Logic System Research Articles

Finite time prescribed performance control for stochastic systems with asymmetric error constraint and actuator faults

This paper investigates the problem of finite time prescribed performance control (PPC) for a number of nonlinear stochastic systems with asymmetric error constraint, unknown control directions, and actuator faults. Firstly, instead of introducing the performance constraint function in the Lyapunov function, a new asymmetric error conversion function (AECF) is presented, which can successfully constrain the tracking errors into the specified asymmetric boundaries and eliminate the feasibility condition of requiring tracking errors to be bounded. Then, in iterative process, the unknown intermediate virtual controller is approximated by the fuzzy logic system (FLS), which makes each actual virtual controller to be reduced to only one item. Furthermore, the investigated finite time PPC strategy can fully compensate the faults impact on the systems and have only one adaptive parameter. In the end, the efficacy of investigated strategy is verified by inverted pendulum systems with stochastic disturbances.

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

Practically fast finite-time stability of stochastic constrained nonlinear systems with actuator dead zone

This article addresses the challenge of achieving practically fast finite-time stabilization for stochastic constrained nonlinear systems, which are subject to both quantization effects and actuator dead zones. To tackle these issues, adaptive parameterization and partial control strategies are introduced with the aim of efficiently approximating and counteracting nonlinear disturbances. This approach ensures the robust stabilization of the controlled system within a finite time frame, despite the presence of uncertainties. Additionally, a novel barrier function is propose that mitigates the constraints usually imposed by boundary functions, while also leveraging a fuzzy logic system to manage nonlinear terms adeptly. Building on these innovations, we formulate a new theorem dedicated to practically fast finite-time control mechanisms for stochastic nonlinear systems. The efficacy of our theoretical developments is substantiated through an illustrative example involving a current-controlled DC motor system, demonstrating the practical applicability and robustness of the proposed control scheme.

Coastal Cultural Ecosystem Services: A Bridge between the Natural Ecosystem and Social Ecosystem for Sustainable Development

Cultural Ecosystem Services (CESs), as non-material benefits and well-being provided by ecosystems to humans, possess the ability to bridge nature and human society and interpret their complex interrelationships. Coastal areas are regions with concentrated human activities, where coastal zones are often subject to human development, pollution, and degradation. Compared to other ecosystems, coastal ecosystems face greater pressures and threats, and the cultural services they provide are more vulnerable. Research on coastal ecosystem cultural services needs to consider ecosystem vulnerability and find ways to protect and restore ecosystem functions. Therefore, this paper explores the intrinsic logical system and feasibility of guiding natural resource management and enhancing human well-being through coastal CESs, discussing related research data acquisition, method analysis, and perceptual application. Based on this, this paper analyzes the development trends of coastal CESs in natural resource management and enhancing human well-being from aspects such as biodiversity, human–nature interaction processes, cultural heritage conservation, local economic development, and community management. Finally, it proposes advancing the in-depth research of coastal CESs from the perspectives of integrating multi-source data, interdisciplinary development, and incorporating CESs into policy making, providing theoretical support for the systematic study of rational resource utilization and sustainable ecosystem development.

Optimal power management for hybrid electrical vehicle

This paper introduces an innovative optimal energy management strategy tailored for a hybrid electric-powered hydraulic excavator system. The aim is to bolster power performance, extend the lifespan of power sources, and optimize hydrogen usage. In this system, the fuel cell serves as the primary power source, while integrating supercapacitors and batteries offers benefits such as enhancing power performance and storing regenerative energy for future use. To efficiently distribute power among these three sources and maximize the utilization of regenerative energy, we propose an energy management strategy. This strategy combines fuzzy logic control with a rule-based algorithm. Moreover, we optimize the parameters of the fuzzy logic system using a combination of the backtracking search algorithm and sequential dynamic programming. This optimization aims to reduce hydrogen consumption and prolong the lifespan of the power sources. Simulation results demonstrate that our proposed energy management strategy significantly enhances vehicle performance, improves fuel economy of the hybrid electric-powered hydraulic excavator system, and enhances the durability and efficiency of the battery and supercapacitor systems within the fuel cell system.

Parametric modeling of resin-bonded sand mold systems using machine learning-based approaches

This study presents the experimental investigations on modeling of the compression strength and permeability of the resin-bonded sand mold system through Machine Learning approaches. The process of constructing the Fuzzy Logic system was automated by utilizing a data-base and rule-base optimized through genetic algorithms, and the recorded datasets. This research employed three AI models, namely Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forests (RF), using the datasets produced by the GA-tuned Fuzzy model. The objective of this study was to assess and compare the predictive capabilities of three different AI models (Artificial Neural Networks, Decision Tree, and Random Forests) in terms of predicting the values of Compression Strength and Permeability. The complete dataset was divided into two separate subsets, referred to as training data and testing data. Based on the findings, it appears that Random Forest (RF) model exhibits promising potential in accurately predicting the desired mold qualities. The model achieved a high R2 value of 0.9487, indicating a strong correlation with the target values. Additionally, the model demonstrated impressively low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 117 and 17.6 points, respectively. Expanding the dataset size may further enhance the efficacy of the models.

