Logical Control Networks Research Articles

Utilizing Fuzzy Logic Control and Neural Networks Based on Artificial Intelligence Techniques to Improve Power Quality in Doubly Fed Induction Generator‐Based Wind Turbine System

This article presents novel artificial intelligence (AI)‐based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy hysteresis‐direct power control (FH‐DPC) and neural hysteresis‐DPC (NH‐DPC), respectively. The primary purpose is to overcome conventional DPC (C‐DPC) limitations in doubly fed induction generator wind turbines (WT‐DFIG), focusing on power quality improvement and enhanced system efficiency. The techniques aim to reduce power ripples and improve the quality of alternating current (AC) grid energy by improving current signal quality in all WTs’ operation modes with WT‐DFIG and all compensation power modes. The suggested techniques are thoroughly examined using the MATLAB/Simulink environment under various wind scenarios, demonstrating a reduction in active power ripples by over 70%, a reduction in reactive power ripples of around 77% on average, a decrease in generated current total harmonic distortions (THDs) by over 70% compared to C‐DPC. The performances of FH‐DPC and NH‐DPC are contrasted with C‐DPC and other previously suggested methods, and it is concluded that the proposed control approaches perform more effectively than them regarding ripples in local reactive compensation and generated active powers as well as THD currents, with FH‐DPC slightly outperforming NH‐DPC. The research indicates that AI can enhance the effectiveness and quality of power generated by wind‐power systems.

Detectability of mix-valued logical control networks based on a Luenberger-like observer approach

This research investigates detectability of a class of mix-valued logical control networks through the application of a Luenberger-like observer. First, three concepts of detectability: detectability, strong detectability, and weak detectability are defined for the mix-valued logical control networks. Subsequently, the equivalent algebraic form of the control networks is obtained to deal with the three kinds of detectability. A Luenberger-like observer for the equivalent algebraic representation is designed to tackle the challenges. Then some necessary and sufficient conditions for detectability are derived by employing the observer associated with detecting the states of the control networks. Finally, two examples are presented to effectively demonstrate our obtained conclusions in both theory and applications. Moreover it is shown that our proposed observer scheme has good performance.

Evaluation of advanced control strategies for PMSG in wind turbines: backstepping fuzzy logic control and adaptive neural network control approaches

This study presents a comparative analysis of two advanced control strategies for Permanent Magnet Synchronous Generators (PMSGs) in wind turbines: Backstepping Fuzzy Logic Control and Adaptive Neural Network Control. The aim is to evaluate their effectiveness in handling structured and unstructured uncertainties and enhancing system performance. Backstepping Fuzzy Logic Control integrates backstepping techniques with fuzzy logic regulation to improve flexibility and robustness. This method addresses the limitations of traditional PI controllers by enhancing adaptability to parameter variations and external disturbances. In contrast, Adaptive Neural Network Control employs neural networks with dynamic compensators to address high uncertainties and nonlinearities, aiming to surpass the performance of conventional PI controllers. The study involves simulations to compare these control strategies based on several criteria: robustness in managing parameter changes and external disturbances, flexibility in adapting to dynamic conditions, and overall performance including efficiency and stability. Simulation results indicate that both strategies offer significant improvements over traditional PI controllers. Backstepping Fuzzy Logic Control demonstrates strong adaptability and robustness, while Adaptive Neural Network Control shows superior efficiency and optimality, particularly in high-uncertainty environments. This comparative analysis provides insights into the strengths and limitations of each approach, offering guidance for selecting the most suitable control strategy based on specific operational requirements. The findings contribute to advancing control methodologies in wind energy systems and suggest directions for future research and practical implementation.

On Controllability and Stabilization of Switched Logical Control Networks With Switching-Input-Triggered Restricted Successors

On Controllability and Stabilization of Switched Logical Control Networks With Switching-Input-Triggered Restricted Successors

Necessary and Sufficient Conditions for Event-Triggered Set Stabilizability of Markovian Jump Logical Networks.

