Intelligent control of a constant turning force system with fixed metal removal rate

The maintenance of a constant cutting force operation via control of the turning systems can increase the metal removal rate (MRR) and tool life. However, an increase in cutting depth reduces feed rate during the constant cutting force operation, resulting in lower productivity for the machine tool. To eliminate the problem, this study proposed an MRR scheme to assist a turning system in constructing a constant turning force system with fixed MRR. This study also presented a self-organizing fuzzy controller (SOFC) for manipulating such a system to maintain a constant turning force operation and improve the productivity of the machine tool. Nevertheless, it is difficult to select a suitable learning rate and an appropriate weighting distribution for the design of an SOFC. To overcome the difficulty, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for such turning systems. The HSFRBNC uses a radial basis function neural-network to adjust in real time the learning rate and the weighting distribution parameters of the SOFC to appropriate values, rather than obtaining the parameters by trial and error. This strategy solves the problem of determining appropriate parameters for designing an SOFC. Simulation results showed that the HSFRBNC achieved better control performance than the SOFC when it came to the control of a constant turning force system with fixed MRR.     

Design of a grey-prediction self-organizing fuzzy controller for active suspension systems

Self-organizing fuzzy controllers (SOFCs) have excellent learning capabilities. They have been proposed for the manipulation of active suspension systems. However, it is difficult to select the parameters of an SOFC appropriately, and an SOFC may extensively modify its fuzzy rules during the control process when the parameters selected for it are inappropriate. To eliminate this problem, this study developed a grey-prediction self-organizing fuzzy controller (GPSOFC) for active suspension systems. The GPSOFC introduces a grey-prediction algorithm into an SOFC, in order to pre-correct its fuzzy rules for the control of active suspension systems. This design solves the problem of SOFCs with inappropriately chosen parameters. To evaluate the feasibility of the proposed method, this study applied the GPSOFC to the manipulation of an active hydraulic-servo suspension system, in order to determine its control performance. Experimental results demonstrated that the GPSOFC achieved better control performance than either the SOFC or the passive method of active suspension control.     

Stability indices for a self-organizing fuzzy controlled robot: A case study

This study developed a model-free self-organizing fuzzy controller (SOFC) for manipulating multiple-input multiple-output systems. The SOFC has an online learning algorithm that can continually update fuzzy rules during the control process, beginning from an empty rule table. The SOFC was used to control a three-link robot with a complex dynamic model in order to evaluate its applicability. Stability and robustness of the SOFC were demonstrated using a state-space approach. Simulation results confirmed that the control performance of the SOFC outperforms that of the fuzzy logic controller for the control of the robot.     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

The SOF-PID controller for the control of a MIMO robot arm

The application of a self-organizing fuzzy proportional-integral-derivative (SOF-PID) controller to a multiple-input-multiple-output (MIMO) nonlinear revolute-joint robot arm is studied in this paper. The SOF controller is a learning supervisory controller, making small changes to the values of the PID gains while the system is in operation. In effect, the SOF controller replaces an experienced human operator, which otherwise would have readjusted the PID gains during the system operation. The three PID gains are tuned using classical tuning techniques prior to the application of the SOF controller at the supervisory level. Two trajectories of step input and path tracking were applied to the SOF-PID controller at the setpoint. For comparison purposes, the same experiments were repeated by using the self-organizing fuzzy controller (SOFC) and the PID controller subject to the same information supplied at the setpoint. For the step input, the SOF-PID controller produced a aster rise time, a smaller steady state error, and an insignificant overshoot than the SOFC and the PID controller. For the path tracking experiments, better results were obtained.     

A self-organizing fuzzy logic controller for dynamic systems usinga fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum     

Self-organizing fuzzy controller for injection molding machines

Most plastic parts are manufactured using an open-loop injection molding machine (IMM). However, this process produces parts with poor dimensional precision and may result in product shrinkage, residual stress and other effects depending on the complexity of parts’ geometric shape, size, or a combination of both. To improve the performance of the IMM, in this study, we modified the IMM’s control structure from open-loop to closed-loop. The IMM has clearly complicated and nonlinear characteristics. Thus, it is difficult to design model-based controllers for manipulating the IMM. To solve the problem, a model-free self-organizing fuzzy controller (SOFC) was developed to control the IMM and its control performance was evaluated. Experimental results demonstrated that the SOFC exhibits better control performance than the fuzzy logic controller or the proportional-integral-derivative (PID) controller in controlling the injection-screw velocity of the IMM and the injection-nozzle holding pressure of the IMM.     

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.     