The Insights and Influence of the Sydney Accord on the Teaching Reform of Vocational Education in China: A Case Study of Professional Curriculum Achievement Evaluation

Scientific curriculum evaluation methods are critical for achieving talent training objectives. The Sydney Accord, one of the three major engineering education certification agreements, focuses on the training of “engineering technologists.” Its logical system and three core principles align well with the connotation development of professional construction in China’s higher vocational colleges, offering significant insights for promoting reform and development in engineering education in Chinese colleges and universities. By incorporating the core principles, elements, and graduate quality defined in the Sydney Accord and focusing on the evaluation of curriculum achievement against “graduation requirements,” the quality of vocational education can be enhanced, making it an important direction for future teaching reforms in vocational education.

Specification and Verification for Unrestricted Algebraic Effects and Handling

Programming with user-defined effects and effect handlers has many practical use cases involving imperative effects. Additionally, it is natural and powerful to use multi-shot effect handlers for non-deterministic or probabilistic programs that allow backtracking to compute a comprehensive outcome. Existing works for verifying effect handlers are restricted in one of three ways: i) permitting multi-shot continuations under pure setting; ii) allowing heap manipulation for only one-shot continuations; or iii) allowing multi-shot continuations with heap-manipulation but under a restricted frame rule. This work proposes a novel calculus called Effectful Specification Logic (ESL) to support unrestricted effect handlers, where zero-/one-/multi-shot continuations can co-exist with imperative effects and higher-order constructs. ESL captures behaviors in stages, and provides precise models to support invoked effects, handlers and continuations. To show its feasibility, we prototype an automated verification system for this novel specification logic, prove its soundness, report on useful case studies, and present experimental results. With this proposal, we have provided an extended specification logic that is capable of modeling arbitrary imperative higher-order programs with algebraic effects and continuation-enabled handlers.

Self-triggered fuzzy trajectory tracking control for the stratospheric airship

A self-triggered trajectory tracking control algorithm, which considers unknown disturbance and actuator saturation, has been studied to address the energy and onboard equipment life limitation problems of stratospheric airships. The controller is based on a backstepping-sliding mode method, utilizing the fuzzy logic systems and auxiliary systems to handle unknown disturbance and actuator saturation, respectively. Based on the continuity of the control law, the self-triggered condition is designed whose paradigm is used to build the fuzzy logic systems. Compared with the existing stratospheric airships event-triggered controller, continuous state detection and calculation are avoided. It is proved that the tracking errors and the weight matrix errors of the fuzzy logic system are uniformly ultimately bounded both at the trigger moment and within the trigger interval. Additionally, Zeno behavior is avoided without imposing additional parameter restrictions. Simulation results demonstrate the effectiveness of the proposed algorithm. Based on execution frequency, the proposed algorithm can conserve 98.3% of resources compared to the traditional algorithm and 97.5% of resources compared to the event-triggered algorithm.

Finite-time fuzzy adaptive consensus control for state-constrained MIMO nonlinear MASs with switched topologies

In this article, the adaptive fuzzy finite-time output feedback consensus control problem is investigated for multiple input and multiple output (MIMO) nonlinear multi-agent systems (MASs) with switched topologies and asymmetric state constraints. Fuzzy logic systems (FLSs) are utilised to approximate uncertain nonlinear dynamics, and a distributed observer and a state observer are established to estimate the unknown leader and system states, respectively. By constructing the barrier Lyapunov functions (BLFs), a finite-time output feedback consensus control scheme is presented via backstepping control technique. It is proved that the controlled MASs are stable, and the consensus errors converge in finite time. Moreover, the system states will not exceed the given bounds. Finally, the proposed finite-time consensus control approach is applied to control multiple unmanned surface vehicles (USVs), the simulation results check the feasibility of the developed finite-time consensus control methodology.

Retraction Note: A Hybrid Approach Using the Fuzzy Logic System and the Modified Genetic Algorithm for Prediction of Skin Cancer

Retraction Note: A Hybrid Approach Using the Fuzzy Logic System and the Modified Genetic Algorithm for Prediction of Skin Cancer

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

Global Low-Complexity Fault-Tolerant Control for Pure-Feedback Systems with Sensor Faults

A low-complexity global fault-tolerant control method is proposed to solve the tracking problem of uncertain pure-feedback systems in the presence of sensor faults. First, a novel modeling approach is introduced to reconstruct the non-affine term, which removes the restriction that the non-affine function must be differentiable. Second, a novel nonlinear mapping based on inverse-tangent function is utilized in the controller design such that the control parameters are free from initial values of states compared to the traditional prescribed performance control methods, resulting in global fault-tolerant control of pure-feedback systems under sensor and actuator faults. Furthermore, the designed global controller is low-complexity in the sense that no time derivatives of system signals are involved in the controller, and no neural networks or fuzzy logic systems are used, though unknown nonlinearities are present in the considered systems, and the control parameters are allowed to be arbitrary positive constants. Finally, the proposed method is applied to numerical and tailless fly-wing UAV examples, which fully demonstrates the effectiveness of the proposed method.

