Adaptive Fuzzy Research Articles

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Adaptive fuzzy finite-time output feedback fault-tolerant control for MIMO stochastic nonlinear systems with non-affine nonlinear faults

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy.

Utilization of Adaptive Neuro Fuzzy Inference System (ANFIS) for Prediction of Sacha Inchi Seed Weight Based on Fruit Morphology and Fertilizer Treatment

Utilization of Adaptive Neuro Fuzzy Inference System (ANFIS) for Prediction of Sacha Inchi Seed Weight Based on Fruit Morphology and Fertilizer Treatment

Adaptive fuzzy-genetic algorithm operators for solving mobile robot scheduling problem in job-shop FMS environment

Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality.

Event-Triggered Adaptive Fuzzy Control for Strict-Feedback Nonlinear FOSs Subjected to Finite-Time Full-State Constraints

In this article, an event-triggered adaptive fuzzy finite-time dynamic surface control (DSC) is presented for a class of strict-feedback nonlinear fractional-order systems (FOSs) with full-state constraints. The fuzzy logic systems (FLSs) are employed to approximate uncertain nonlinear functions in the backstepping process, the dynamic surface method is applied to overcome the inherent computational complexity from the virtual controller and its fractional-order derivative, and the barrier Lyapunov function (BLF) is used to handle the full-state constraints. By introducing the finite-time stability criteria from fractional-order Lyapunov method, it is verified that the tracking error converges to a small neighborhood near the zero and the full-state constraints are satisfied within a predetermined finite time. Moreover, reducing the communication burden can be guaranteed without the occurrence of Zeno behavior, and the example is given to demonstrate the effectiveness of the proposed controller.

Command filter‐based adaptive robust prescribed performance formation control for nonlinear multi‐agent systems

SummaryThis paper investigates an observer‐based adaptive robust fuzzy formation tracking control method for nonlinear multi‐agent systems (MAS) with input delay. The considered nonlinear MAS contains the unknown nonlinear functions, that is, unmeasured states. The fuzzy logic systems (FLS) are used to approximate the unknown nonlinear functions, and then a fuzzy adaptive state observer is designed to estimate the unmeasured states. The Pade approximation approach is introduced to solve the input delay issue. By combining command filtering and backstepping recursive control techniques, a robust adaptive prescribed performance formation output feedback tracking controller is designed. Moreover, the proposed controller can avoid the “explosion of complexity” and “singularity” problems in the backstepping design framework. Based on Lyapunov stability theory, it is proved that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded (SGUUB), and the formation tracking errors do not exceed the prescribed performance bounds. Finally, the feasibility of the proposed control approach can be further verified through numerical and practical examples.

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

Polishing force is one of the key process parameters in the polishing process of blisk blades, and its control accuracy will affect the surface quality and processing accuracy of the workpiece. The contact mechanism between the polishing surface and flap wheel was analyzed, and the calculation model of the polishing force and nonlinear dynamic model of the polishing force control system was established. Considering the influence of friction characteristics, parameter perturbation, and nonlinear dead zone on the control accuracy of the polishing force system, an adaptive fuzzy sliding mode controller (AFSMC) was designed. AFSMC uses a fuzzy system to adaptively approximate the nonlinear function terms in the sliding mode control law, adopts an exponential approach law in the switching control part of the sliding mode control (SMC), and designs the adaptive law for adjustable parameters in the fuzzy system based on the Lyapunov Theorem. Simulation and experimental results show that the designed AFSMC has a fast dynamic response, strong anti-interference ability, and high control accuracy, and it can reduce SMC high-frequency chatter. Polishing experiments show that compared with traditional PID, AFSMC can improve the form and position accuracy of the blade by 42% and reduce the surface roughness by 50%.

Fuzzy Adaptive Containment Control for Nonlinear Multi-Manipulator Systems With Actuator Faults and Predefined Accuracy

Fuzzy Adaptive Containment Control for Nonlinear Multi-Manipulator Systems With Actuator Faults and Predefined Accuracy

Application of artificial intelligence based on the fuzzy control algorithm in enterprise innovation

