Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

Fuzzy adaptive force control of industrial robot manipulators with position servos

A fuzzy adaptive force control algorithm is suggested for commercialized industrial robots equipped with position servo drives in cascade with a software filter for the control of acceleration/deceleration profile to avoid vibrational shocks due to a sudden start or stop, where the control algorithm is composed of a Fuzzy Interpolation Logic Controller (FILC) and a Fuzzy Adaptive Stiffness Estimator (FASE FILC determines a control action according to the magnitude of an environmental stiffness in such a way that good force response is maintained regardless of changes of environmental stiffness. Specifically, some fuzzy controllers are designed for several representative environmental stiffness values, and then a control action for an estimated environmental stiffness value which is not the same as any representative stiffness values is decided by fuzzily aggregating different control actions of those fuzzy controllers. Here, FASE plays the role of estimating an environmental stiffness value and transfers the estimated stiffness value to FILC. To show the validity of the proposed adaptive fuzzy force controller, several numerical examples and some experimental results are illustrated, where soft, medium and hard environments are considered.     

An adaptive fuzzy sliding-mode controller for servomechanismdisturbance rejection

A two-level spring-lumped mass servomechanism system was constructed for disturbance rejection control investigation. This dynamic absorber is similar to a model of the serial-type vehicle suspension system. The lower level is actuated by two DC servo motors, to provide the specified internal and external disturbances to the vibration control system. The upper level has another DC servo motor to control the main body balancing position. In order to tackle the system's nonlinear and time-varying characteristics, an adaptive fuzzy sliding-mode controller is proposed to suppress the main mass position variation due to external disturbance. This intelligent control strategy combines an adaptive rule with fuzzy and sliding-mode control technologies. It has online learning ability for responding to the system's time-varying and nonlinear uncertainty behaviors, and for adjusting the control rules and parameters. Only seven rules are required for this control system, and its control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control approach effectively suppresses the vibration amplitude of the body, with respect to the external disturbance     

Intelligent position/force controller for industrial robotmanipulators-application of fuzzy neural networks

An intelligent controller, which consists of an intelligent planner and an adaptive fuzzy neural position/force controller, is proposed for a robot manipulator. The proposed controller deals with the human expert knowledge and skills for planning and control. In this paper, it is applied to the task of deburring with an unknown object. The effectiveness of the proposed controller is evaluated by computer simulations     

Fuzzy adaptive speed control of a permanent magnet synchronous motor

A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.     

Adaptive control of uncertain nonlinear teleoperation systems

Kinematic parameters of a robotic manipulator are hard to measure precisely and the varying size and shape of tools held by the robot end-effector introduce further kinematic uncertainties. Moreover, the exact knowledge of the robot nonlinear dynamics may be unavailable due to model uncertainties. While adaptive master–slave teleoperation control strategies in the literature consider the dynamic uncertainties in the master and the slave robots, they stop short of accounting for the robots’ kinematic uncertainties, which can undermine the transparency of the teleoperation system. In this paper, for a teleoperation system that is both dynamically and kinematically uncertain, we propose novel nonlinear adaptive controllers that require neither the exact knowledge of the kinematics of the master and the slave nor the dynamics of the master, the slave, the human operator, and the environment. Therefore, the proposed controllers can provide the master and slave robots with a high degree of flexibility in dealing with unforeseen changes and uncertainties in their kinematics and dynamics. A Lyapunov function analysis is conducted to mathematically prove the stability and master–slave asymptotic position tracking. The validity of the theoretical results is verified through simulations as well as experiments on a bilateral teleoperation test-bed of rehabilitation robots.     

