Logic System Research Articles

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles. However, as these states are not directly measured, Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them. Unfortunately, existing methods tend to ignore the impact of data loss on estimation performance. Furthermore, the process noise, which changes dynamically due to varying driving conditions, is not adequately considered. In response to these constraints, we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter (FAFTEKF). Initially, a fault-tolerant EKF is devised to handle missing measurements. Additionally, a fuzzy logic system that dynamically updates the process noise matrix, is built to improve estimation accuracy under different driving conditions. Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

Dynamic Performance Evaluation of a Five-Phases Induction Motor System controlled using Fuzzy Logic Director

The five-phase induction motor drive offers distinct benefits that enhance the motor's torque production capabilities. The proposed fuzzy logic controller is well suited for high-performance five-phase induction motor drives. This paper is discussed about a speed control strategy for a five-phase induction motor drive system using a Fuzzy Logic Controller (FLC). This controller adjusts system parameters through a rule-based fuzzy logic system, mimicking human reasoning for process control. The speed control algorithm employs indirect vector control.

Light-Operated Diverse Logic Gates Enabled by Modulating Time-Dependent Fluorescence of Dissipative Self-Assemblies.

Light-fueled dissipative self-assembly possesses enormous potential in the field of optical information due to controllable time-dependent optical signals, but remains a great challenge for constructing intelligent light-operated logic circuits due to the limited availability of optical signal inputs and outputs. Herein, a series of light-fueled dissipative self-assembly systems with variable optical signals are reported to realize diverse logic gates by modulating time-dependent fluorescence variations of the loaded fluorophores. Three kinds of alkyl trimethylammonium homologs are employed to co-assemble with a merocyanine-based photoinduced amphiphile separately to construct a series of dissipative self-assemblies, showing unexpectedly different fluorescence control behaviors of loaded fluorophores during light irradiation and thermal relaxation processes. The opposite monotonicity of time-dependent emission intensity is achieved just by changing the excitation wavelength. Furthermore, by varying the types of trimethylammoniums and excitation wavelengths, a robust logic system is accomplished, integrating AND, XNOR, and XOR functions, which provides an effective pathway for advancing information transmission applications.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

PREDICTION SYSTEM FOR THE AMOUNT OF SUGAR PRODUCTION USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Sugar is one of the staple foods most Indonesians use, so sugar production needs to be done optimally to meet people's needs. This research will design a prediction system for the amount of sugar production in PTPN XI PG Prajekan using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. ANFIS is a combined method of two systems, namely a fuzzy logic system and an artificial neural network system. This research consists of data collection, ANFIS system design, ANFIS training, ANFIS testing, accuracy calculation, and result analysis. The prediction system for the amount of sugar production is designed to predict the variable which is the amount of sugar production in the year using the input variables (sugarcane harvested area in year ), (amount of sugarcane in year ), (average of yield in year ), and (number of milling days in year ). The experiments in this research used variations of the type of membership function and the number of membership functions. The best model obtained in this research is a model with a difference between two sigmoidal membership functions and a product of two sigmoidal membership functions with a total of 2 membership functions for each input variable. Both models have the same Mean Absolute Percentage Error (MAPE) value, which is 1.79% in the training process and 4.82% in the testing process.

Ratiometric fluorescence probe based on boric acid-modified carbon dots and alizarin red for sensitive and rapid detection of glyphosate.

Bycombining boric acid-modified carbon dots (p-CDs) and alizarin red (ARS), a double emission probe p-CDs@ARS with fluorescence at 410nm and 600nm is designed for the detection of glyphosate. When Cu2+ is added, it binds with ARS to cause ARSreleasefrom p-CDs@ARS, which decreases the fluorescence at 600nm. However, in the presence of glyphosate, glyphosate competes to thebinding ofCu2+, releasing ARS to bind with p-CDs again. Therefore, the fluorescence of 600nm recovers. Based on this, the fluorescence of 410nm and 600nm act as the reference and response signal, respectively,achieving the ratiometric fluorescence detection of glyphosate. The linear range of glyphosate detection is 0.5-50µM with a limit of detection at 0.37µM which is well below the maximum residue limit for glyphosate in food. When the probe is used to detect the glyphosate residue in Pearl River water and cucumber, the detection results are well consistent with those detected by HPLC. The established method based on p-CDs@ARS has the advantages that the assembly of ratiometric fluorescence probe is simple, and the detection speed is fast. Additionally, a typical INHIBIT logical system has been successfully constructed based on glyphosate, Cu2+, and the fluorescence signal of p-CDs@ARS.