Artificial Intelligence (AI) has gained immense popularity in recent years as many enterprises have realized their potential to change the way of conducting business innovatively. The new concepts, items, or procedures are developed and implemented within a business or organization to enhance productivity, effectiveness, and competitiveness, and this is called Enterprise Innovation (EI). AI techniques are required to make decisions more effectively in challenging and dynamic situations, like EI, as result of competitive marketplace. Hence, an intelligent, innovative strategy with Q-learning and Takagi Sugeno Fuzzy Control (Q-TSFC) algorithm has been proposed as it combines adaptive learning and Fuzzy Logic (FL) that humans understand to improve decision-making in enterprise innovation. Q-learning seeks to maximize the enterprise's profit by utilizing the newly acquired knowledge, exploring activities, and adaptive learning based on the optimal ε greedy policy that results with rewards and the experiences. To develop a framework for making decisions and connections between input from the learned Q values and output decisions using enterprise expertise and linguistic conventions. The objective is to handle language uncertainty and imprecision in market trend. So, it leads to right decisions even without accurate numerical facts. The proposed approach is validated by evaluating metrics like cost savings, customer satisfaction, and innovation performance efficiency in the competitive edge in the market. With this proposed Q-TSFC algorithm, the obtained results are 96.5% customer satisfaction ratio, 96% enterprise performance efficiency, cost savings of about 48% profitable value, and the coefficient of determination R2 is 0.83, respectively.

Reinforcement Learning-Based Adaptive Event-Triggered Fuzzy Control for Cyclic Switched Stochastic Nonlinear Systems With Actuator Faults

Reinforcement Learning-Based Adaptive Event-Triggered Fuzzy Control for Cyclic Switched Stochastic Nonlinear Systems With Actuator Faults

Online Recursive Detection and Adaptive Fuzzy Mitigation of Cyber-Physical Attacks Targeting Topology of IMG: An LFC Case Study

Online Recursive Detection and Adaptive Fuzzy Mitigation of Cyber-Physical Attacks Targeting Topology of IMG: An LFC Case Study

A Dual Triggering Scheme to Adaptive Fuzzy Resilient Control of Switched Nonlinear Systems Against Mixed Attacks.

A dual event-triggered adaptive fuzzy resilient control scheme for a class of switched nonlinear systems with vanishing control gains under mixed attacks is proposed in this article. The scheme proposed achieves dual triggering in the channels of sensor-to-controller and controller-to-actuator by designing two new switching dynamic event-triggering mechanisms (ETMs). An adjustable positive lower bound of interevent times for each ETM is found to preclude Zeno behavior. Meanwhile, mixed attacks, that is, deception attacks on sampled state and controller data and dual random denial-of-service attacks on sampled switching signal data, are handled by constructing event-triggered adaptive fuzzy resilient controllers of subsystems. Compared with the existing works for switched systems with only single triggering, more complex asynchronous switching caused by dual triggering and mixed attacks and subsystem switching is addressed. Further, the obstacle caused by vanishing control gains at some points is eliminated by proposing an event-triggered state-dependent switching law and introducing vanishing control gains into a switching dynamic ETM. Finally, a mass-spring-damper system and a switched RLC circuit system are applied to verify the obtained result.

Command Filter Adaptive Fuzzy Formation Asymptotic Tracking Control for Nonholonomic Multirobot Systems With Multiple Constraints

Command Filter Adaptive Fuzzy Formation Asymptotic Tracking Control for Nonholonomic Multirobot Systems With Multiple Constraints

Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

Adaptive Fuzzy Fast Finite-Time Output-Feedback Tracking Control for Switched Nonlinear Systems With Full-State Constraints

Adaptive Fuzzy Fast Finite-Time Output-Feedback Tracking Control for Switched Nonlinear Systems With Full-State Constraints

Discrete-Time Adaptive Fuzzy Event-Triggered Command Filtered Control for Induction Motors With Input Saturation

Discrete-Time Adaptive Fuzzy Event-Triggered Command Filtered Control for Induction Motors With Input Saturation

Game-Based Adaptive Fuzzy Optimal Bipartite Containment of Nonlinear Multiagent Systems

Game-Based Adaptive Fuzzy Optimal Bipartite Containment of Nonlinear Multiagent Systems

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization

Inverse Optimal Adaptive Fuzzy Output Feedback Control for Nonlinear Systems With Output Quantization

Robust Image-Based Adaptive Fuzzy Controller for Guarantee Field of View With Uncertain Dynamics

Robust Image-Based Adaptive Fuzzy Controller for Guarantee Field of View With Uncertain Dynamics

Adaptive Fuzzy Control of Underactuated Switched Systems With Disturbance Observation and Actuated/Unactuated Motion Constraints

Adaptive Fuzzy Control of Underactuated Switched Systems With Disturbance Observation and Actuated/Unactuated Motion Constraints