一种用于非线性控制的神经网络模糊自组织控制器

本文提出一种神经网络自组织控制器，并应用于非线性跟踪控制中，为了加快模糊控制器的在线学习，文中给出了一种变的最速梯度下降学习算法，仿真结果表明，该控制是有效的。     

机械臂自调整因子模糊PD迭代学习控制研究

大部分模糊控制器不具有适应控制对象变化的能力,基于此设计一种自调整因子模糊控制器,并针对机械臂长时间重复操作导致运动精确度下降这一类问题,结合迭代学习控制方法,提出一种自调整因子模糊PD迭代学习控制方法;以双关节机械臂为研究对象,利用Fuzzy工具箱编写模糊控制规则,通过系统产生的误差以及误差的变化率作为模糊控制器的输入量调整模糊系统中的量化因子和比例因子,实现模糊规则的更新和对迭代学习控制中的PD参数的实时调整,系统的自适应性得到提高,并在Simulink中进行机械臂的运动控制实验,仿真结果表明,所提控制方法最终产生的误差可以精确到0.0001 rad,同时在进行第2次迭代时关节角度和角速度误差收敛基本趋于零,整体的控制效果较好。     

On-line constructive fuzzy sliding-mode control for voice coil motors

In this paper, a voice coil motor (VCM) featuring fast dynamic performance and high position repeatability is developed. To achieve robust VCM control performance under different operating conditions, an on-line constructive fuzzy sliding-mode control (OCFSC) system, which comprises of a main controller and an exponential compensator, is proposed. In the main controller, a fuzzy observer is used to on-line approximate the unknown nonlinear term in the system dynamics with on-line structure learning and parameter learning using a gradient descent algorithm. According to the structure learning mechanism, the fuzzy observer can either increase or decrease the number of fuzzy rules based on tracking performance. The exponential compensator is applied to ensure the system stability with a nonlinear exponential reaching law. Thus, the chattering signal can be alleviated and the convergence of tracking error can be speed up. Finally, the experimental results show that not only the OCFSC system can achieve good position tracking accuracy but also the structure learning ability enables the fuzzy observer to evolve its structure on-line.     

基于可变学习率BP算法的空调控制系统的研究

通过分析控制器参数学习率和控制器性能之间的关系,设计一种基于可变学习速率反向传播算法VLRBP和模糊神经元网络的变频空调控制系统.该系统不仅可以通过反传误差信号训练控制器参数,而且可以根据网络的当前状态朝最优化方向调整控制器参数的学习率.实验结果表明,该控制系统不仅比传统的空调PID控制器和模糊控制器具有更好的控制性能,而且相比基于标准BP算法和动量BP算法的模糊神经网络控制系统,也具有更快的收敛速度和更好的控制精确度.     

Robust iterative learning controller design using the performance weighting function of feedback control systems

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the ?₂-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the ?₂-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.     

A self-organizing fuzzy logic controller for the active control of flexible structures using piezoelectric actuators

This paper proposes an on-line self-organizing fuzzy logic controller (FLC) design applied to the control of vibrations in flexible structures containing distributed piezoelectric actuator patches. In this methodology, the fuzzy rules are generated using the history of input/output (I/O) pairs without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. The validity of the proposed fuzzy logic control has been demonstrated experimentally in a steel cantilever test beam and a set of experimental tests are made in the system to verify the efficiency of the on-line self-organizing fuzzy controller.     

Observer-Based Relaxed ${{cal H}}_{infty }$ Control for Fuzzy Systems Using a Multiple Lyapunov Function

This short paper proposes a method of designing a fuzzy observer-based H _infin controller for discrete-time Takagi-Sugeno (T-S) fuzzy systems. To enhance the applicability of the output-feedback controller and improve its performance, this short paper first builds a set of fuzzy control rules with premise variables different from those of the T-S fuzzy system, and sets the overall controller to be dependent on not only the current time but also the one-step-past information on the estimated fuzzy weighting functions. Then, based on the fuzzy control rules, this short paper establishes a less conservative H _infin stabilization condition incorporated with a multiple Lyapunov function dependent on the estimated fuzzy weighting functions. Through a two-step design procedure, the H _infin stabilization condition is formulated in terms of parameterized linear matrix equalities (PLMIs), which are reconverted into LMIs with the help of an efficient and effective relaxation scheme.     

Adaptive fuzzy learning control for a class of nonlinear dynamic systems

This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed‐loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc.     

双模自组织自整因子模糊控制器在PMLSM精密伺服系统中的应用

就PMLSM(永磁直线同步电动机)伺服系统,应用新型的自组织自整因子模糊控制器。它可以在经验知识十分缺乏的情况下自动形成良好的控制表,同时改善控制器的动态和静态特性。使用无静差的双模设计,提高了伺服系统动态响应速度和稳态精度。实验证明,该伺服系统具有自组织、自学习能力强、快速跟踪、定位精确、鲁棒性强等特点。     

Adaptive fuzzy controller with sliding surface for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate model for designing a model-based controller. Here, an adaptive fuzzy sliding mode controller is proposed to suppress the sprung mass position oscillation due to road surface variation. This intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms. It has online learning ability to deal with the system time-varying and nonlinear uncertainty behaviors, and adjust the control rules parameters. Only eleven fuzzy rules are required for this active suspension system and these fuzzy control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control algorithm effectively suppresses the oscillation amplitude of the sprung mass with respect to various road surface disturbances.