Showing papers on "Fuzzy control system published in 2023"

Self-Evolving Recurrent Chebyshev Fuzzy Neural Sliding Mode Control for Active Power Filter

Abstract: A self-evolving recurrent Chebyshev fuzzy neural network (SERCFNN) approximator based on a fractional order sliding mode controller (FOSMC) is developed for an active power filter to suppress harmonic distortions. The self-evolving algorithm, which incorporates the structure learning with parameter learning, is able to dynamically adjust the number of fuzzy rules and the shape of fuzzy partitions. The consequent part of the proposed SERCFNN combines with Chebyshev polynomials to expand the dimensionality of the input. For relaxing the requirement of the parametric and functional certainty, a SERCFNN-based uncertainty approximator is utilized to dynamically approximate the compound unknown function, yielding an approximator-based FOSMC to tolerate extensive uncertainties. The approximator-based control law and parameter updating laws are obtained from the Lyapunov stability theory, which guarantee the designed control system is asymptotically stable. The control algorithm is implemented in a dSPACE-based experimental system to validate its feasibility, and the hardware experimental results confirm its superiority in harmonic compensation regardless of load disturbances.

Nonsingular Fixed-Time Fault-Tolerant Fuzzy Control for Switched Uncertain Nonlinear Systems

Abstract: This article investigates the adaptive fixed-time fault-tolerant tracking fuzzy control issue for nonlinear switched systems with dynamic uncertainties. Actuator faults considered in this article simultaneously contain the loss of effectiveness and time-varying bias fault depending on the switching signal. The generated scheme extends the fixed-time convergence to switched nonlinear systems with unmodeled dynamics. An improved adaptive fixed-time fault-tolerant controller is proposed by employing the backstepping method and fuzzy logic estimator. In particular, the presented framework removes the singularity, and the convergence time is assignable for any initial condition. Finally, a numerical simulation example and a resistor-capacitor-inductor circuit system example are given to prove the system output can converge to a desired trajectory within a fixed time.

Low-complexity Fuzzy Neural Control Of Constrained Waverider Vehicles Via Fragility-free Prescribed Performance Approach

Abstract: In this article, we propose a concise fuzzy neural control framework for Waverider Vehicles with input constraints, while the spurred prescribed performance can be guaranteed, and the challenging fragility problem associated with the existing prescribed performance control (PPC) is avoided. Unlike the existing control protocols without considering computational costs, in this study, the low-complexity fuzzy neural approximation is combined with simple performance functions, which reduces the complexity burden and improves the practicability. Then, in order to handle the adverse effect of the actuator saturation on the control performance, bounded-input-bounded-state stable systems are developed to stabilize the closed-loop control system based on bounded compensations. Specially, flexible adjustment terms are exploited to modify the developed simple performance functions, while fragility-free prescribed performance is achieved for tracking errors, and moreover the fragility defect of the existing PPC is remedied. Finally, the efficiency and superiority of the design are verified via compared simulations.

Distributed Fuzzy Optimal Consensus Control of State-Constrained Nonlinear Strict-Feedback Systems

Abstract: This article investigates the distributed fuzzy optimal consensus control problem for state-constrained nonlinear strict-feedback systems under an identifier-actor-critic architecture. First, a fuzzy identifier is designed to approximate each agent's unknown nonlinear dynamics. Then, by defining multiple barrier-type local optimal performance indexes for each agent, the optimal virtual and actual control laws are obtained, where two fuzzy-logic systems working as the actor network and critic network are used to execute control behavior and evaluate control performance, respectively. It is proved that the proposed control protocol can drive all agents to reach consensus without violating state constraints, and make the local performance indexes reach the Nash equilibrium simultaneously. Simulation studies are given to verify the effectiveness of the developed fuzzy optimal consensus control approach.

Modern Adaptive Fuzzy Control Systems

Abstract: This book explains the basic concepts, theory and applications of fuzzy systems in control in a simple unified approach with clear examples

Adaptive Fuzzy Asynchronous Control for Nonhomogeneous Markov Jump Power Systems Under Hybrid Attacks

Abstract: This article investigates the adaptive fuzzy asynchronous control problem for discrete-time nonhomogeneous Markov jump power systems under hybrid attacks. A nonhomogeneous Markov process is used to describe the phenomenon of transient failures occurring in power lines and subsequent switching of associated circuit breakers. The corresponding nonhomogeneous hidden Markov model is utilized to detect the jump modes of power systems. Both deception attack and denial-of-service attack are analyzed simultaneously owing to the vulnerability of power systems. With detected modes and fuzzy logic systems, an adaptive fuzzy asynchronous control strategy is proposed. Using the mode-dependent Lyapunov function, the existence conditions of the desired controller law are obtained such that the closed-loop power systems are bounded stable in the mean-square sense. Finally, the usefulness of the developed control strategy is demonstrated by a numerical example.