This technical paper utilizes the Lyapunov theory to characterize the event-triggered set stabilizability of Markovian jump logical control networks (MJLCNs). Whereas the existing result for checking the set stabilizability of MJLCNs is only sufficient, this technical paper further establishes its necessary and sufficient condition. First, the Lyapunov function is established to describe the set stabilizability of MJLCNs necessarily and sufficiently by combining recurrent switching modes and desired state set. Then, the triggering condition and the input updating mechanism are designed regarding the value change of the Lyapunov function. Finally, the effectiveness of theoretical results is demonstrated by a biological example concerning the lac operon in Escherichia coli.

Set stabilization of logical control networks: A minimum node control approach

In network systems, control using minimum nodes or pinning control can be effectively used for stabilization problems to cut down the cost of control. In this paper, we investigate the set stabilization problem of logical control networks. In particular, we study the set stabilization problem of probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs) via controlling minimal nodes. Firstly, an algorithm is given to search for the minimum index set of pinning nodes. Then, based on the analysis of its high computational complexity, we present optimized algorithms with lower computational complexity to ascertain the network control using minimum node sets. Moreover, some sufficient and necessary conditions are proposed to ensure the feasibility and effectiveness of the proposed algorithms. Furthermore, a theorem is presented for PBCNs to devise all state-feedback controllers corresponding to the set of pinning nodes. Finally, two models of gene regulatory networks are considered to show the efficacy of obtained results.

Transition Analysis of Stochastic Logical Control Networks

Transition analysis is essential for analyzing and regulating logical control networks (LCNs). The algebraic state-space representation method, which relies on the semi- tensor product of matrices, has been shown to allow deterministic LCNs to be represented as linear- like systems. However, due to the inherent uncertainty, it is hard to obtain the algebraic expression of a stochastic LCN. A unified paradigm for the transition analysis of LCNs with stochastic and deterministic dynamics is provided in this research. First, the algebraic expression of LCN with deterministic dynamics is reviewed. Second, the algebraic expression of LCN with stochastic dynamics is considered, where the non-equivalence between the dispersed form and the integrated form is proposed. Then the reason for the non-equivalence is provided. After that, a consistency condition is presented to bridge the gap between the independent model and the conditionally independent model. Lastly, we specifically point out that probabilistic LCN satisfies the consistency criteria, allowing one to calculate the probabilistic LCN transition matrix by using a power-reducing operator.

Artificial Intelligence Techniques based on aquaculture solar thermal water heating system control

The One of the most promising applications of hot water is aquaculture. Temperature is one of the most principle parameters affects aquaculture life. It can cause stress and mortality or superior environment for growth and reproduction. Shrimp is warm water aquaculture type need hot water supply. To increase pond production it is necessary to keep water temperature at 34oC. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper a control system using artificial neural network control (NNC) either adaptive fuzzy logic control (AFLC) are design to control the hot water temperature. A comparison study is applied between the performance of FLC and NNC. The performance of NNC is the best because the control design takes into consideration different variables which give an accurate output than FLC. Also this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the proposed aquaculture heating system.

A comprehensive exploration of IoT-enabled smart grid systems: power quality issues, solutions, and challenges

The potential for Internet of Things (IoT) technology to transform energy management has led to significant interest in its incorporation into smart grid systems. This review discusses the state of IoT-powered smart grids today, focusing on applications, current technology, and power quality (PQ) issues. Key problems including harmonics, transients, and voltage fluctuations are identified, and mitigation techniques using sophisticated filters and intelligent systems like fuzzy logic control (FLC) and artificial neural networks (ANN) are investigated. Concerns about interoperability and scalability are among the other challenges the review lists for IoT implementation. The revolutionary potential of IoT in improving smart grid efficiency and dependability is highlighted in our findings, which provide valuable insights for scholars and practitioners seeking to develop this sector.

