Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.     

Adaptive Fuzzy Sliding Mode Control Algorithm for a Non-Affine Nonlinear System

This paper concerns the design of robust controller for a nonlinear system that can be represented or approximated in a non-affine form. The control algorithm is based on sliding mode control that incorporates a fuzzy tuning technique, and it superposes equivalent control, switching control, and fuzzy control. An equivalent control law is firstly designed based on a nominal system model that was obtained by using curve fitting techniques under MATLAB. Switching control is then added to guarantee that the state reaches the sliding surface in the presence of parameter and disturbance uncertainties. Also, fuzzy tuning schemes, which can be supported by learning techniques derived from neural networks, are employed to improve control performance and to reduce chattering in the sliding mode. To verify the performance of this controller, an experimental platform of a pneumatically actuated top-guided single-seated control valve, which belongs to a classical complex nonlinear system, was constructed. Also, the experimental results show that high performance and attenuated chatter are achieved and thus verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.     

Robust H ∞ Control of a Class of Switching Nonlinear Systems with Time-Varying Delay Via T–S Fuzzy Model

This paper considers H _∞ control of a class of switching nonlinear systems with time-varying delays via T–S fuzzy model based on piecewise fuzzy weighting-dependent Lyapunov–Krasovskii functionals (PFLKFs). The systems are switching among several nonlinear systems. The Takagi and Sugeno (T–S) fuzzy model is employed to approximate the sub-nonlinear dynamic systems. Thus, with two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce a continuous-time switched fuzzy systems, which inherently contain the features of the switched hybrid systems and T–S fuzzy systems. Average dwell-time approach and PFLKFs methods are utilized for the stability analysis and controller design, and with free fuzzy weighting matrix scheme. Switching and control laws are obtained such that the H _∞ performance is satisfied. The conditions of stability and the control laws are given in the form of LMIs which can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible. A numerical example and the control of an uncertain radio-controlled (R/C) hovercraft with time-varying delay are given to demonstrate the efficiency of the proposed method.     

基于区间二型模糊神经网络的高分辨率遥感影像分割方法

高分辨率遥感影像同质区域地物目标异质性增大,光谱测度空间复杂性增加使像素类属的不确定性以及分割决策不确定性增大,引起分割精度下降。提出一种基于区间二型模糊神经网络的高分辨率遥感影像监督分割方法。对同质区域构建一型高斯隶属函数模型刻画像素类属的不确定性;模糊化高斯隶属函数参数构建区间二型模糊模型处理分割决策的不确定性;以训练样本在所有类别中的一型模糊隶属度及上、下隶属度为输入,建立模糊神经网络模型并融入像素邻域关系作为模糊决策。采用文中算法、FCM方法、HMRF-FCM及区间二型模糊神经网络方法分别对合成影像及真实高分辨遥感影像进行分割,定性与定量的对比分析验证了文中算法具有更高的分割精度。      

区间二型模糊C均值聚类在图像分割中的应用

聚类分析是非监督模式识别的重要分支,模糊C均值聚类算法(FCM)是其中的一类经典算法,然而该算法以一型模糊集为基础,无法处理数据集以及算法中的不确定性,为此引入区间二型模糊C均值聚类算法(IT2FCM)。二型模糊集处理不确定性的能力强于一型模糊集,基于二型模糊集的IT2FCM在处理不确定性时效果优于FCM算法。文章以图像分割为应用对象,比较IT2FCM和FCM算法的分割效果,实验证明IT2FCM较传统FCM有更好的抗噪性。     

Design and experimental research of observer-based adaptive type-2 fuzzy steering control for automated vehicles with prescribed performance

Steer-by-Wire (SbW) system is a significant electromechanical subsystem of automated vehicles. This paper proposes an observer-based type-2 fuzzy control method for the SbW system with uncertain nonlinearity, unknown modeling parameters, and unavailable state. First, an interval type-2 fuzzy logic system (IT2 FLS) and an IT2 FLS-based state observer are constructed to estimate the uncertain nonlinearity and unavailable state of SbW systems. Then, a prescribed performance control (PPC) method is proposed to achieve the prescribed tracking performance of SbW systems. Much importantly, a modified performance function is incorporated in this control method, such that the prescribed tracking performance can be guaranteed within a finite time regardless of the initial state. Finally, simulation and vehicle experiments are given to verify the effectiveness and superiority of the proposed methods.     