Ln3+-doped glasses: Advancing molecular logic for integration into photonic and electronic devices

The rapid miniaturization of electronic devices has pushed the limits of conventional silicon-based technologies, creating a pressing need for novel approaches to sustain the exponential growth of computing power. This study explores the innovative application of Ln3+-doped glasses in developing molecular logic systems as a potential solution. Exploiting Eu3+ and Dy3+, we investigated the luminescence properties under various physical stimuli such as excitation wavelength and temperature. Our findings reveal the capability to construct logic elements of varying complexity, from simple AND and OR gates to advanced FULL-ADDER and FULL-SUBTRACTOR circuits. This work represents the first instance of using Ln3+ exclusively for molecular logic via physical stimuli. The robust and stable optical properties of the doped glasses enable their integration into conventional electronic and photonic devices, potentially revolutionizing the landscape of molecular logic and computing. This research not only enhances the understanding of light-matter interactions in Ln3+-doped systems but also opens new pathways for the development of miniaturized, high-performance innovative computational devices.

Study on Non-iterative Algorithms for Center-of-Sets Type-Reduction of Interval Type-2 Takagi–Sugeno–Kang Fuzzy Logic Systems

Study on Non-iterative Algorithms for Center-of-Sets Type-Reduction of Interval Type-2 Takagi–Sugeno–Kang Fuzzy Logic Systems

Logics for contact and measure

Abstract We enrich contact algebras with a new binary relation that compares the size of regions, and provide axiom systems for various logics of contact and measure. Our contribution is three-fold: (1) we characterize the relations on a Boolean algebra that derive from a measure, thereby improving an old result of Kraft, Pratt and Seidenberg; (2) for all $n \geq 1$, we axiomatize the logic of regular closed sets of ${\mathbb{R}}^{n}$ with null boundary; (3) considering a broad class of equational theories that contains all logics of contact, we prove that they all have unitary or finitary unification, and that unification and admissibility are decidable.

Barrier function–based adaptive STSMC for steering feel feedback of SBW vehicle with uncertainty

As an essential component of human–computer interaction in steer-by-wire (SBW) vehicles, the steering feel feedback system provides drivers with accurate steering feel by precisely controlling the output torque of the steering feel motor. Meanwhile, the existence of uncertainties makes the accurate control of steering feel feedback torque become an acknowledged challenging issue. This article proposes an adaptive super-torque sliding mode control strategy based on barrier functions for the steering feel feedback system of SBW vehicles with highly complex dynamics in the presence of uncertainties. First, to achieve intelligent modeling under the influence of uncertainty factors, a dynamic system with unknown uncertainty based on adaptive fuzzy logic system (FLS) is proposed. In the framework of this model, the uncertainty factors introduced by unknown external disturbance, parameter uncertainty, and model couplings are considered as a lumped uncertainty function. Furthermore, the membership functions of the FLS are dynamically adjusted based on nearest neighbor clustering, aiming to enhance the modeling accuracy beyond traditional fuzzy modeling. To further offset the system uncertainty and FLS approximation error, an adaptive super-twisting sliding mode control (STSMC) method based on barrier function is proposed. This algorithm retains the advantages of traditional sliding mode control (SMC) methods in dealing with systems with uncertainty and eliminates the need for a priori knowledge of the upper bound of uncertainties by introducing the adaptive law based on barrier function, avoiding the use of complex identification algorithms. Moreover, it eliminates the gain of overestimation and significantly reduces chattering phenomenon in traditional sliding mode control. Finally, based on Lyapunov stability theory, this article proves the proposed system can achieve convergence within a finite time. Compared with STSMC, traditional SMC and proportional–integral–derivative (PID) control, the results verify the effectiveness of the proposed control method. The proposed strategy provides a new solution to reduce the steering feel feedback torque error under the influence of uncertain factors.

Observer-based adaptive fuzzy quantized fault-tolerant control of nonstrict-feedback nonlinear systems with actuator and sensor faults

This article emphasises the adaptive quantised fault-tolerant output feedback control problem for one type of nonstrict-feedback nonlinear systems suffering from input and output quantisation as well as actuator and sensor faults. By introducing a bounded time-varying function (BTVF), a generalised intermediate-variable estimator (IVE) is designed to estimate actuator bias fault. For the scaling measurement sensor fault, by applying a projection operator containing the measured output quantised signal, a novel gain-compensated state observer is constructed. In addition, relying on fuzzy logic system (FLS) and intelligent adaptive algorithm, a fuzzy fault-tolerant controller is devised to handle dual faults and quantisation. After theoretical proof, the presented control framework can realise that the closed-loop system is semi-globally uniform ultimately bounded (SGUUB), while the tracking error is within an adjustable area. Lastly, the validity and superiority of the suggested scheme is confirmed by a relevant simulation example.