Adaptive Fuzzy Q-Learning Control Design and Application to Grid-Tied Nine-Level Packed E-Cell (PEC9) Inverter

Abstract: This letter applies a multiagent fuzzy Q-learning (FQL) algorithm, incorporated with a model-free nonlinear controller (MFNC), entitled FQL-MFNC for stabilized controlling of a recently introduced grid-connected nine-level Packed E-Cell (PEC9) inverter under dynamical operation. Unlike previous tuning schemes, which concentrate on extracting mathematical formulation of a controlled plant, this letter investigates a fuzzy Q-learning agent for optimal design of PEC9. In first step, the fuzzy reinforcement learning is adopted to tune the MFNC controller in the simulation environment. In fact, the FQL algorithm finds the optimal policy based on a reward function for adjustment of the MFNC control coefficients to guarantee the grid-connectivity requirements under PEC9 dynamical operation are met. The experimental tests are conducted to assure efficiency and practicability of the designed multi-agent FQL-MFNC scheme on the single-phase grid-connected PEC9 inverter.

An Event-Triggered Predefined Time Decentralized Output Feedback Fuzzy Adaptive Control Method for Interconnected Systems

Abstract: This article investigates the event-triggered predefined time output feedback control design problem for nonlinear interconnected systems with nonstrict feedback control structures. Compared with the existing event-triggered output feedback control design schemes, the most significant contribution of this article is that the system stability time can be preset directly. Fuzzy logic systems (FLSs) and FLSs-based state observer deal with unknown nonlinear dynamics and unmeasured states. Combining with dynamic surface control technology and event-triggered mechanism based on switching threshold strategy, an event-triggered predefined time decentralized output feedback control method is proposed, in which a predefined time filter is designed to solve the computational complexity problem. In addition, the algebraic loop problem and control singular problem are also solved, respectively, by applying the property of fuzzy basis function and L’Hospital’s rule. Utilizing the predefined time Lyapunov stability theory, the system stability analysis is given. Finally, the effectiveness and practicability of the proposed control method are verified by practical system simulation cases.

Observer-based event-triggered control for interval type-2 fuzzy networked system with network attacks

Abstract: This paper is focused on the adaptive event-based interval type-2 (IT-2) fuzzy security control problem for networked control systems with multiple network attacks. In order to save the network resources, this paper proposes an improved adaptive event-triggered model, which can adjust the threshold dynamically according to the change of current signal and previous triggering signal. In addition, due to the change of external environment, the impact of randomly occurring output bias is considered in the systems. Taking the effects of multiple network attacks and adaptive event-triggered scheme into consideration, a class of augmented error fuzzy system model is established. Next, some sufficient conditions are obtained for the stochastic stability with an $H_{\infty }$ performance index via the Lyapunov stability analysis method. The corresponding paraments of the IT-2 fuzzy controllers and observers are also derived by some linear matrix inequalities (LMIs). Finally, two simulation examples are given to support the advantages of the proposed method.

Fuzzy Boundary Control for Nonlinear Delayed DPSs Under Boundary Measurements

Abstract: For nonlinear delayed distributed parameter systems (DDPSs), this article considers a fuzzy boundary control (FBC) under boundary measurements (BMs). Initially, we accurately describe the nonlinear DDPS through a Takagi-Sugeno (T-S) fuzzy partial differential-difference equation (PDDE). Then, in accordance with the T-S fuzzy PDDE model, an FBC design under BMs ensuring the exponential stability for closed-loop DDPS is subsequently presented by spatial linear matrix inequalities (SLMIs) via using Wirtinger's inequality, Halanay's inequality, and the Lyapunov direct method, which respects the fast-varying and slow-varying delays. Moreover, we formulate SLMIs as LMIs for solving the fuzzy boundary controller design of nonlinear DDPSs under BMs. Finally, the effectiveness of the proposed FBC strategy is presented via simulation examples.