Modality across different logics

Abstract In this paper, we deal with the problem of putting together modal worlds that operate in different logic systems. When evaluating a modal sentence $\Box \varphi $, we argue that it is not sufficient to inspect the truth of $\varphi $ in accessed worlds (possibly in different logics). Instead, ways of transferring more subtle semantic information between logical systems must be established. Thus, we will introduce modal structures that accommodate communication between logic systems by fixing a common lattice $L$ that contains as sublattices the semantics operating in each world. The value of a formula $\Box \varphi $ in a world with lattice $L^{\prime}$ will be defined in terms of the values of $\varphi $ in accessible worlds relativized to $L^{\prime}$ using the common order of $L$. We will investigate natural instances where formulas $\varphi $ can be said to be necessary/possible even though all the accessible world falsify $\varphi $. Further, we will discuss frames that characterize dynamic relations between logic systems: classically increasing, classically decreasing and dialectic frames. Finally, we formalize the semantics of considering worlds operating in classical logic or logic of paradox, exemplifying the kind of issue one should face in this kind of formalization.

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

On soft measurement modeling for predicting photovoltaic power with uncertainty based on the Takagi–Sugeno model

Abstract Accurate solar power forecast is becoming more essential for safe and reliable power grid operation with the increasing number of grid-connected photovoltaic (PV) power production units. However, PV power exhibits significant output fluctuation due to both external inputs and intrinsic stochasticity in system dynamics. Therefore, an efficient and reliable soft measurement model of PV power with uncertainty is demanded in practice applications. The technique described in this paper captures the impacts produced by the fundamental uncertainty observed in the data, instead of relying on unrealistic assumptions about uncertainty. A soft measurement model using the Takagi-Sugeno (TS) Fuzzy Logic System (FLS) based on several input-output time series of PV plants is presented in this study. Chebyshev's inequality from probability theory and statistics is adopted to create the confidence interval-based response envelopes for these time series at each moment. An envelope-based measure of output uncertainty and a center-valued response forecasting model can be obtained by the proposed identification technique. PV datasets are employed to demonstrate the concept, which indicates the proposed soft measurement may outperform existing methods in terms of prediction accuracy. The average values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Correlation Coefficient (R) are only 0.0787, 0.1113, and 0.9979. The average values of the prediction interval coverage probability (PICP) and the prediction interval normalized average width (PINAW) are 0.9806 and 0.1051.

Nonsingular fixed-time K-filter output-feedback control for high-order nonlinear multi-agent systems

This paper investigates the problem of nonsingular fixed-time control for high-order nonlinear multi-agent systems with unmeasurable states and disturbances. To address the unmeasurable states, a K-filter is introduced to reconstruct the system with the input and output only and to facilitate the construction of the observer. To provide an approximation for the unknown nonlinear functions, the fuzzy logic system technique is applied. A novel tracking controller is proposed to ensure that all signals remain bounded for the closed-loop system and that the tracking error converges into a small and adjustable set within a fixed time by utilising the adaptive backstepping recursive technology and the adding power integrator technique. Meanwhile, the singular problems arising from the fixed-time stability criterion can be also avoided. Finally, a simulation experiment verifies the effectiveness of the designed control scheme.

The key universal quality of logical abductive inferences: Does optimization of eco-cognitive situatedness supersede other criteria like minimality, consistency, relevance and plausibility?

Abstract Throughout my investigation into abductive cognition, which is also associated with the endeavor to naturalize logic of its special consequence relation, I stressed the relevance of the following key elements: ‘optimization of eco-cognitive situatedness’, ‘maximization of changeability’ of both input and output of the general form of an inferential abductive problem and high ‘information-sensitiveness’. These elements will be summarized and further deepened in this article. In addition, I will demonstrate how, in contrast to traditional demonstrative logical ideal systems, which are typified by what I refer to as the ‘maximization of memorylessness’, a naturalized logic of abduction demonstrates the necessity of recording the ‘past life’ of abductive inferential praxes. Lastly, I will analyze the significance and nature of the maximization of eco-cognitive openness, which characterizes abduction in science and describes how the many above-mentioned elements are relevant to the aims of new eco-cognitive epistemology.

Command filtering‐based adaptive backstepping control for a class of discrete‐time uncertain nonlinear systems with mismatched disturbances

SummaryThis paper investigates the adaptive fuzzy backstepping control problem for uncertain discrete‐time nonlinear systems with mismatched disturbances. A novel adaptive fuzzy command filtered backstepping approach is proposed, which can overcome the noncausal problem by using the command filter. The filter errors generated by filter are removed by constructing error compensation mechanism. The fuzzy logic systems are used to approximate the uncertain nonlinear dynamics, and the unknown mismatched disturbances are actively attenuated by using adaptive control technique. Based on the proposed new weighed Lyapunov functions, all the signals in the close‐loop system are proved to be uniformly ultimately bounded. The viability of proposed method is demonstrated by simulation results.

A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC's staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.