Set Of Control Rules Research Articles

A Risk Assessment Framework Based on Fuzzy Logic for Automotive Systems

Recent advancements in the automotive field have significantly increased the level of complexity and connectivity of modern vehicles. In this context, the topic of cybersecurity becomes extremely relevant, as a successful attack can have an impact in terms of safety on the car navigation, potentially leading to harmful behavior. Risk assessment is typically performed using discrete input and output scales, which can often lead to an identical output in terms of risk evaluation despite the inputs presenting non-negligible differences. This work presents a novel fuzzy-logic-based methodology to assess cybersecurity risks which takes attack feasibility and safety impact as input factors. This technique allows us explicitly model the uncertainty and ambiguousness of input data, which is typical of the risk assessment process, providing an output on a more detailed scale. The fuzzy inference engine is based on a set of control rules expressed in natural language, which is crucial to maintaining the interpretability and traceability of the risk calculation. The proposed framework was applied to a case study extracted from ISO/SAE 21434. The obtained results are in line with the traditional methodology, with the added benefit of also providing the scatter around the calculated value, indicating the risk trend. The proposed method is general and can be applied in the industry independently from the specific case study.

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems.

Black-box artificial intelligence (AI) induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represented using a black-box AI are capable of efficiently executing the underlying control task and achieving optimal closed-loop performance-controlling the agent from the initial time step until the successful termination of an episode, the developed control rules are often complex and neither interpretable nor explainable. In this article, we use a recently proposed nonlinear decision-tree (NLDT) approach to find a hierarchical set of control rules in an attempt to maximize the open-loop performance for approximating and explaining the pretrained black-box DRL (oracle) agent using the labeled state-action dataset. Recent advances in nonlinear optimization approaches using evolutionary computation facilitate finding a hierarchical set of nonlinear control rules as a function of state variables using a computationally fast bilevel optimization procedure at each node of the proposed NLDT. In addition, we propose a reoptimization procedure for enhancing the closed-loop performance of an already derived NLDT. We evaluate our proposed methodologies (open- and closed-loop NLDTs) on different control problems having multiple discrete actions. In all these problems, our proposed approach is able to find relatively simple and interpretable rules involving one to four nonlinear terms per rule, while simultaneously achieving on par closed-loop performance when compared to a trained black-box DRL agent. A postprocessing approach for simplifying the NLDT is also suggested. The obtained results are inspiring as they suggest the replacement of complicated black-box DRL policies involving thousands of parameters (making them noninterpretable) with relatively simple interpretable policies. The results are encouraging and motivating to pursue further applications of proposed approach in solving more complex control tasks.

Fuzzy-IoT smart irrigation system for precision scheduling and monitoring

Adopting and developing sustainable precision irrigation systems allows for improving productivity, reducing water and energy losses in agricultural fields.This paper deals with the development and real-time implementation of a smart precision irrigation system (SPIS). The proposed system combines two irrigation approaches, processed by a feedback fuzzy logic (FL) controller, and long-range data transmission and monitoring via the LoRa protocol. The combined approaches not only enhance overall performance but also address several limitations commonly associated with existing approaches. The proposed system uses the collected data from the sensors unit to provide the optimal irrigation decisionand communicates it with the operator (farmer) via a developed IoT platform. Data from soil moisture, ambient temperature, solar irradiance, and rainfall sensors are processed through the FL controller to adjust the irrigation times. The inputs and outputs are fuzzified by trapezoidal and triangular membership functions. Mamdani's fuzzy inference is used to control the system through a set of linguistic control rules for a more accurate decision for each scenario. The field experimentation of the developed SPIS in the eastern region of Morocco shows high efficiency and reliability by saving water and energy compared to traditional irrigation control. The proposed system, with remote data monitoring, promotes better management of irrigation, water, and energy consumption, and it is suitable to be mounted with any type of irrigation installation in the field, without extensive modifications.

