlong Zhang, in the IEEE Transactions on Industrial Electronics, Early Access, May 2017 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper copies equations from the paper(s) cited below. The content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. The lead and the second author of the paper were responsible for the violation. A correction letter for this misconduct was submitted and published at http://ieeexplore.ieee.org/document/8017449 Coordinated Control with Electronic Stability Control and Active Front Using the Optimum Yaw Moment Distribution Under a Lateral Force Constraint on the Active Front Steering by Seongjin Yim, Seungjun Kim and Heesung Yun, in the Proceedings of Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2016, Vol. 230(5) pp. 581-592 This paper presents an Integrated Chassis Control (ICC) strategy for Electronic Stability Control (ESC) and Active Four Wheel (AFWS) based on an efficient optimal yaw moment distribution. To further enhance the handling and lateral stability of vehicle equipped with ESC, a new Weighted Pseudo- inverse Control Allocation (WPCA) based ICC is proposed. The cornering forces of both front and rear wheels are used to cooperate with the ESC braking forces, so as to further extend the operational envelope of the vehicle. A bi-level hierarchical control structure is employed. In the upper level, the sliding mode control with a combined sliding surface is used to generate the desired virtual control. In the lower level, a revised optimal function is defined to tune the control authority of actuators, and an algebraic operation based WPCA method is adopted. To avoid tire forces saturation and enforce a certain stability margin, a boundary layer constraint is further considered in the proposed optimization problem. A severe lane change maneuver is used to investigate the performance via closed-loop driver-vehicle- controller simulations using CarSim and MATLAB/Simulink. Simulation results demonstrate that the proposed algorithm outperforms current control practice without violating the actuators physical limitation.

Hierarchical Stochastic Frequency Constrained Micro-Market Model for Isolated Microgrids

With the developments of isolated microgrids (IMGs) and prosumers in remote areas, energy trading has emerged as a critical aspect of IMGs. However, the lack of an upstream network and the low inertia of the system may threaten the secure operation of these networks. This paper proposes a Micro-Market (lM) model for IMGs that includes a precise hierarchical control structure. To address the IMGs low inertia and high intermittency of renewable energy sources (RES), the proposed lM manages the active-reactive power and schedules primary and secondary active reserves to maintain the frequency within in a predefined range. Additionally, a bidirectional linearized AC power flow is established to schedule the reactive reserve and the proposed model is formulated as a two-stage stochastic mixed-integer linear problem (MILP) to maximize social welfare (SW) over the next 24 hours. To validate the effectiveness of the proposed model, the lM is tested on an IMG based on a CIGRE medium-voltage benchmark system, and different operational cases are simulated. The results demonstrate that the proposed model, which takes into account hierarchical control levels and technical issues of the IMG, is a cost-effective way to maximize social welfare while ensuring the secure operation of the IMG.

A Hierarchical Distributed Data-Driven Adaptive Learning Control for Nonaffine Nonlinear MASs.

This article designs a new hierarchical distributed data-driven adaptive learning control algorithm to accomplish the leader-following tracking control objective for nonaffine nonlinear multiagent systems (MASs). The proposed hierarchical control structure is composed of a distributed observer and a decentralized data-driven adaptive learning controller. Considering that some followers cannot directly receive information from the leader, a distributed observer is designed to estimate the information of the leader. Based on this, a decentralized data-driven adaptive learning controller is further devised to enable the follower to track the estimated information of the leader, where the model parameter learning algorithm is developed to capture the dynamic characteristics of the original system. One advantage of the developed hierarchical control learning algorithm is that neither the leader's system model nor the follower's system model is needed. The other one is the elimination of the noncausal problem without the additional assumption. Simulation results exemplify the merits of the theoretical results by comparisons.

Risk Assessment of an Automated Document Management System in a Hierarchical Management System

The functioning of any document management system in the infrastructure of a hierarchical process control system is to ensure uninterrupted work with documents at all stages of their use for the system's subjects, from designers, administrators to users and executors. Only with a well-established security system with an authorization management system can an automated document management system (ADMS) achieve efficient operation. In its turn, the security system of the automated document management system is based on the developed security policy and risk management system. The paper considers the infrastructure of an automated document management system, methods for assessing risk levels, which determine the choice of security tools and determining the level of security for a hierarchical management system. The proposed research results have been tested in the functioning of security systems at airports with a hierarchical structure of process control.

A Novel Control Architecture Based on Behavior Trees for an Omni-Directional Mobile Robot

Robotic systems are increasingly present in dynamic environments. This paper proposes a hierarchical control structure wherein a behavior tree (BT) is used to improve the flexibility and adaptability of an omni-directional mobile robot for point stabilization. Flexibility and adaptability are crucial at each level of the sense–plan–act loop to implement robust and effective robotic solutions in dynamic environments. The proposed BT combines high-level decision making and continuous execution monitoring while applying non-linear model predictive control (NMPC) for the point stabilization of an omni-directional mobile robot. The proposed control architecture can guide the mobile robot to any configuration within the workspace while satisfying state constraints (e.g., obstacle avoidance) and input constraints (e.g., motor limits). The effectiveness of the controller was validated through a set of realistic simulation scenarios and experiments in a real environment, where an industrial omni-directional mobile robot performed a point stabilization task with obstacle avoidance in a workspace.

Data-Driven Optimal Control for Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste (MSW) composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network (OAFNN) is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory (SOLSTM) network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory (LSTM) neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

Enhancing dynamic operation optimization feasibility for constrained economic model predictive control systems

AbstractBased on the hierarchical control structure, optimization and control problems of large‐scale and multivariate plants are solved sequentially. The economic performance of the plant plays an essential role in the plant‐wide modern industry. The optimal operating conditions will change as the economic criteria changes throughout the operation of the plant as the result of variations in raw material prices, product prices, production demand, market fluctuations, disturbances, and so forth. In reality, soft constraints are frequently used to denote the production requirements of various operating conditions. In order to improve economic performance and guarantee feasibility for the entire plant operation, a novel economic model predictive control (EMPC) strategy is proposed to control the constrained multi‐variable process system with varying economic performance criteria under soft constraints. By incorporating the transient steady‐state and two categories of slack variables for soft constraints, a modified economic performance index is optimized to cope with the changing criteria. In addition, a contractive constraint is added to the closed‐loop system to guarantee stability for non‐dissipative stage costs. This approach ensures recursive feasibility and asymptotic stability. The effectiveness of the proposed method is demonstrated by numerical examples and the fluid catalytic cracking unit (FCCU) process.

Speed control with low complexity for multiple autonomous vehicles in roundabouts

The paper introduces a high level speed control method for the coordination of multiple autonomous vehicles (AVs) in roundabout scenarios. The aim of the control method is to guarantee collision-free motion of the AVs, and similarly, to minimize their traveling time. In the method a priority-based ordering process of the AVs is used, which enforces the time-efficient motion of the AVs. The collision-free motion is guaranteed through an optimization-based method including control input constraints. The ordering process and the optimization form a low complexity solution, which requires low computation effort. The proposed control strategy is involved in the high level of a hierarchical control structure. The effectiveness of the proposed control strategy is illustrated by simulation examples and Hardware-in-the-Loop demonstration.

Distributed and multi‐layer hierarchical controller placement in software‐defined satellite‐terrestrial network

AbstractAiming at the controller placement in software‐defined satellite‐terrestrial network, a distributed and multilayer hierarchical controller placement mechanism is proposed to improve the reliability and scalability. First, the metrics of network reliability, maintenance cost and usage cost are defined to establish the controller placement optimization model for the software‐defined satellite‐terrestrial network. Then, a distributed and hierarchical control structure composed of super controller, master controller, and slave controller is presented to enhance the scalability. Furthermore, according to the latitude variation of the LEO satellites, a time‐slot division method is proposed to staticize the network control topology. It reduces control link handover and enhances the stability of control links. In addition, a LEO satellite latitude based slave controller selection strategy and a shortest distance based inter layer control strategy are proposed to facilitate cost reduction and stability enhancement. Finally, extensive simulations demonstrate that the proposed control structure not only obtains high stability and scalability, but also efficiently saves the maintenance and usage cost. Besides, the impacts of service request distribution and satellite number on the control structure are analyzed.

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 Distributed Multiparticle Precise Stopping Control Model Based on the Distributed Model Predictive Control Algorithm for High-Speed Trains

Abstract The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional–integral–derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains.

Simultaneous control of mechanical strength and hierarchical structure in freeze‐casted porous alumina by two‐step sintering

AbstractTo increase mechanical strength of porous ceramics, here, an effective two‐step sintering (TSS) technique capable of producing highly porous alumina with enhanced mechanical strength is suggested. Based on the sintering theories, here, a significantly lower activation energy for densification at low temperature region allowed a beneficial temperature range for the TSS to be deduced. With a specific TSS regime (T1 = 1550°C and T2 = 1400°C), significantly higher compressive strength levels (8.00–15.24 MPa) were measured with an apparent porosity of 56.49% compared to conventional sintering (1.03–1.86 MPa) with similar apparent porosity of 57.84%. Specifically, another TSS regime (T1 = 1550°C and T2 = 1380°C) left submicron‐sized open pores within the lamellar walls, providing a hierarchical porous structure with enhanced mechanical strength. An evaluation of the mechanical stability by a finite element analysis indicated outstanding compressive strength even with small pores in the lamella walls.