Games for hybrid logic from semantic games to analytic calculi

Abstract Game semantics and winning strategies offer a potential conceptual bridge between semantics and proof systems of logics. We illustrate this link for hybrid logic—an extension of modal logic that allows for explicit reference to worlds within the language. We lift a Hintikka-style semantic game to a disjunctive game. The disjunctive game adequately models entailment and validity over classes of frames characterizable in the hybrid language. The search for winning strategies in this game can be reformulated as a sequent-style proof system.

Type-3 fuzzy neural networks for dynamic system control

This paper presents a Type-3 Fuzzy Neural Network (T3FNN) for dynamic system control. The structure of the T3FNN controller, which is based on the compositional rule of inference of interval type-3 fuzzy systems, is proposed. TSK-type fuzzy rules, employing three-dimensional membership functions (MFs) in the antecedent part and linear functions with coefficients represented as interval sets in the consequent part, are utilized to design T3FNN. Gaussian functions with uncertain centers are employed to describe the type-3 membership functions. By employing three-dimensional MFs, type-3 fuzzy logic rules can accurately model a high degree of uncertainty and effectively handle uncertain information. The design of the T3FNN was accomplished by integrating the type-3 fuzzy logic system (T3FLS) with neural networks. The learning of the T3FNN was conducted using the genetic algorithms and gradient descent algorithm. As a result, the type-3 rule base of the system was effectively developed. The learning algorithms are presented and the proposed T3FNN structure is employed for the development of dynamic system control. The simulations of the T3FNN system for the control of the nonlinear and time-varying dynamic systems were carried out. The results obtained from the simulations indicate the suitability of using the T3FNN in controlling dynamic systems.

Predefined-time adaptive fuzzy control for nonlinear output constraint systems with unknown backlash-like hysteresis via command filtering

In this article, the predefined-time adaptive fuzzy control problem for strict-feedback nonlinear systems with asymmetric time-varying output constraint and unknown backlash-like input hysteresis is considered. An innovative predefined-time adaptive control method, in which utilises unified barrier function (UBF) to deal with the output constraint problem and adopts a differential equation to represent the unknown backlash-like hysteresis, is presented by combining a novel command filter with backstepping technology. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinearities. Moreover, by designing appropriate parameters, the constructed control strategy ensures that all signals are predefined-time bound and the tracking error stays in a small neighbourhood adjacent zero. Ultimately, two simulation examples are given to prove the effectiveness of the presented control algorithm.

Framework for LLM applications in manufacturing

In the era of Industry 4.0, the proliferation of data within manufacturing environments has presented both unprecedented opportunities and challenges. This paper introduces a framework that capitalizes on the capabilities of Large Language Models (LLMs) to revolutionize data integration and decision-making processes in manufacturing systems. Addressing the critical need for efficient data management, our framework streamlines the consolidation, processing, and generation of responses to essential inquiries, thus enhancing manufacturers’ capabilities to extract valuable insights. The focus of this paper is twofold. First to establish a framework for the use of LLM applications in manufacturing settings. Secondly, to provide an overview of the manufacturing connection between data, AI, and chat-bots, while also addressing a few pain points identified from the manufacturing literature. The paper then introduces FILLIS (Factory Integrated Logic and Language Interface System), a Large Language Model assistant, through a compelling case study. FILLIS showcases remarkable versatility, excelling in tasks ranging from elucidating machine operations to language translation. The study underscores FILLIS’s proficiency in handling specific contexts, answering questions from uploaded documents with precision. However, inherent limitations surface in tasks involving mathematical operations, emphasizing the need for external agents in specific scenarios. This pivotal opportunity is explored in the proposed framework as it advocates for integrating external agents alongside LLMs, creating a more versatile and comprehensive assistant tool. The findings of this paper and proposed framework position LLMs as transformative tools for intelligent data processing.