Convex Optimization-Based Adaptive Fuzzy Control for Uncertain Nonlinear Systems With Input Saturation Using Command Filtered Backstepping

Abstract: This article presents a modified command filter backstepping tracking control strategy for a class of uncertain nonlinear systems with input saturation based on the convex optimization method and the adaptive fuzzy logic system (FLS) control technique. First, the effect of complex uncertainties is eliminated by introducing n command filters and a single FLS. Then, the update laws of FLS weights are designed based on the convex optimization technique. Next, a new piecewise continuous function is employed to deal with the input saturation problem. The closed-loop system performance is also analyzed using the Lyapunov stability theorem and the Lasalle invariant principle. Finally, the simulation and experimental results are presented to show the effectiveness of our controller.

Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property

Abstract: This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

Finite-Time Adaptive Fuzzy Event-Triggered Output-Feedback Containment Control for Nonlinear Multi-Agent Systems with Input Saturation

Abstract: This paper considers the finite-time containment output-feedback control problem for a class of nonlinear multi-agent systems (MASs) with unmeasurable states and input saturation. Fuzzy logic systems (FLSs) and a smooth function are first employed to model unknown agent's subsystems and input saturation, respectively. Then, a novel fuzzy state observer is established via the intermittent output signal. By introducing first-order filter and using the sampled estimating states and triggered output signals, an event-triggered mechanism consisting of the sensor-to-controller and controller-to-actuator is formulated. Consequently, under the framework of the finite-time stability criterion and adaptive backstepping control design technique, a finite-time adaptive fuzzy event-triggered output-feedback containment control design method is proposed and the semiglobal finite-time stability of the controlled system is rigorously proved. Finally, the simulation results are given to confirm the effectiveness of the proposed control scheme.

Adaptive Fuzzy Fixed Time Time-Varying Formation Control for Heterogeneous Multiagent Systems With Full State Constraints

Abstract: This article presents an adaptive fuzzy fixed time time-varying formation control (TVFC) method for uncertain heterogeneous nonlinear multiagent systems (HNMASs) with full state constraints. Meanwhile, both partial loss of effectiveness and bias fault are considered in HNMASs. The fuzzy logic systems are selected as an effective tool to approximate uncertain nonlinear functions. The original constrained states of the systems will be converted to unconstrained states by the nonlinear transformed function. Compared with previous papers, it is the first time to handle the TVFC problem of HNMASs with full state constraints. In addition, formation control based on an adaptive fuzzy fixed time strategy not only ensures fast convergence of the system, but also the convergence time doesn't depend on any initial conditions. The stability of HNMAs is proven by the fixed time stability theory. Finally, a simulation is given to testify the effectiveness of the control method.

Multi-Area Load Frequency Control in Power System Integrated With Wind Farms Using Fuzzy Generalized Predictive Control Method

Abstract: Frequency security is critical for the power systems’ stability and reliability. The integration of renewable energy resources brings new challenges to the frequency security of power systems. To provide a more robust control method for controlling the frequency and tie-line power flow of an interconnected power system integrated with wind farms (IPSWF), a load frequency control (LFC) controller of generalized predictive control (GPC) based on the Takagi-Sugeno (T–S) fuzzy model (fuzzy-GPC) is proposed in this paper. First, the T–S fuzzy model of the IPSWF is constructed. Then, the GPC is used to design the controller. Lastly, to validate the proposed scheme, an LFC strategy is established for a two-area interconnected power system integrated with a wind farm in power systems computer aided design (PSCAD). The control performance of proportional-integral, GPC, and fuzzy-GPC controller are compared under two faulty operating conditions. The simulation results show that the performance indicators and the dynamic behavior when the fuzzy-GPC controller is used are better than the performance when the PI controller and GPC controller are used. The proposed fuzzy-GPC used in the LFC of the IPSWF can regulate the frequency deviation and tie-line power deviation adaptively and achieve minimum frequency deviation and tie-line power deviation in a multi-area IPSWF.

Fuzzy Adaptive Finite-Time Tracking Control for a Class of Nonlinear Systems: An Event-Triggered Quantized Control Scheme

Abstract: This brief proposes a fuzzy adaptive event-triggered quantized finite-time control (ETQFTC) scheme for uncertain strict-feedback nonlinear systems with full-state constraints. By means of state transformation, filtered backstepping technique and fuzzy adaptive control with the idea of minimum learning parameter (MLP), a $C^{1}$ ETQFTC scheme with low differential calculation and few parameter updates is developed. In the proposed scheme, the feasibility condition generally required in the existing state-constrained control, and the control chattering and singularity problem which are common in the existing FTC schemes are removed and avoided respectively. Finally, complete stability analysis and a set of comparative simulations are presented.