Sustainable management of a new fishing stock: Bioeconomic approach under biological and market uncertainty

Potential and emerging fisheries create challenges and opportunities for fishery managers who need to decide how to sustainably manage a fishery that does not yet exist. A new Pacific hake stock off the west coast of Mexico has been identified. The Mexican government is interested in assessing the feasibility of a new commercial fishery. This work proposes and analyzes alternative potential fishery management measures for this possible new fishery under biological and market uncertainties. Results indicate that a new fishery could be biologically sustainable and economically profitable under a set of management strategies and control rules. A limited access strategy with low effort is recommended because it is most profitable per vessel and biologically cautious, considering the high uncertainty associated with the exploitation of an unfished stock. Despite the combination of high operating costs and low prices the fishery could still be profitable in the long-term, although there is risk of overexploitation if high fishing effort is allowed. Nevertheless, our results suggest low risk of fishing down the dwarf hake stock if the fishery is managed under low effort levels, allowing for sufficient opportunities for data collection and adaptive management. This work addresses the rare opportunity of assessing a fishery previous to its operation, with all the associated data limitations and uncertainty.

A nearly throughput-maximum knotted intersection design and control for connected and automated vehicles

Traffic at urban intersections constitute a series of complex conflicting vehicular movements and contribute greatly to the problems of traffic congestion and air pollution. The emerging of connected and automated vehicle (CAV) technologies inspires innovative ideas in traffic management at intersections; it not only enables new control paradigms, but also allows for possibilities to revolutionize the geometric layouts of intersections. This study proposes a novel intersection design called knotted intersection (KI), to resolve the complexity of conflicting relations at intersections in a full CAV environment. This new design is associated with a set of neat control rules for vehicles to realize the smooth traffic operation at these intersections. The design problem of intersection geometry and control strategy is optimized jointly and formulated as a mixed-integer linear program (MILP), and some basic theoretical properties of KI design are derived, including nearly throughput-maximizing property and bounded within-intersection delay of vehicles. Furthermore, the KI design is then extended to a road network composed of multiple intersections to improve the overall efficiency. The optimal design of multiple knotted intersections (MKI) is developed to coordinate the traffic organization methods among intersections and a two-level solution method is proposed to solve the optimization efficiently. Numerical and simulation experiments are conducted at both the intersection level and network level under various cases, and the results validate the superior performance of the proposed methods compared to classical traffic signal control (TSC) strategies.

Fusing domain knowledge and reinforcement learning for home integrated demand response online optimization

Electricity–gas integrated household energy systems (HESs) expose obvious system uncertainties, requiring their integrated demand response (IDR) programs to be able to adapt automatically and quickly to system changes. Deep Reinforcement Learning (DRL) methods, though having been proven promising to tackle such problems, are typically not efficient in learning from random explorations. This paper proposes a method based on DRL with HESIDR knowledge penetration. By interpreting the domain IDR knowledge as a set of control rules, the DRL agent gains learning samples from knowledge-based exploration in addition to the traditional exploration–exploitation tradeoff. Correspondingly, we develop a cooperation scheme for action selection and replay sampling, which is based on exponential probability functions to balance the penetration of knowledge-based exploration, random exploration and policy exploitation. We conduct case studies in a typical home multi-energy system environment. After determining parameters in the exponential probability functions, the learning and cost reduction performance of the proposed algorithm was tested. The results show that the method proposed in the present study spends 48.78% less training time than the standard DQN, which enables few-minute optimization on a lightweight PC. Also, the proposed method can further reduce energy bills by 26.17% compared to the uncontrolled scenario and by 9.88% to the integrated rule-based controller.