Soft pneumatic actuators adapted in multiple environments: A novel fuzzy cascade strategy for the dynamics control with hysteresis compensation

Soft pneumatic actuators (SPAs) have been widely used in the design of various soft robots due to their compliance, adaptability, and high force density characteristics. However, it is a challenge to accurately model and control such soft pneumatic robotic systems due to inherent hysteresis nonlinearity, uncertainties, and disturbances from external environments. In this paper, we propose a novel fuzzy cascade strategy to control the dynamics of bellow-type soft pneumatic actuators when working in multiple environments (air, water, and their transition process). First, the components of the soft pneumatic system including the actuator and solenoid valve are mathematically modeled using second-order transfer functions, which are derived with a system identification method. Then, the Prandtl-Ishlinskii (P-I) model is proposed to accommodate and characterize the complex hysteresis effect. In the P-I model, the parameters are identified and derived using a particle swarm optimization (PSO) method. Subsequently, an inverse P-I model is constructed and placed in the feed-forward path to compensate for the hysteresis effect. In addition to the hysteresis nonlinearity, the uncertainties and disturbances from multiple environments will also degrade the tracking performance of soft pneumatic actuators. To enhance the adaptability, especially during the trans-environment process (e.g., from air into water or the reverse), a single-input FUZZY P+ID controller is proposed and integrated into the cascade strategy aiming to improve the robustness and precisely control the system dynamics. Extensive simulations and real-world tracking experiments of soft pneumatic actuators fabricated with the fused deposition modeling (FDM) method are performed to evaluate the performance of the proposed strategy and three designed controllers (PID, fuzzy PID, and FUZZY P+ID). It is noted that the comparison of tracking results has proved that the proposed FUZZY P+ID controller with only single input has better overall performance than traditional PID and fuzzy PID controllers in terms of adaptability and robustness.     

薄膜材料的绕组处理的适应失真压力的控制系统

薄膜材料的绕组处理是在一个高度非线性的动态系统中维持应力不变.提出一个为薄膜材料的绕组处理在不同摩擦锟供料速度下的适应模糊应力的控制系统.该提出的适应模糊应力的控制系统包括一个模糊应力控制器和一个适应调谐器.模糊应力控制器是主进度控制器,一个平移宽度的概念和变化模式技术被包括在模糊推论中以矫正模糊现象,而且只有一个参数因素需要被调整.为了对抗在实际应用中的不确定,一个失真压力的控制系统占据着简单控制框架,无震颤的,稳定跟踪性能和对不确定性的鲁棒的优势.与传统的比例积分应力控制方法相比较可提出的这种控制方法有显著的优势.     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

Vision-based nonlinear tracking controllers with uncertainrobot-camera parameters

This paper considers the problem of position tracking control of planar robot manipulators via visual servoing in the presence of parametric uncertainty associated with the robot mechanical dynamics and/or the camera system. Specifically, by assuming exact knowledge of the mechanical parameters, we design an adaptive camera calibration controller that compensates for uncertain camera parameters and ensures global asymptotic position tracking. We then develop an adaptive robot controller that accounts for parametric uncertainty throughout the entire robot-camera system while producing global asymptotic position tracking. Experimental results illustrating the viability of the adaptive controllers and extensions regarding robust control and redundant robot manipulators are also included     

Dissipativity-based adaptive and robust control of UPS in unbalanced operation

In this paper, we investigate the output voltage control for three phase uninterruptible power supply (UPS) using controllers based on ideas of dissipativity. To provide balanced sinusoidal output voltages even in the presence of nonlinear and unbalanced loads, we first derive a dissipativity-based controller using a conventional /spl alpha//spl beta/ (fixed frame) representation of system dynamics and a frequency-domain representation of system disturbances. Adaptive refinements have been added to the controller to cope with parametric uncertainties. Second, based on the structure of the first adaptive controller, we propose another controller that leads to a linear time-invariant (LTI) closed loop system which is directly connected to synchronous frame harmonic voltage control. This controller, denoted as robust, avoids the most computationally demanding parameter estimation during adaptation, and offers important advantages for implementation. For the proposed robust controller, a sufficient condition in terms of the design parameters is presented to guarantee stability of the desired equilibrium and robustness against certain parametric uncertainties. Finally, simulation and experimental results on a three-phase prototype show effectiveness and advantages of the proposed class of controllers.     

Adaptive integral robust control of hydraulic systems with asymptotic tracking

In this paper, an adaptive integral robust controller is developed for high accuracy motion tracking control of a double-rod hydraulic actuator. We take unknown constant parameters including the load and hydraulic parameters, and lumped unmodeled disturbances in inertia load dynamics and pressure dynamics into consideration. A discontinuous projection-based adaptive control law is constructed to handle parametric uncertainties, and an integral of the sign of the extended error based robust feedback term to attenuate unmodeled disturbances. Moreover, the present controller does not require a priori knowledge on the bounds of the lumped disturbances and the gain of the designed robust control law can be tuned itself. The major feature of the proposed full state controller is that it can theoretically guarantee global asymptotic tracking performance with a continuous control input, in the presence of various parametric uncertainties and unmodeled disturbances such as unmodeled dynamics as well as external disturbances via Lyapunov analysis. Comparative experimental results are obtained for motion control of a double-rod hydraulic actuator and verify the high-performance nature of the proposed control strategy.     

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.     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

Adaptive robust control of fully-constrained cable driven parallel robots

In this paper, adaptive robust control (ARC) of fully-constrained cable driven parallel robots is studied in detail. Since kinematic and dynamic models of the robot are partly structurally unknown in practice, in this paper an adaptive robust sliding mode controller is proposed based on the adaptation of the upper bound of the uncertainties. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The proposed controller not only keeps all cables under tension for the whole workspace of the robot, it is chattering-free, computationally simple and it does not require measurement of the end-effector acceleration. The stability of the closed-loop system with proposed control algorithm is analyzed through Lyapunov second method and it is shown that the tracking error will remain uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed control algorithm is examined through some experiments on a planar cable driven parallel robot and it is shown that the proposed controller is able to provide suitable tracking performance in practice.     

Experimental design of a fuzzy controller for improving power factor of boost rectifier

This article presents the design and the implementation of dSPACE DS1104 controller board-based PI and fuzzy logic peak current-mode controllers in the voltage loop and two controllers in the current loop based first on a standard fixed hysteresis band control, followed by a variable hysteresis band control to achieve constant switching frequency for a single-phase active power factor corrector in the continuous conduction mode. All these controllers have been verified via simulation in Simulink and a real-time implementation is performed on an experimental test bench utilising a rapid prototyping tool. The controllers are experimentally compared for steady-state performance and transient response. It is shown that the PI and fuzzy logic controllers give a superior steady-state performance, whereas the fuzzy logic inference based controller can achieve better dynamic response than its PI counterpart under large load disturbance and plant uncertainties. Furthermore, the variable hysteresis band control in the current loop gives a low total harmonic distortion of the input current compared to a standard fixed hysteresis band control.     

Use of adaptive fuzzy systems in parameter tuning of sliding-modecontrollers

The paper proposes to combine adaptive fuzzy systems with sliding-mode control (SMC) to solve the chattering problem of SMC for robotic applications. In the design of the controller, special attention is paid to chattering elimination without a degradation of the tracking performance. Furthermore, the a priori knowledge required of the system dynamics for design is kept to a minimum. The paper first considers the basic principles of sliding-mode and fuzzy controllers. Implementation difficulties and most popular solutions are then overviewed. Next, the design of a SMC reported in the literature is outlined and guidelines for the selection of controller parameters for the best tracking performance without chattering are presented. A novel approach based on the introduction of a "chattering variable" is developed. This variable, as a measure of chattering, is used as an input to an adaptive fuzzy system responsible for the minimization. Online tuning of parameters by fuzzy rules is carried out for the SMC. The experimental results obtained are given, and conclusions are presented     

变论域自适应模糊控制器的CMOS模拟 电路芯片实现

为解决传统的自适应模糊控制器算法过于复杂难以用模拟电路实现的问题,本文研究了输入输出论域可随输入变量的变化而自适应变化的在线自适应模糊控制器及其在非线性系统控制中的应用,并制作了CMOS模拟电路芯片.提出了一种新的尖三角形隶属度函数实现输入变论域的功能,输出变论域部分采用对输入变量进行加权积分并求其绝对值的方法.控制器的其他部分为求小电路和重心法去模糊电路.以上各电路均为CMOS模拟电路,它们和集成的整体电路均在无锡上华(CSMC) 0.6μm工艺下流片,测试结果表明该芯片完成了变论域模糊控制器的功能.     

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.