Adaptive Control Policy Research Articles

A decentralized multi-authority CP-ABE scheme from LWE

As a cryptographic primitive supporting finer and richer access control, attribute-based encryption can provide more efficient, concise, secure and highly adaptive access control policies for cloud data, which has attracted the attention of many researchers. However, in a standard ABE scheme, the key can only be generated and issued by a central authority. The single authorization center has a heavy load and is vulnerable to attack. Once the center is paralyzed, it will bring serious security consequences. Multi-authorization center attribute encryption (MA-ABE) allows multiple parties to play an authoritative role, which can solve this problem.In this paper, we propose a MA-ABE scheme, which combines a global ID model, a two-stage sampling technique on a lattice and a monotonous linear secret sharing schemes (M-LSSS) to achieve a static security against arbitrary collusion in the random oracle model, in which the access policy of the scheme is expressed by DNF formula. Technically, our scheme is an improvement on the work of Datta et al. (2021), through this improvement, we can get shorter ciphertext and smaller key size.

Overview of advances in ASDEX Upgrade plasma control to support critical physics research for ITER and beyond

The successful operation of fusion reactors requires plasma scenarios with good core confinement and acceptable first wall heat loads that are stable and robust to external perturbations. This poses both physical and technological challenges. One of the technologies that addresses these challenges is a complex feedback control system that supports advances in physical understanding and helps to ensure stable operating conditions. The operation of marginally stable plasmas often leads to off-normal events (such as disruptions) and feedback control can prevent these to some extent. This contribution gives an overview of the main results of the development and operation of the feedback control algorithms on ASDEX Upgrade (AUG). Fueling actuators, using a combination of gas valves and pellet injection, can simultaneously control neutral density of the divertor and the density of the plasma core above the Greenwald limit. Impurity injection is employed to control the position of the X-point radiator, allowing the creation of an ELM-suppressed H-mode with high radiation fraction. Heating actuators are used to control the plasma energy content, which supports advanced tokamak experiments and enables stable I-mode operation, and the electron temperature control, which supports turbulence studies. In control technology, AUG has pioneered the use of virtual actuators, which allow effective use of the limited number of heating actuators, adaptive control policies, and exception handling. Such technologies will also be used in ITER. Advanced nonlinear state observers (RAPTOR, RAPDENS) and codes to evaluate the power deposition properties (RABBIT, TORBEAM) are available for routine use in the AUG feedback controllers. Extensive use of the AUG discharge control system further enhances the research capabilities of this machine.

Adaptive supervisory control for automated manufacturing systems using borrowed-buffer slots

Robust deadlock supervisory control techniques for automated manufacturing systems under resource failures that need additional central buffers may lead to supererogatory cost in control implementation. To mitigate this issue, this paper reports a Petri net-based low-cost adaptive supervisory control policy that does not require extra buffers. If an unreliable resource fails, three classes of buffer spaces can be temporarily used to store parts that require the failed resource in their impending processing routes. The proposed adaptive supervisory control comprises of control places and switch controllers. If an unreliable resource fails, the switch controllers are activated to move the part types that require the failed resource in their subsequent processing stages into borrowed buffer spaces. After the failed resource is recovered, the system returns to its normal operating mode. The part types in the borrowed buffer spaces will be returned to their last processing stages before the occurrence of the failure and continued along their processing route. We propose criteria for selecting buffer space borrowers and buffer space lenders. Furthermore, redundant control places are removed by a new technique proposed in the paper in order to reduce the complexity of the supervisory control structure. We demonstrate the proposed method using examples.

N-Level Hierarchy-Based Optimal Control to Develop Therapeutic Strategies for Ecological Evolutionary Dynamics Systems.

This article mainly proposes an evolutionary algorithm and its first application to develop therapeutic strategies for ecological evolutionary dynamics systems (EEDS), obtaining the balance between tumor cells and immune cells by rationally arranging chemotherapeutic drugs and immune drugs. First, an EEDS nonlinear kinetic model is constructed to describe the relationship between tumor cells, immune cells, dose, and drug concentration. Second, the N-level hierarchy optimization (NLHO) algorithm is designed and compared with five algorithms on 20 benchmark functions, which proves the feasibility and effectiveness of NLHO. Finally, we apply NLHO into EEDS to give a dynamic adaptive optimal control policy and develop therapeutic strategies to reduce tumor cells, while minimizing the harm of chemotherapy drugs and immune drugs to the human body. The experimental results prove the validity of the research method.

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.

Non-optimal and optimal fractional control analysis of measles using real data

This study employs fractional, non-optimal, and optimal control techniques to analyze measles transmission dynamics using real-world data. Thus, we develop a fractional-order compartmental model capturing measles transmission dynamics. We then formulate an optimal control problem to minimize the disease burden while considering constraints such as vaccination resources and intervention costs. The proposed model’s positivity, boundedness, measles reproduction number, and stability are obtained. The sensitivity analysis using the partial rank correlation coefficient is shown for the fractional orders of 0.99 and 0.90. It is noticed that the rate of recruitment into the susceptible population (π), the rate at which individuals in the latent class become asymptomatic (α1), and the transmission rate (β) contribute positively to the spread of the disease, while the rate at which individuals in the asymptomatic class become symptomatic (α2), the vaccination rate for the first measles dose (γ1), and the rate at which individuals in the latent class recover from measles (δ1) contribute significantly to the reduction of measles in the community. Utilizing numerical simulations and sensitivity analyses, we identify optimal control strategies that balance the trade-offs between intervention efficacy, resource allocation, and societal costs. Our findings provide insights into the effectiveness of fractional optimal control strategies in mitigating measles outbreaks and contribute to developing more robust and adaptive disease control policies in real-world scenarios.

АНАЛИЗ АДАПТИВНЫХ СИСТЕМ УПРАВЛЕНИЯ ДОРОЖНЫМ ДВИЖЕНИЕМ

This paper describes state-of-the-art adaptive control systems both in Split Cycle Offset Optimization (SCOOT), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Adaptive Control System-Lite (ACS-Lite), and worldwide. Urban road conditions and traffic signal timing to accommodate traffic volume changes and minimize stops and delays are controlled by adaptive traffic control systems on roadways. Based on the research objectives and comparing the positive aspects of past experiences will help traffic engineers and decision makers to select the most effective adaptive control system.

Adaptive green split optimization for traffic control with low penetration rate trajectory data

Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.

Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World

Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World

An adaptive product changeover policy for a capacitated two-product supply chain in a non-stationary demand environment

ABSTRACT The present paper investigates the effectiveness, in terms of fill rate, of a new adaptive production control policy for a two-product, two-echelon (i.e. factory and retailer) supply chain subject to a non-stationary customer demand. To model the capacity constraints more realistically, the factory node consists of an unbuffered production line subject to failures, and non-negligible changeover times. To cope with the disruptive events, we propose a new production control strategy named Adaptive Hedging Corridor Policy. A simulation model based on discrete-time difference equations is developed, and a two-step experimental campaign is conducted to compare the proposed control policy with two well-known alternatives proposed in the literature. The analysis of the results demonstrates the effectiveness of the new adaptive strategy in maximising the fill rate of supply chains subject to a non-stationary demand.

Optimal non-autonomous area coverage control with adaptive reinforcement learning

Area coverage control with multi-agent systems is formulated here in the framework of Bellman’s optimality. In the literature, optimal configurations are obtained by Lloyd’s algorithm, which iteratively converges the agents to centroidal Voronoi configurations that correspond to local maxima of a coverage metric, where metric sub-domains are assigned to each agent according to Voronoi tessellations. In this work, optimal tracking is achieved by an adaptive control policy using an actor–critic neural network-based reinforcement learning technique, which rewards actions that decrease a Lyapunov function based on the area coverage metric to drive the error dynamics to zero, and uses a feed-forward neural network to approximate the tracking control signal. The implementation of the area-coverage control is model-free in the sense that it relies on neural networks that interpolate local data gathered and shared by each agent, without relying on a global model of the system. We prove that optimality is achieved when the agents converge to Voronoi centroids, therefore maximizing the coverage metric, with the important implication that the obtained class of solutions is consistent with the ones obtained with Lloyd’s algorithm and its extension to non-autonomous systems.

Online adaptive optimal tracking control for model-free nonlinear systems via a dynamic neural network

This paper presents an online adaptive approximate solution for the optimal tracking control problem of model-free nonlinear systems. Firstly, a dynamic neural network identifier with properly designed weights updating laws is developed to identify the unknown dynamics. Then an adaptive optimal tracking control policy consisting of two terms is proposed, i.e. a steady-state control term is established to ensure the desired tracking performance at the steady state, and an optimal control term is proposed to ensure the optimal tracking error dynamics optimally. The composite Lyapunov method is used to analyse the stability of the closed-loop system. Two simulation examples are presented to demonstrate the effectiveness of the proposed method.

A reliability-oriented integration model of production control, adaptive quality control policy and maintenance planning for continuous flow processes

Many studies reveal that integrating the concepts of production, maintenance and quality control helps reduce costs compared to studying them separately. But almost all the research works in this field are centred on piece part manufacturing. This paper presents an integrated model of the three concepts for the continuous flow process of manufacturing which is vastly different from piece-parts processes, as there is no explicit production unit. To quickly detect the assignable cause occurrence, an adaptive X̄ control chart with variable sampling interval designed for the continuous flow process is employed as the quality control tool. A mathematical definition is presented to describe the proposed model before a genetic algorithm is developed to find the optimal setting values of decision parameters that minimize the expected total cost per unit time. The numerical comparison indicates that the proposed model has reduced the cost by an average of 1.5% than the classical model with fixed sampling interval policy in continuous flow manufacturing. Finally, Taguchi’s design of experiments (DOE) is employed to investigate the main effects of cost and process parameters.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

A Multiagent Deep Reinforcement Learning-Enabled Dual-Branch Damping Controller for Multimode Oscillation

This study develops a multiagent deep reinforcement learning (MADRL)-enabled framework for the decentralized cooperative control of a novel dual-branch (DB) damping controller for both low-frequency oscillation (LFO) and ultralow-frequency oscillation (ULFO). It has two branches, each of which consists of a proportional resonance (PR) and a second-order polynomial that is designed to handle target oscillation modes. To improve the robustness of the controller to system uncertainties, MADRL is developed, where multiagents are centrally trained to obtain the coordinated adaptive control policy while being executed in a decentralized manner to provide the optimal parameter setting for each controller with only local states. Comparisons with the IEEE 10-machine 39-bus system demonstrate that the proposed method achieves better robustness to uncertainties, lower communication delay, and single-point failure, as well as damping control performances for both LFO and ULFO.

Adaptive quantitative control for finite time synchronization among multiplex switched nonlinear coupling complex networks

Synchronization is an essential factor of multiplex dynamic networks. We study synchronization among multiplex delay networks with random switching nonlinear coupling under random disturbances. Different from the existing research works, this paper focuses on multiplex delay nonlinear coupling networks with Bernoulli random switching topologies. To overcome the time-varying dynamic characteristics of the multiplex networks and describe the limitation in signal transmission process, an adaptive delay-dependent quantitative tracking control policy is presented. To achieve synchronization among the multiplex switched delay networks in a limited time, finite time control technology is introduced into the design of the adaptive quantitative controller in the policy. The theoretical framework about the stochastic synchronization among the multiplex switched nonlinear coupling networks is derived. We find that the setting time is related to quantization level, parameters of controllers and Lyapunov functions. Finally, an example is presented to show the effective for the theoretical framework.

Designing a resilient supply chain through a robust adaptive model predictive control policy under perishable goods and uncertain forecast information

We consider the inventory control problem for Supply Chains (SC) with deteriorating items and an uncertain future customer demand freely varying inside a given compact set. The problem is to define a resilient Replenishment Policy (RP) keeping the actual inventory level as close as possible to a desired reference trajectory. This requirement should be satisfied despite uncertainties on the decay factor of stocked goods and unexpected customer demand patterns outside the bounds of the compact set. We propose a method based on a Resilient Robust Model Predictive Control (RRMPC) approach. This requires dealing with a Min-Max Constrained Optimization Problem (MMCOP). To reduce the numerical complexity of the algorithm, the control signal is parametrized using B-spline functions.

Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing

Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such as reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings — computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.

Appointed-time guaranteed adaptive fault-tolerant attitude tracking for quadrotors with aperiodic data updating

In this paper, an appointed-time guaranteed adaptive fault-tolerant attitude tracking for quadrotors with aperiodic data updating is concerned, where inertial moment variation, extraneous disturbance and unknown actuator faults are simultaneously involved. An adaptive parameter estimation law is updated to identify the partial effectiveness loss of actuation and alleviate the estimation burden of suggested observer. Then through filtering manipulations upon aperiodic system states, a novel event-triggered unknown system dynamics estimator (ETUSDE) is developed to learn the remaining uncertainties with exponential error decaying and decreased updating cost, where an event-triggering criterion is incorporated to schedule the transmission instants for control behavior and angular rate, and the explicit formulation between estimation error upper boundary and design coefficients is derived. An appointed-time prescribed performance control (APPC) is introduced to allow for pregiven attitude behaviors. By fusion of adaptive law, ETUSDE and APPC, an adaptive event-triggered attitude control policy is synthesized that delivers appointed-time convergence, accurate steady response and decreased sampling burden. Meanwhile, Lyapunov analysis shows that all the error variables contained in the closed-loop system are ultimately uniformly bounded. Eventually, experiments on a quadrotor attitude test rig are conducted to confirm the superiorities of suggested control strategy.

How to effectively control vertical collusion in bidding for government investment projects-Based on fsQCA method.

The impact of collusion during the bidding processes of Chinese government investment projects is a major concern in academic and policy circles, as collusion breeds corruption and destroys the credibility of governments. Furthermore, it negatively impacts successful project completion, leading to cost overruns and the illegitimate enrichment of colluding agents, regardless of the intended social benefits. Using data from 166 selected regional policy implementations as the research sample, this paper uses the fuzzy set qualitative comparative analysis method to conduct a group analysis of typical cases. The purpose of this study is to identify and better understand the cooperative regional policy implementation environments in China and to identify effective methods to improve the governance quality of collusion controls in construction investment project bidding processes. Five key control paths are identified, covering 94% of the cases. It is also found that in lower social collusion situations, reasonable market competition regulations can directly reduce collusive behavior. The research results will help the government to formulate more adaptive control policies and promote high-quality development of government investment projects.