Novel Controller Methodology for the Cessna Citation X Under Turbulence During Cruise

The design of a new control approach for the longitudinal motion of the Cessna Citation X during cruise is performed using a combination of sliding mode control (SMC) system, type 1 fuzzy logic system, and adaptive control system. This methodology is presented for 1) controlling the aircraft pitch rate and 2) stabilizing the aircraft speed during turbulence. The nonlinear model of the aircraft was generated using a simulation platform, which was designed based on flight data obtained from the highest Federal Aviation Administration–certified Level-D Research Aircraft Flight Simulator. The type 1 adaptive fuzzy logic system was implemented to approximate unknown functions for constructing the equivalent part of the SMC system that handled the effects of uncertainties and turbulence. The adaptation laws, derived from the Lyapunov theorem, were used to update the approximated functions in the control law at each flight condition and simulation iteration. Using the control systems combination, the pitch rate could follow the given reference signal, while the aircraft speed remained at a reference value with and without turbulence across the whole flight envelope. Results have shown that the proposed controllers satisfied tracking performance while generating smooth elevator deflection, both of which are important for real-aircraft applications.

Classification of high-resolution remote sensing images based on interval type-2 fuzzy logic system

Abstract Aiming at the complexity and uncertainty of remote sensing image classification, this paper proposes a high-resolution remote sensing image classification method based on Interval type-2 Fuzzy Logic Systems for Remote Sensing (IT2FLS-RS), which focuses on dealing with multiple uncertainties and aims to improve the accuracy and reliability of remote sensing image classification. The method establishes a more complex interval two-type fuzzy system covering two types of fuzzifiers, a rule base and an inference machine, and finally uses an integrated algorithm as a defuzzifier to achieve accurate pixel-level classification. In addition, the model uses Constrained Optimization BY Linear Approximations (COBYLA) to optimise the key parameters. In the DLRSD dataset, the accuracy of this model is improved by about 20%, 14%, 24% and 10% compared to the state-of-the-art interval two-type fuzzy neural network algorithm and the benchmark model XGBoost, respectively. On the WHDLD dataset, the accuracy of the proposed method is improved by about 12% and 10% compared to the state-of-the-art interval type-2 fuzzy neural network algorithm and the benchmark model XGBoost, respectively. The experimental results confirm the robustness of the proposed method in processing high-resolution remote sensing image classification, especially the excellent adaptability and scalability in complex feature scenes.

تصميم متحكم المنطق الضبابي لتتبع أقصى نقطة قدرة لنظام الطاقة الكهروضوئية

This research paper presents the method of fuzzy logic system to optimize the energy extraction in a photovoltaic (PV) power system. The maximum power of a (PV) module varies due to changing temperature, solar radiation, and load. To maximize efficiency, PV systems use a maximum power point tracker (MPPT) to constantly extract the highest power that can be produced by a solar panel and then deliver it to the load. The general structure of an (MPPT) system contains a (DC-DC) converter (an electronic device that converts a source of direct current DC from one voltage level to another) and a controller. The (MPPT) finds and maintains operations at the maximum power point using a tracking algorithm during variations in weather conditions. Because of the nonlinear behavior of (PV) module current-voltage characteristics and the nonlinearity of (DC-DC) converters due to switching, conventional controllers are unable to provide the best response, especially when dealing with wide parameter variations and line transients. The goal of this work is to design andimplement a maximum power point tracker that uses a fuzzy logic control algorithm. Fuzzy logic naturally provides a superior controller for this type of nonlinear application. This method also benefits from the artificial intelligence approach for overcoming the complexity in modeling nonlinear systems. In order to succeed in this work, an (MPPT) system consisting of a (PV) module, a (DC-DC) converter, and a fuzzy logic controller is designed and simulated in Simulink. Analyses of buck- boost converter characteristics are carried out to find the most suitable topology for the PV system used. An integrated model of the PV module with the identified converter is simulated in MATLAB to derive the expert knowledge needed to formulate and tune the fuzzy logic controller. The simulation results show that, the fuzzy logic controller is able to obtain the desired outcomes which are actual power generation of solar panels at different irradiance and temperature. Keywords: Fuzzy logic, PV, MPPT, DC-DC converter, MATLAB programming

A Formal Language for Performance Evaluation Based on Reinforcement Learning

Temporal Logics are a rich variety of logical systems designed for specifying properties over time, and about events and changes in the world over time. Traditional temporal logic, however, is limited to binary outcomes true or false and lacks the capacity to specify performance properties of a system such as the maximum, minimum, or average costs between states. Current languages do not accommodate the quantification of such performance properties, especially in scenarios involving infinite execution paths where performance property like cumulative sums may fail to converge. To this end, this paper introduces a novel formal language aimed at assessing system performance, which encapsulates not only temporal dynamics but also various performance-related properties. In this study, this paper utilizes reinforcement learning techniques to compute the values of performance property formulas. Finally, in the experimental part, a formal language representation of system performance properties was implemented, and the values of the performance property formulas were computed using reinforcement learning. The effectiveness and feasibility of the proposed method were validated.