Hierarchical Control System Research Articles

Neural Mechanisms of Hypnotic Analgesia

There is increasing evidence demonstrating that hypnosis could be effective in the down-modulation of pain sensation in both acute and chronic pain states. In the neurophysiological context, recent evidence has deciphered, to a certain extent, the mystery of pain relief upon hypnosis. It is probable that hypnotic suggestions of analgesia are able to modulate pain processing at multiple levels and sites within the central nervous system (CNS). At the peripheral level, hypnosis may modulate the nociceptive input through the down-regulation of the stimulation of A delta and C fibers and reduction of sympathetic arousal. At the spinal level, sensory analgesia occurring during hypnosis has been demonstrated to be linearly associated with the reduction in the nociceptive flexion (RIII) reflex, a polysynaptic spinal reflex. At the supraspinal cortical level, neuro-imaging and electrophysiological studies have demonstrated that hypnotic suggestions of analgesia could directly modulate both sensory and affective dimensions of pain perception, and the affective dimensions exhibit more significant reduction compared to the sensory ones. Moreover, highly hypnotizable subjects possess stronger attentional filtering abilities in comparison to the low hypnotizable subjects; this greater cognitive flexibility of the former might result in better focusing and diverting the attention from the nociceptive stimulus as well as in better ignoring of the irrelevant stimuli in the environment. Cognitive control processes are associated with a “supervisory attentional system” which involves fronto-temporal limbic cortices. Multiple hierarchical pain control systems functioning during hypnotic suggestions of analgesia, demonstrating specific patterns of peripheral and central activation associated with the hypnotic state and with the processing of hypnotic suggestions, provide a novel description of the neurobiological basis of hypnotic analgesia.

Towards Universal Control System for Powered Ankle–Foot Prosthesis: A Simulation Study

In powered ankle–foot prostheses, multilevel hierarchical control systems are usually used with predetermined parameters (tuned during the prosthesis trial). Therefore, control systems cannot adaptively interact with terrains variation where control systems is most effective for ground level walking and less effective for ascending or descending stair/slop. In order to address the control system performance in ever-changing terrains, an adaptive mechanism should be included the control system structure. Here, we present a pilot study to illustrate the applicability of a genetic algorithm-based adaptive fuzzy logic control system. The design method could be divided into two stages: initial knowledge base and membership functions for the genetic pool on the basis of the analysis of biological ankle–foot behaviour. Additionally, the construction of genetic optimization mechanism rules and constraints (fitness function, mutation rats, replacement rate, etc.). Takagi–Sugeno–Kang fuzzy (TSK-fuzzy) inference system is selected because the system structure could depict the character of simple impedance control system. The control system and dynamic model were developed using C code and evaluated using MATLAB/Simulink (2019a).

A Universal and Reconfigurable Stability Control Methodology for Articulated Vehicles With Any Configurations

To meet with many different transportation needs, it comes in a rich diversity and variety of articulated vehicles. Vehicle combinations are seen in different axle configurations, number of articulations, powertrain, active actuation systems, etc. This research is, therefore, motivated to develop a model-based control system in a universal and reconfigurable fashion to any articulated vehicles stability control. To achieve its universality and reconfigurability, we introduce a hierarchical (two-layer) control system. Namely, the high layer formulates a model predictive control (MPC) tracking problem to generate corrective Center of Gravity (C.G.) forces/moment. The lower-level controller is formulated as Control Allocation (CA) algorithm to regulate steering or torque (brake) at each wheel optimally and reconfigurable as to meet high-level calculations. Real-time constraints, i.e. actuator limits, tire capacity, and actuator failure are discussed. Diverse applications are presented that the universal and reconfigurable methodology is handy, capable and effective on stability control while applying to various vehicle configurations and objectives.

Inter-domain resource optimization method of software-defined multi-domain optical network

In practical application scenarios, the division of different operators or the same operator in different regions constitutes a natural network region, forming a multi-domain optical network. At the beginning, this paper introduces the hierarchical architecture of software-defined optical network (SDON) and the functional model of control plane. Then, it describes a control system of software-defined multi-domain optical network based on hierarchical control mode and briefly analyzes their basic functional model. In the end, around the problems of resource allocation between scenarios in multi-domain optical network, this paper respectively analyzes the optimization of routing resources in inter-domain and the mechanism of spectrum defragmentation. At the same time, it separately simulates the resource optimization algorithms of single control architecture and hierarchical control architecture to verify the superiority of hierarchical control system.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators.

Bioreactors of various forms have been widely used in environmental protection, healthcare, industrial biotechnology, and space exploration. Robust demand in the field stimulated the development of novel designs of bioreactor geometries and process control strategies and the evolution of the physical structure of the control system. After the introduction of digital computers to bioreactor process control, a hierarchical structure control system (HSCS) for bioreactors has become the dominant physical structure, having high efficiency and robustness. However, inherent drawbacks of the HSCS for bioreactors have produced a need for a more consolidated solution of the control system. With the fast progress in sensors, machinery, and information technology, the development of a flat organizational control system (FOCS) for bioreactors based on parallel distributed smart sensors and actuators may provide a more concise solution for process control in bioreactors. Here, we review the evolution of the physical structure of bioreactor control systems and discuss the properties of the novel FOCS for bioreactors and related smart sensors and actuators and their application circumstances, with the hope of further improving the efficiency, robustness, and economics of bioprocess control.

Optimal resource allocation in the difference and differential Stackelberg games on marketing networks

We consider difference and differential Stackelberg game theoretic models with several followers of opinion control in marketing networks. It is assumed that in the stage of analysis of the network its opinion leaders have already been found and are the only objects of control. The leading player determines the marketing budgets of the followers by resource allocation. In the basic version of the models both the leader and the followers maximize the summary opinions of the network agents. In the second version the leader has a target value of the summary opinion. In all four models we have found the Stackelberg equilibrium and the respective payoffs of the players analytically. It is shown that the hierarchical control system is ideally compatible in all cases.

Robust Cascade LMI Design of MIMO Control System for Plasma Position, Current, and Shape Model with Time-Varying Parameters in a Tokamak

A new robust hierarchical plasma magnetic control system with time-varying parameters and cascade circuits for the Globus-M2 tokamak (Ioffe Institute) has been synthesized by means of the linear matrix inequalities (LMI) method. Each control cascade has a separate objective: placing poles of the closed-loop system in the D-region to guarantee robust performance, restriction of the H∞-norm of the transfer function between external disturbances and the plant outputs of the closed-loop system, tracking PF-currents, CS-current, plasma current, plasma position, gaps between the plasma separatrix and the first wall. Control cascades were synthesized for an array of plant linear models corresponding to reconstructed plasma equilibria at different time points of the tokamak discharges. The LMIs allow the synthesis of one LTI controller, providing the required performance and system stability margin for each model from a given array. Tracking cascades use new MIMO PID controllers adjusted by means of the LMIs.

Cyber-physical Power System Modeling for Timing-driven Control of Active Distribution Network

In a cyber-physical power system, active distribution network (ADN) facilitates the energy control through hierarchical and distributed control system (HDCS). Various researches have dedicated to develop the control strategies of primary devices of ADN. However, an ADN demonstration project shows that the information transmission of HDCS may cause time delay and response lag, and little model can describe both the ADN primary device and HDCS as a cyber-physical system (CPS). In this paper, a hybrid system based CPS model is proposed to describe ADN primary devices, control information flow, and HDCS. Using the CPS model, the energy process of primary devices and the information process of HDCS are optimized by model predictive control (MPC) methodology to seamlessly integrate the energy flow and the information flow. The case study demonstrates that the proposed CPS model can accurately reflect main features of HDCS, and the control technique can effectively achieve the operation targets on primary devices despite the fact that HDCS brings adverse effects to control process.

ПРОТОТИП ТРАНСФЕМОРАЛЬНОГО ПРОТЕЗА С АКТИВНЫМ ИСКУССТВЕННЫМ КОЛЕННЫМ СУСТАВОМ

This article presents a transfemoral prosthesis prototype with active control of an artificial knee joint. One of the main criteria used in the design of the prosthesis was to achieve the maximum biological similarity of this device in order to provide optimal conditions conducive to user natural walking. The artificial knee joint, designed on the basis of a polycentric higed mechanism with intersecting links, provides such conditions at the design level, and a three-level hierarchical control system, built on the basis of an intelligent-synergetic concept, at the control level. To recognize user's intentions, the intelligent subsystem uses algorithms for comparing graphic images of user's walking phases by the method of estimating the invariant moments of Hu. After that, prosthesis elements movements are planned in the synergistic subsystem in accordance with the synergistic quality criteria. The algorithms used in the control system are adjusted depending on what type of artificial foot is used in the prosthesis: active, semi-active or passive (purely mechanical). Mathematical modeling of the prosthesis operation shows that the nature of its functioning corresponds to the quality criteria adopted in the design.

Energy Costs of Production and Project Assessment

Corporation cycle of developing a project and its implementation in the production to minimize energy costs is considered. A hierarchical control system for this cycle is reviewed for harmonizing the interests of its elements taking into account the human factor. A procedure for a comprehensive assessment of project to diminish the energy costs of production is proposed. The key factor of such an assessment is the energy costs rating of existing production. The problem of determination of such a rating is formulated. Conditions for such determination are obtained taking into account the interests of the elements of the corporation responsible for production. These conditions are illustrated by the example of determining the ratings of energy costs of production in large scale corporation Russian Railways. An example of a comprehensive assessment procedure for projects to reduce the cost of energy production in this corporation based on these ratings is given.

The application of game theory principles for the increase in the productivity of coal industry workers

In the Russian Federation the main factor for the reduced labor productivity in the coal mining industry, with almost the same level of technological equipment as in other countries, is human: non-compliance with safety regulations, low motivation, lack of labor discipline among employees of enterprises. The solution to the problem of increasing the labor productivity of coal industry workers largely depends on how effective the fight against these factors is. By choosing the mechanisms of economic and administrative incentives for employees, as well as by creating a hierarchical control system, it is possible to ensure that the behavior optimal for employees is exactly what management needs. This work is devoted to the discussion of methodology for constructing such mechanisms, based on the analysis of game-theoretic models of organizing a safety management system at a coal mining enterprise, proposed in earlier works by the authors of the paper.

Mixed Integer nonlinear optimization of biological processes in wastewater sequencing batch reactor

Wastewater treatment plays a key role for humanity. The waste entering lakes, rivers, and seas deteriorates daily quality of life. Therefore, it is very important to improve the efficiency of wastewater treatment. From a control point of view, a biological wastewater treatment plant is a complex, non-linear, multidimensional, hybrid control system. The paper presents the design of the optimizing hierarchical control system applied to a Sequencing Batch Reactor (SBR) in a biological Wastewater Treatment Plant (WWTP). Based on real data records, the ASM2d model of biological processes and the aeration system model are applied. The two-layer hierarchical control structure described in previous works has been extended by an additional optimization layer, used to determine the optimal operating parameters of the SBR reactor (sequence and durations of individual phases), the optimal desired trajectory of dissolved oxygen, and the optimal parameters of the adaptive controller operating in the upper control layer. A large number of different deterministic and non-deterministic optimization algorithms were tested. Finally, the Artificial Bee Colony (ABC) algorithm and the Direct Search Algorithm (DSA) were applied. In the proposed control structure, the lower control layer is the Internal Model Control (IMC) based airflow controller. Results of simulation tests for the wastewater treatment plant case study are presented. As an optimization result, the process operations have become more efficient and the electrical energy consumption has been reduced.

Hierarchical control system for a flexible microgrid with dynamic boundary: design, implementation and testing

The design, implementation, and testing of a control system for a flexible microgrid (MG) is presented in this study. The MG controllers can be implemented in a real-world MG with multiple smart switches, photovoltaic panel system, and battery energy storage systems (BESSs). With the benefits from smart switches, the MG has unique characteristics such as dynamic boundary and flexible point of interconnection (POI) concepts. To control such a unique MG and realise the dynamic boundary, an MG central controller and two types of local controllers are implemented. Compared to the MG with fixed boundary, the MG with dynamic boundary can have smaller BESS capacity, better utilisation of renewable energy, and multiple POI options. Also, compared with IEEE Std 2030.7–2017, the topology identification and active and reactive power balance functions are newly designed to realise the dynamic boundary concept. The planned islanding and reconnection functions are modified to realise the flexible POI concept. These functions are introduced including the software architecture, cooperation, and interaction among them. Finally, a hardware-in-the-loop testing platform based on the Opal-RT real-time simulator is set up to verify the performance, realisation of the dynamic boundary, and flexible POI concepts with four comprehensive test scenarios.

Prediction of optimum sampling rates of air quality monitoring stations using hierarchical fuzzy logic control system

Air quality monitoring systems are being developed and optimized continuously through different methods, and emerging technologies in computational software and simulations have provided efficient and promising results. The process of air quality monitoring requires taking air samples, and data sampling can consume energy, produce a redundant traffic load on the communication channel, and memory space in monitoring station electronic control units. Researches showed that adaptive sampling rate can be a solution to save energy and memory space by turning on sampling module during sensing different events. Therefore, the purpose of this paper was to predict the optimum sampling rates of air quality monitoring stations without compromising data accuracy. For this aim, a hierarchical fuzzy logic control system (simulated in MATLAB®/Simulink) was proposed based on contextual conditions including meteorological data of the country and measured concentrations of air pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). The analysis of the results showed that the sampling rate had a positive correlation with the concentration of the pollutants and the permissible thresholds. Also, the meteorological factors had a lower influence in determining the sampling rate compared to the concentrations of air pollutants.

Policy Adaptation in Hierarchical Attribute-based Access Control Systems

In Attribute-Based Access Control (ABAC), access to resources is given based on the attributes of subjects, objects, and environment. There is an imminent need for the development of efficient algorithms that enable migration to ABAC. However, existing policy mining approaches do not consider possible adaptation to the policy of a similar organization. In this article, we address the problem of automatically determining an optimal assignment of attribute values to subjects for enabling the desired accesses to be granted while minimizing the number of ABAC rules used by each subject or other appropriate metrics. We show the problem to be NP-Complete and propose a heuristic solution.

Hierarchical Adaptive Control System of a Manipulator Based on the Synthesis of a Neural Network of Fuzzy Inference and an Iterative Refinement Algorithm

The main aim of this work is to develop an adaptive control system for a kinematically redundant multilink industrial manipulator. Proposed solution allows to construct a unified real-time control system with the ability to control the accuracy of calculations. In order to achieve the required accuracy of the calculations and the performance of the control system, we propose an algorithm that is based on the so-called hybrid method for finding the solution of the inverse kinematics (IK) problem, including the adaptive neural network and fuzzy inference system with subsequent iterative refinement of numerical solution by the Newton --- Raphson method. The influence of the training sample size on the quality of the obtained initial approximation for the neural network part of the algorithm is described in the paper. The results of experimental studies of the developed hybrid algorithm are presented in comparison with the iterative and neural network methods for three-, five- and eight-link manipulator structures. Paper presents the main steps of the control system synthesis for kinematically redundant industrial manipulator, including the description for developed algorithms for finding an IK solution of multilink structures. The structure of a multi-level hierarchical manipulator control system, based on a programmable logic controller and electric stepping motors with the possibility of integration into the production system at various levels, is presented.

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

Integrated wide area protection and control for power grid security

The traditional “three defense lines” for power systems are based on local information and static protection & control strategy, which are not suitable to modern largescale power systems. In order to improve the security of UHV hybrid power grids, the Integrated Wide Area Protection & Control (IWAPC) is proposed in this paper by applying the new technologies of synchronized high-speed communication, which integrates “three defense lines” and promotes existing wide area protection only for security control. The IWAPC is the hierarchical protection and control system which provides the protection and control for wide area power networks to improve their reliability and security. It is divided into three levels, the local bay level, the substation integrated protection level and the wide area protection level. The wide area realtime protection and control information platform is the most important part of the IWAPC, which is based on a synchronized wide area communication network. The key technologies and new development trends include network topology analysis, wide area backup protection, wide area intelligent reclosing, wide area load shedding, wide area auto-switching, overload cutoff and transfer, transmission section protection, intelligent system splitting and dynamic stability control. It cannot only integrate three lines of defense for power system protection and control, but also improve security of the power grid.

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajectory for the lower level parts of the hierarchical control system. Proper adjustment of this element has an essential impact on the efficiency of the wastewater treatment process as well as on the costs generated by the plant, especially by the aeration system. The main goal of the presented solution is to reduce the plant energy consumption and to maintain the quality of effluent in compliance with the water-law permit. Since the optimization is nonlinear and includes variations of different types of variables, to solve the given problem the authors performed simulation tests and decided to implement a hybrid of two different optimization algorithms: artificial bee colony (ABC) and direct search algorithm (DSA). Simulation tests for the wastewater treatment plant case study are presented.

A Hierarchical Smart Home Control System for Improving Load Shedding and Energy Consumption: Design and Implementation

This paper presents a hierarchical smart home control system, which controls the residential appliances. In this system, there are a supervisory controller, a secondary controller, and some smart plugs. The supervisory controller is the power utility, which produces the instantaneous or multi-time electricity tariffs and the appropriate load shedding commands. The secondary controller, which is central controller of the smart home connected to a smart power meter, produces ON/OFF commands to control the residential appliances optimally and automatically, which are connected to the smart plugs. The smart plugs apply the central controller commands to the appliances. The central controller considers tariff signal and load shedding commands to reduce the power consumption and costs while maintaining the consumer satisfaction. In this control system, the optimum time for customers to use each appliance is considered. Then, the best answer to the optimization using the brute-force search algorithm is selected and implemented in compliance with all constraints. In addition, conservation voltage reduction is considered and implemented in this system. The simulation results illustrate the performance of the hierarchical control system. The whole system is developed and implemented in a pilot home and is introduced in this paper.