ANFIS based sliding mode control of a DFIG wind turbine excited by an indirect matrix converter

<span lang="EN-US">Doubly fed induction generator (DFIG) is very popular in commercial turbines installed worldwide. Indirect matrix converter (IMC) is a fully silicon-based converter without capacitor that could be used in wind turbines. In this paper a robust ANFIS based second order sliding mode controller is proposed to control the DFIG wind turbine excited by IMC converter. ANFIS is a combination of fuzzy logic control and artificial neural networks. This hybrid combination could decrease the complexity of the wind turbine system. This scheme does not need the exact mathematical model of the system as needed in classical control methods. The effectiveness of the proposed controller is verified by simulation results compared to the classical PI controller.</span>

Safe control of logical control networks with random impulses

Under the framework of a hybrid-index model, this paper investigates safe control problems of state-dependent random impulsive logical control networks (RILCNs) on both finite and infinite horizons, respectively. By using the ξ-domain method and the constructed transition probability matrix, the necessary and sufficient conditions for the solvability of safe control problems have been established. Further, based on the technique of state-space partition, two algorithms are proposed to design feedback controllers such that RILCNs can achieve the goal of safe control. Finally, two examples are shared to demonstrate the main results.

Robust optimal control of logical control networks with function perturbation

Optimal control is an important branch in modern control theory. In this article, the robustness of optimal control sequence is investigated for logical control networks (LCNs) subject to function perturbation. Firstly, the impact of function perturbation on the Bolza-type optimal control problem of LCNs is analyzed, and several sufficient conditions are presented for the robustness of optimal control sequence. Secondly, combining the characteristics of state transition matrix and stage cost function, a kind of optimal matrix is constructed, which brings a new way for the solvability of Bolza-type optimal control problem. Thirdly, the optimal matrix is used to measure the deviation of cost function with respect to function perturbation, and several necessary and sufficient conditions are proposed to guarantee the robustness of optimal control sequence. Finally, the robustness of optimal control sequence in the Bolza-type optimal control problem is applied to other types of finite-horizon and infinite-horizon optimal control problems of LCNs.

SECURING CENTRALIZED SDN CONTROL WITH DISTRIBUTED BLOCKCHAIN TECHNOLOGY

Software Defined Networks (SDN) advocates segregation of network control logic, forwarding functions and management applications into different planes to achieve network programmability, automated and dynamic flow control in next generation networks. It promotes deployment of novel and augmented network management functions to have flexible, robust, scalable and cost-effective network deployments. All these features introduce new research challenges and require secure communication protocols among the segregated network planes. This manuscript focuses on the security issue of southbound interface which operates between the SDN control and data plane. We have highlighted the security threats associated with an unprotected southbound interface and the issues related with the existing TLS based security solution. A lightweight blockchain based decentralized security solution is proposed for southbound interface to secure the resources of logically centralized SDN controllers and distributed forwarding devices from opponents. The proposed mechanism can operate in multi-domain SDN deployment and can be used with wide range of network controllers and data plane devices. In addition to it, the proposed security solution is analyzed in terms of security features, communication and reauthentication overhead.

Fault Isolation of Logical Control Networks via Set Controllability of Augmented System

Fault isolation is an important problem in many control engineering applications, which focuses on monitoring systems, identifying when faults occur, and determining fault types and their locations. This brief aims to investigate the fault isolation of logical control networks (LCNs) via the set controllability method. A new augmented system is firstly introduced, which differs from the existing methods of constructing an augmented system because the dimension is much smaller. The reachability and set controllability of the augmented system are then investigated. By constructing the proper destination set and initial set, the equivalence between set controllability of the new augmented system and fault isolation of LCNs is revealed. Consequently, by resorting to the set controllability of the new augmented system, a criterion is established to verify the fault isolation of LCNs. An example is established to test the feasibility of the developed results.

Event-triggered set stabilization of impulsive logical control networks based on the hybrid index model approach

This paper proposes two kinds of event-triggered control (ETC) schemes to stabilize k-valued impulsive logical control networks (ILCNs) towards a given state set. It is worth pointing out that the impulsive process of traditional ILCNs is usually assumed to occur in a unit of time, which neglects continuous impulsive jumpings. In contrast, ILCNs here are redefined based on a 2-D index model to demonstrate the detailed impulsive process. In this ground, we establish the set stabilization criteria for ILCNs in both the hybrid domain and the time domain. It reveals that if an ILCN achieves set stabilization in the hybrid domain, it must achieve set stabilization in the time domain, but not vice versa. Then, two types of ETC, which obey the event-triggered inputting strategy and event-triggered updating strategy, are designed in the hybrid domain and the time domain. Finally, a biological example is presented to elaborate on the controller design procedures.