Quadratic Stabilization of Uncertain Discrete-Time Switched Systems via Output Feedback

Quadratic stabilization of discrete-time switched systems with norm-bounded time-varying uncertainties is studied. A robust switching rule is proposed to stabilize switched systems by using a designed switched static or dynamic output feedback controller. All the switching rules adopted are constructively designed and state dependent, and they do not rely on any uncertainties.     

通讯资源受限网络系统的非均匀采样切换控制

针对含有通道资源受限和量化器的网络控制系统难于控制的问题,提出了基于切换原理的输出反馈控制器设计和动态调度方法.考虑到介质访问约束的影响,利用开关调度矩阵将通信受限的网络化控制系统,转化为含有多个子系统的非均匀采样的切换系统.利用Lyapunov稳定性理论推导出系统鲁棒镇定的充分条件,设计了可以满足任意切换稳定的最优鲁棒控制器和最优动态调度器.最后,通过仿真实例验证了所提出方法的有效性.     

Fuzzy combination of linear state-feedback and switching controllers

A fuzzy controller, which is a fuzzy combination of linear state-feedback and switching controllers, is proposed for nonlinear systems subject to parameter uncertainties. By proper design of the proposed fuzzy controller, the chattering effect near the origin can be eliminated. The global system stability is also guaranteed.     

Robust Stabilization of Complex Switched Networks With Parametric Uncertainties and Delays Via Impulsive Control

In this paper, a general complex switched network (CSN) model is presented. The model is more general than those in the literatures in which it contains switching behaviors on both its nodes and topology configuration. Robust stabilization of directed time-varying CSN with parametric uncertainties and two types of delays is investigated. The two types of delays consist of the system delay at each node and the coupling delay between nodes. Based on the Lyapunov stability theory, sufficient robust stabilization conditions are proposed for CSNs via impulsive control. In addition, four special stabilization cases: CSNs with both system and coupling delays, CSNs with parametric uncertainties and either the system delay or the coupling delay, and complex networks with parametric uncertainties and both type of delays, are discussed. A systematic design procedure for stabilizing impulsive control is presented. A numerical example is provided for illustration. A comparative study of the stability ranges of the impulsive intervals corresponding to the general case of the directed time-varying CSN and the four special cases is carried out by simulation.     

Fuzzy sliding-mode controllers with applications

This paper concerns the design of robust control systems using sliding-mode control that incorporates a fuzzy tuning technique. The control law superposes equivalent control, switching control, and fuzzy control. An equivalent control law is first designed using pole placement. Switching control is then added to guarantee that the state reaches the sliding mode in the presence of parameter and disturbance uncertainties. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The practical application of fuzzy logic is proposed here as a computational-intelligence approach to engineering problems associated with sliding-mode controllers. The proposed method can have a number of industrial applications including the joint control of a hydraulically actuated mini-excavator as presented in this paper. The control hardware is described together with simulated and experimental results. High performance and attenuated chatter are achieved. The results obtained verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties     

Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems

This paper presents a connection admission control (CAC) method that uses a type-2 fuzzy logic system (FLS). Type-2 FLSs can handle linguistic uncertainties. The linguistic knowledge about CAC is obtained from 30 computer network experts. A methodology for representing the linguistic knowledge using type-2 membership functions and processing surveys using type-2 FLS is proposed. The type-2 FLS provides soft decision boundaries, whereas a type-1 FLS provides a hard decision boundary. The soft decision boundaries can coordinate the cell loss ratio (CLR) and bandwidth utilization, which is impossible for the hard decision boundary.     

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach     

Identification of pneumatic artificial muscle manipulators by a MGA-based nonlinear NARX fuzzy model

This study investigates the technique of modeling and identification of a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, there are difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model. All these factors deteriorate the identification performance when dealing with dynamic nonlinear industrial processes. To overcome these difficulties, this paper proposes a novel approach by using a modified genetic algorithm (MGA) combined with the predictive capability of nonlinear ARX (NARX) model for generating the dynamic NARX Takagi–Sugeno (TS) fuzzy model. The MGA algorithm processes the experimental input–output training data from the real system and optimizes the NARX fuzzy model parameters. This is referred to as fuzzy identification, which automatically generates the appropriate fuzzy if-then rules to characterize the dynamic nonlinear features of the real plant. The prototype pneumatic artificial muscle (PAM) manipulator, being a typical nonlinear and time-varying system, is used as a test system for this novel approach. This result shows that, with this MGA-based modeling and identification, the novel NARX fuzzy model identification approach to the PAM manipulator achieved highly outstanding performance and high precision as well. The accuracy of the proposed MGA-based NARX fuzzy model proves excellent in comparison with the MGA-based TS fuzzy model and the conventional GA-based TS fuzzy model.     

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.     

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.     

One wheel vehicle real world control based on interval type 2 fuzzy controller

In this study, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is used for a single-wheel vehicle (SWV) control problem. A control technique for use by a person riding on an SWV that enables real-world balance control of electric unicycles is proposed. The proposed SWV can keep balance and move around. Moreover, it can also stand up stably in any position when a person rides on it without touching the ground. AIT2FC is an advanced version of the traditional Mamdani-like fuzzy controller. The AIT2FC parameters are adjusted online using an intelligent dynamic tuning method. Moreover, the parameter learning rates of the adaptive laws are determined using a stability algorithm to guarantee the system error convergence. Furthermore, an SWV based on a microcontroller with some handmade hardware circuits is implemented. Finally, the efficiency of the AIT2FC is proved by real-time control of SWVs.     

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.     

基于切换原理的非均匀采样系统输出反馈控制

在非均匀采样系统中,存在着刷新时间间隔不确定和时变的情况,这给系统控制器的设计造成了很大困难.针对此问题,本文将刷新时间间隔看作时延变量,将不同时延的系统动态变化过程转化为不同子系统的动态变化过程,从而将时延的变化转化为不同子模型之间的切换,最终将非均匀采样系统描述为一类具有有限个子系统的离散时间切换系统.利用输出反馈控制方法,基于平均驻留时间条件,使输出反馈闭环非均匀采样系统指数稳定.最后,通过一个非均匀采样系统仿真实例证明了提出方法的有效性.     

Adaptive Fuzzy Control of Nonlinear in Parameters Uncertain Chaotic Systems Using Improved Speed Gradient Method

This paper presents an adaptive fuzzy controller for Nonlinear in Parameters (NLP) chaotic systems with parametric uncertainties. In the proposed controller, the unknown parameters are estimated by the novel Improved Speed Gradient (ISG) method, which is a modification of Speed Gradient (SG) algorithm. ISG employs the Lagrangian of two suitable objective functionals for on-line estimation of system parameters. The most significant advantage of ISG is that it is applicable to NLP systems and it results in a faster rate of convergence for the estimated parameters than the SG method. Estimated parameters are used to design the fuzzy controller and to calculate the Lyapunov exponents of the chaotic system adaptively. Furthermore, established on the well-known Takagi–Sugeno (T-S) fuzzy model, a LMI (Linear Matrix Inequality)-based fuzzy controller is designed and is tuned using estimated parameters and Lyapunov exponents. Throughout the controller design procedure, several important issues in fuzzy control theory including relaxed stability analysis, control input performance specifications, and optimality are taken into account. Combination of ISG parameter estimation method and T-S-based fuzzy controller yields an adaptive fuzzy controller capable to suppress uncertainties in parameters and initial states of NLP chaotic systems. Finally, simulation results are provided to show the effectiveness of the ISG and adaptive fuzzy controller on chaotic Lorenz system and Duffing oscillator.