A Privacy-Preserving System Design for Digital Presence Protection

A person’s privacy has become a growing concern, given the nature of an expansive reliance on real-time video activities with video capture, stream, and storage. This paper presents an innovative system design based on a privacy-preserving model. The proposed system design is implemented by employing an enhanced capability that overcomes today’s single parameter-based access control protection mechanism for digital privacy preservation. The enhanced capability combines multiple access control parameters: facial expression, resource, environment, location, and time. The proposed system design demonstrated that a person’s facial expressions combined with a set of access control rules can achieve a person’s privacy-preserving preferences. The findings resulted in different facial expressions successfully triggering a person’s face to be blurred and a person’s privacy when using a real-time video conferencing service captured from a webcam or virtual webcam. A comparison analysis of capabilities between existing designs and the proposed system design shows enhancement of the capabilities of the proposed system. A series of experiments exercising the enhanced, real-time multi-parameter-based system was shown as a viable path forward for preserving a person’s privacy while using a webcam or virtual webcam to capture, stream, and store videos.

Herding stochastic autonomous agents via local control rules and online target selection strategies

We propose a simple yet effective set of local control rules to make a small group of “herder agents” collect and contain in a desired region a large ensemble of non-cooperative, non-flocking stochastic “target agents” in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. The effectiveness of the proposed approach is confirmed in both simulations in ROS and experiments on real robots.

Harvest control rules of pelagic fisheries in the Bali Strait, Indonesia

Abstract. Harlyan LI, Badriyah L, Rahman MA, Sutjipto DO, Sari WK. 2021. Harvest control rules of pelagic fisheries in the Bali Strait, Indonesia. Biodiversitas 23: 947-953. Multispecies pelagic fisheries have contributed as the main fishery production for the Bali Strait fisheries. To maintain the sustainability of the fish stock, sets of harvest control rules (HCRs) have been introduced in terms of fishery management. Conventional single-species surplus production models have been used extensively to determine the future annual allowable biological catch (ABC). However, they may not technically apply to multispecies fishery management. The feedback harvest control rule HCR has been validated and applied to maintain the catch variability of the multispecies fishery. This study aims to technically compare the use of the surplus production model and the feedback HCR in the ABC estimation. The catch and effort data series of the three dominant species from 2010-2019, collected from Pengambengan and Muncar fishing port, were analyzed by two HCRs, the Schaefer surplus production model and the feedback HCR to obtain the ABC. As a result, the Schaefer model showed a higher estimation of ABC than the feedback HCR because the feedback HCR may reflect the recent historical catch consistent with precautionary principles. The feedback HCR showed an initial step toward sustainably managing multispecies fisheries while dealing with unavailability in species-specific data conditions.

Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

A novel cascade approach to control variables optimisation for advanced series-parallel hybrid electric vehicle power-train

In recent years, road vehicles are being increasingly equipped with hybrid electric power-trains in order to provide significant gains in fuel economy and reductions in greenhouse gases emissions. Since hybrid power-trains consist of two or more different energy sources, many of their variants are present nowadays which leads to many open questions in terms of hybrid electric power-train structure selection, components sizing and energy management control, which all have influence on the power-train purchase cost and efficiency. The control variables optimisation is crucial in order to find the set of optimal control rules for different power-train operating regimes which would yield the minimum possible fuel consumption. Among different control variable optimisation methods, the dynamic programming approach is usually used in literature, because of its unique feature to find the global optimum solution with a certain degree of precision. However, this optimisation method also requires significant computing power and its application is limited to low-order systems. Having this in mind, this paper evaluates the benefits of innovative cascade approach to hybrid electric vehicle control variable optimisation wherein dynamic programming is combined with a gradient-based optimisation algorithm in a systematic and a straightforward way in order to significantly reduce the optimisation execution time and also to increase the precision of the globally-optimal result.

Incorporating environmental forcing in developing ecosystem-based fisheries management strategies

AbstractThis study incorporated two pathways of environmental forcing (i.e. “larval mortality forcing” and “somatic growth forcing”) into an end-to-end ecosystem model (Object-oriented Simulator of Marine ecOSystEms, OSMOSE) developed for the Pacific North Coast Integrated Management Area (PNCIMA) off western Canada, in order to evaluate alternative fisheries management strategies under environmental changes. With a suite of ecosystem-level indicators, the present study first compared the ecosystem effects of different pathways of environmental forcing scenarios; and then evaluated the alternative fisheries management strategies which encompassed a series of fishing mortality rates relative to FMSY (the fishing mortality rate that produces maximum sustainable yield) and a set of precautionary harvest control rules (HCRs). The main objectives of this study were to (i) explore the ecosystem effects of different environmental forcing scenarios; (ii) identify the impacts of different fishing mortality rates on marine ecosystem structure and function; and (iii) evaluate the ecosystem-level performance of various levels of precautionary HCRs. Results indicated that different pathways of environmental forcing had different ecosystem effects and incorporating appropriate HCRs in the fisheries management process could help maintain ecosystem health and sustainable fisheries. This study provides important information on future fisheries management options within similar marine ecosystems that are facing global changes.

Learning versus optimal intervention in random Boolean networks

Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ‘natural’ steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.

ITrust: identity and trust based access control model for healthcare system security

The patient and healthcare professionals use the Electronic Healthcare System (EHS) for accessing medical records from the remote locations via the Internet. The emerging healthcare system has several advantages such as better management of the healthcare data, streamlined collaboration, improvement of medical care, insurance purpose, medical data backup, etc. Regardless of its advantages, the sensitivity and openness nature of the healthcare system arises different type of attacks and threats such as insider attack, service hijacking, abuse use of healthcare data, and impersonation attack. In the EHS, without knowing the prior information of the requester, data sharing is another considerable issue. Hence, a dynamic Access Control Model (ACM) is needed to overcome the above-discussed issues. In the EHS, the addition of trust into the access control solutions can provide dynamic access to the resources. To achieve such a model, in this paper, we have added user trust into the Identity Based Access Control (IBAC) model. For the computation of user trust, we have used beta reputation approach. An access control rule set has been proposed based on the trust degree and identity of the user to provide access in a controlled manner. This hybrid ACM and rule set not only protect the data from unauthorized access but also dynamically control the access view of the healthcare data. The experimental result of the proposed model shows that it is more accurate and reliable as compared to other trust models.

An insect-inspired model for acquiring views for homing.

Wasps and bees perform learning flights when leaving their nest or food locations for the first time during which they acquire visual information that enables them to return successfully. Here we present and test a set of simple control rules underlying the execution of learning flights that closely mimic those performed by ground-nesting wasps. In the simplest model, we assume that the angle between flight direction and the nest direction as seen from the position of the insect is constant and only flips sign when pivoting direction around the nest is changed, resulting in a concatenation of piecewise defined logarithmic spirals. We then added characteristic properties of real learning flights, such as head saccades and the condition that the nest entrance within the visual field is kept nearly constant to describe the development of a learning flight in a head-centered frame of reference, assuming that the retinal position of the nest is known. We finally implemented a closed-loop simulation of learning flights based on a small set of visual control rules. The visual input for this model are rendered views generated from 3D reconstructions of natural wasp nesting sites, and the retinal nest position is controlled by means of simple template-based tracking. We show that naturalistic paths can be generated without knowledge of the absolute distance to the nest or of the flight speed. We demonstrate in addition that nest-tagged views recorded during such simulated learning flights are sufficient for a homing agent to pinpoint the goal, by identifying nest direction when encountering familiar views. We discuss how the information acquired during learning flights close to the nest can be integrated with long-range homing models.

What is multispecies MSY? A worked example from the North Sea.

The concept of an optimum yield at intermediate levels of fishing (the so called maximum sustainable yield or MSY) has been with us since the 1930s and is now enshrined in legislation as a key objective of fisheries management. The concept seems intuitively reasonable and is readily applicable to a single stock treated in isolation and assuming a constant environment. However, translating this concept into a mixed and multispecies fishery, where there are complex trade‐offs between fleets and stocks and in general no simple optimum solution, has been problematic. Here I introduce a framework for thinking about multispecies MSY in terms of an integrated risk of stock depletion and expected long‐term yield. Within this framework I consider the performance of a set of simple harvest control rules based upon a single‐limit fishing mortality rate (F) which is common to all stocks and a target biomass which is a set fraction of a stock's virgin biomass. Using a multispecies management strategy evaluation, I compare expected outcomes for a set of these harvest control rules with alternative scenarios, in which each stock has its own F based on the assessment process. I find that the simple framework can produce outcomes that are similar to those from the more sophisticated estimates of F. I therefore conclude that achieving multispecies MSY may depend more upon setting reasonable biomass targets and faithfully applying a harvest control rule approach rather than determining the best possible Fs for each stock.

Speed Control of DC Motor using Fuzzy Logic Controller by PCI 6221 with MATLAB

This thesis demonstrates the importance of Fuzzy Logic Controller. The operation of a DC motor is performed using Fuzzy Logic Controller (FLC) in MATLAB environment. Fuzzy Logic is one of the most successful applications of fuzzy set in which the variables are linguistic rather than numeric. A Fuzzy Logic Controller (FLC) is based on a set of control rules (fuzzy rules) among linguistic variables. The proposed fuzzy controller results in a better response compared to the normal response of DC motor. This thesis consists of two parts; software and hardware implementation. The software part aims to design and develop a Fuzzy Logic Controller in MATLAB Simulink. The hardware Part Consist of DC motor Driver and PCI 6221. The DC drive is used to convert AC voltage into variable DC voltage PCI 6221 is used as the hardware interface between Hardware and Software.

Self-organizing control for satellite clusters using artificial potential function in terms of relative orbital elements

Self-organizing control for satellite clusters is a challenging and promising problem which has drawn considerable attention in the recent past. The artificial potential function method has been widely used for self-organizing control due to its elegant mathematical analysis and simplicity. This paper proposes a set of self-organizing control rules for satellite clusters described by artificial potential functions, so that the reconfiguration, uniform distribution and collision avoidance operations can be achieved spontaneously. It may work regardless of the failure of satellites, attendance of new members or existence of space debris, and the corresponding communication and control system are completely distributed. Particularly, the artificial potential functions are written in terms of relative orbital elements derived from T–H equations, so that the self-organizing control can reflect relative motion dynamics, and guide the satellite along a fuel-efficient trajectory. The proposed method is especially suitable for highly distributed micro-satellite clusters and large-scale clusters to track moving targets, with few fuel cost and relatively high control accuracy, and it is applicable to clusters in either deep space or near-earth space. The stability of the control was proved by Lyapunov second method, and verified by Monte Carlo simulation. Finally, the comparison between self-organizing control and fuel-optimal control was made to demonstrate the performance properties.

Rule Optimization of Self-organization Control of Traffic Signals in Urban Net Based on Hydrodynamic

Prior researchers indicate that hydrodynamics models of traffic-flow is lack of description of changing mechanism under urban traffic, and self-organization control system can not explain the dynamic characteristics of urban traffic flow clearly. The aim of the paper is to puts forward an optimized method on control rules that make the united application of hydrodynamics and self-organization system in signal control. The parameter sets of control rules are built from parameter sets of road network which are evolution under hydrodynamic equations such as the length of each lane, phase, queue length and so on .With the aim of the maximum traffic volume at each intersection in the road network, the control rules optimize its parameter sets to adapt to the dynamic change. By means of the computer simulation, the application of signal self-organizing control under hydrodynamic is proved effective in urban traffic.a

How to provide scientific advice for ecosystem‐based management now

AbstractIn this paper, I argue that we have at hand what is needed to provide scientific advice for ecosystem‐based management of small pelagics and other species groups now. The ingredients for this advice are (i) large marine ecosystems as spatial management units; (ii) maintaining ecosystem productivity and exploiting at multispecies maximum yield as overarching management objectives; (iii) assessment of ecosystems by evaluating changes in primary productivity; (iv) an operational management procedure in which single‐species catch proposals are adjusted to ecosystem productivity using a set of control rules. Inspection of historic landings for small pelagics and other small species in the Northeast Atlantic (ICES area) reveals that most likely fisheries exploitation does not, and never did, exceed system productivity in most LMEs and is therefore overall sustainable, although not necessarily for individual stocks.