Survey of semi-tensor product method in robustness analysis on finite systems.

Recently, the theory of semi-tensor product (STP) method of matrices has received much attention from variety communities covering engineering, economics and industries, etc. This paper describes a detailed survey on some recent applications of the STP method in finite systems. First, some useful mathematical tools on the STP method are provided. Second, many recent developments about robustness analysis on the given finite systems are delineated, such as robust stable analysis of switched logical networks with time-delayed, robust set stabilization of Boolean control networks, event-triggered controller design for robust set stabilization of logical networks, stability analysis in distribution of probabilistic Boolean networks, and how to solve a disturbance decoupling problem by event triggered control for logical control networks. Finally, several research problems in future works are predicted.

Distributed pinning controllers design for set stabilization of $ k $-valued logical control networks

<abstract><p>Design of distributed pinning controllers for set stabilization of $ k $-valued logical control networks is investigated in this paper. Firstly, by analyzing the coupling correlations among nodes, pinned node set is determined. Secondly, based on the solvability of a class of matrix equations, sufficient conditions which resort to local information are put forward for the design of pinning controllers. Furthermore, an algorithm for designing pinning feedback controllers is presented, where the designed controllers are related to part of state variables instead of all variables. Finally, two illustrative examples are presented to verify the effectiveness of the main results.</p></abstract>

On robust control invariance and robust set stabilization of mix‐valued logical control networks

AbstractThis article investigates the robust control invariance and robust set stabilization problems based on a semi‐tensor product method for a class of mix‐valued logical control networks (MVLCNs) with disturbances. First, a calculation method for the largest robust control invariant subset contained in a given set is proposed. Second, based on the robust control invariant subset, the robust set stabilization of MVLCNs is discussed, and new results are presented. Furthermore, the design algorithm of time‐optimal state feedback stabilizers via antecedence solution technique is derived. The study of an illustrative example shows the effectiveness of the obtained new results.

Asymptotical feedback controllability of continuous-time probabilistic logic control networks

In this article, we study the controllability of continuous-time probabilistic logic control networks (CT-PLCNs) under sampled-data feedback controls (SDFCs). First, we demonstrate that the concept of finite-time controllability with probability one for discrete-time probabilistic logic control networks cannot be generalized to CT-PLCNs. Then, we propose the concepts of asymptotical feedback reachability and asymptotical feedback controllability for CT-PLCNs. Based on the invariant subsets, we prove that a target state is asymptotically feedback reachable if and only if the target state is a control equilibrium point and any initial state has an admissible path to the target state. Moreover, we introduce the concept of reachability matrix and propose an easily verifiable criterion for asymptotical feedback reachability expressed in terms of the reachability matrix. Based on these, we prove that a CT-PLCN is asymptotically feedback controllable if and only if every state is a control equilibrium point and there is an admissible path between any pair of initial and target states. The relation between controllability and stabilizability is also discussed. We prove that a CT-PLCN is asymptotically feedback controllable if and only if every state is asymptotically feedback stabilizable. For a controllable CT-PLCN, we propose an algorithm of designing a stabilizing SDFC for any given target state. Additionally, we discuss the asymptotical feedback controllability of CT-PLCNs under time-varying nonuniform SDFCs. Finally, an illustrative example is presented to explain the proposed methods and verify the controllability criteria.

Robust Set Controllability of Logical Control Networks: A Set Monotonicity Approach

Set controllability is a powerful tool to solve several issues of logical control networks (LCNs), including observability, set stabilization, output regulation, and synchronization. This article explores the robustness of set controllability for LCNs subject to function perturbation. Two perturbation sets, called decreasing reachable pair set and increasing reachable pair set, are constructed to characterize the change of set controllability index sets. Based on the perturbation sets, several criteria are established for the robustness of set controllability. As applications, the robust observability and robust output regulation of LCNs with function perturbation are discussed.