Algebraic Formulation and Application of Multi-Input Single-Output Hierarchical Fuzzy Systems With Correction Factors

Abstract: Hierarchical fuzzy systems (HFSs) are successfully applied to control systems and computer networks. Particularly, correction factor is introduced to regulate fuzzy rules and alleviate the problems of memory usage and real-time performance of systems. This article considers the multi-input single-output HFSs with correction factors, and proposes an algebraic framework by using the semitensor product of matrices. First, a kind of fuzzy correction factor matrix (FCFM) is constructed to realize the fuzzification of correction factor, based on which, an algebraic form is developed for two-input single-output fuzzy systems. Second, the structure diagram of HFSs is decomposed into several fuzzy logic units, which have the form of two-input single-output fuzzy systems with FCFM. Based on this operation, the algebraic formulation of serial, parallel and hybrid HFSs with correction factors is proposed. Finally, the effectiveness of HFSs with FCFM is verified by studying the on-ramp metering of freeway.

Particular-Function-Based Preassigned-Time Stability of Discontinuous System: Novel Control Scheme for Fuzzy Neural Networks

Abstract: In this article, the preassigned-time stability (PATS) problems are considered for a class of discontinuous dynamic systems via differential inclusion. By using some particular functions, including hyperbolic-tangent function and logistic function, the novel PATS theorems are proposed by means of the Lyapunov method. Based on developed PATS theorems, the preassigned-time stabilization control is realized for fuzzy switched neural networks (FSNNs) by designing a suitable control scheme, where the stabilization time does not depend on any system parameters and initial values of FSNNs. Meanwhile, the simulation examples are provided to verify the main results.

Protocol-Based Fuzzy Control of Networked Systems Under Joint Deception Attacks

Abstract: The security control issue of nonlinear networked systems is considered in this article on the basis of interval type-2 fuzzy modeling strategy. For avoiding communication congestion, a stochastic scheduling strategy called Markovian communication protocol is introduced to coordinate the sensor transmission order. An asynchronous observer is designed for estimating the unmeasured states via the hidden Markov model. In addition, a more comprehensive scenario on deception attack is considered, in which attacks occur both in the sensor–observer and controller–actuator communication channels with different types of deception signals. In view of slack matrix approach and stochastic analysis technique, some sufficient conditions for ensuring the ultimately boundedness of the resulting closed-loop system are obtained. In the end, simulations show the validity of the proposed protocol-based fuzzy control method.

Event-Triggered Input–Output Finite-Time Stabilization for IT2 Fuzzy Systems Under Deception Attacks

Abstract: This article addresses the issue of input–output finite-time stabilization for interval type-2 fuzzy systems in the presence of deception attack effects. Our main goal is to make efficient use of network resources by developing an event-triggered controller for interval type-2 fuzzy systems. For the stabilization process, an event-triggered controller that does not share the same membership functions as the system is designed by using affine transformation parameters. Following that, by using an asymmetric Lyapunov–Krasovskii functional and some advanced integral inequalities to establish the sufficient conditions for the existence of the proposed controller. Furthermore, an asymptotic stabilization result is presented and discussed in a comparative analysis as a special case. Due to the asymmetric Lyapunov–Krasovskii functional structure, the transmission delay interval is large compared with some recent studies. Finally, two simulation examples are carried out and the efficiency of designed controller is verified.

Probabilistic Regularized Extreme Learning for Robust Modeling of Traffic Flow Forecasting

Abstract: The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

General and Less Conservative Criteria on Stability and Stabilization of T-S Fuzzy Systems With Time-Varying Delay

Abstract: This paper deals with the problem of stability and stabilization of Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. First, a novel Lyapunov-Krasovskii functional is constructed, which is dependent on membership-functions and takes more information on the time-varying delay into account. Next, based on an $N$ -order free-matrix-based integral inequality and a switching method, a cluster of criteria on the stability and stabilization are obtained for the closed-loop system connected with switching state-feedback controllers. Then, a parameter tuning method and an iterative algorithm are devised to calculate control gains. Finally, three numerical examples including the truck-trailer system are given to show that the proposed criteria can offer less conservative results than some existing ones.

Practically Predefined-Time Adaptive Fuzzy Quantized Control for Nonlinear Stochastic Systems With Actuator Dead Zone

Abstract: This article focuses on the practically predefined-time adaptive fuzzy quantized control for nonlinear stochastic systems with actuator dead zone. Fuzzy logic systems are employed to approximate uncertain nonlinear functions. A novel stochastic predefined-time control scheme is proposed, which can help reduce the control parameters and increase the robustness of the closed-loop system. Taking the quantization and dead zone in the control link into account, the adaptive parameters and a part of the control are used to estimate and compensate the nonlinear disturbance, respectively. In addition, under reasonable assumptions, the complexity of the Lyapunov function compared with conventional stochastic adaptive control is reduced. Based on the stochastic predefined-time stabilization theory, an adaptive fuzzy controller is designed to make the upper bound of the expected settling time arbitrarily configured. Finally, two examples show the effectiveness of the main results.

Hierarchical Fuzzy Fault-Tolerant Controller Synthesis for Finite-Time Multitarget Surrounding of Networked Perturbed Mechanical Systems

Abstract: This article investigates the hierarchical synthesis of fuzzy fault-tolerant controllers for networked perturbed mechanical systems to solve the problem of finite-time multitarget surrounding, namely, encircling multiple moving targets in a finite time. Given the system with actuator failures, faults, and perturbations (namely, parametric uncertainties and external disturbances), we propose a hierarchical framework for synthesizing a cascade-form controller to stabilize the system state in a finite time, as well as decouple and solve the abovementioned problem. Specifically, the methods of fuzzy logic systems and finite-time adaptive laws are included within the synthesized controller to approximate the unknown perturbations, and meanwhile to compensate the negative effects of the actuator failures and faults. By employing the theories of perturbations and fault-tolerant analysis, we derive the sufficient conditions on control gains for finite-time convergence of the closed-loop system. Finally, we carry out several simulation experiments on a network of six 2-DOF robots and four moving targets to verify the performance of the synthesis method and the obtained controller.

Stability Analysis of Time-Varying Delay T–S Fuzzy Systems via Quadratic-Delay-Product Method

Abstract: The stability of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay is investigated in this article. First of all, a novel Lyapunov–Krasovskii functional (LKF) is proposed by fully utilizing single integral polynomial-delay-product terms and membership-function-dependent matrices, where more delay information is considered. Second, by introducing negative integral estimation inequalities and polynomial inequality, the estimation gap of derivatives is further decreased. As consequence, the criterion with less conservatism is presented. Finally, the examples are utilized for verifying the validity of the stability approach.

Further Results on Dissipativity Analysis for T–S Fuzzy Systems Based on Sampled-Data Control

Abstract: This article investigates the dissipative stability and stabilization problems of Takagi–Sugeno fuzzy systems by employing sampled-data control. First, in order to enhance the adaptability of the controller, the internal and external influence factors, such as communication time delay and aperiodic sampling pattern, are taken into consideration in the design process. Then, some new terms are proposed to construct the looped-functional-like Lyapunov functional, which can improve the dissipative performance, enlarge the admissible upper bound of the aperiodic sampling periods, and reduce the occupation of control signals in the communication channel. Next, based on the free-matrix-based integral inequalities, some novel dissipative criteria are presented in terms of linear matrix inequalities. Finally, the superiority of the provided criteria is demonstrated through a truck–trailer system.

Event-Triggered Cooperative Adaptive Fuzzy Control for Stochastic Nonlinear Systems With Measurement Sensitivity and Deception Attacks

Abstract: In this article, the leaderless adaptive fuzzy consensus control problem is studied for a class of stochastic nonlinear multiagent systems with unknown measurement sensitivity under false data injection attacks. Unknown measurement sensitivity and false data injection attacks can prevent sensors from obtaining right state information and make it difficult to design controllers and adaptive laws. The existing works considered only one of these cases for deterministic systems. In this article, the coexistence of both cases is considered with the help of Nussbaum functions and fuzzy logic systems, and the corresponding controllers and auxiliary variables are not only codesigned to solve the problem, but also extended to stochastic multiagent systems. Then, an improved switching threshold event-triggered mechanism is proposed to reduce the communication burden of the control channel. Furthermore, the leaderless asymptotic consensus control scheme for stochastic multiagent systems is proposed. The boundedness of all signals and leaderless asymptotic consensus control performance are guaranteed via the Lyapunov stability theorem. Finally, two simulation examples are given to verify the effectiveness of the proposed control scheme.

Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques

Abstract: In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance.