Hybrid self-organizing fuzzy and radial basis-function neural-network controller for gas-assisted injection molding combination systems

This study proposed a self-organizing fuzzy controller (SOFC) to manipulate a gas-assisted injection molding combination system (GAIMCS) and determined the control performance of the system. However, both the learning rate and the weighting distribution of the SOFC are difficult to select and are fixed after selection. To address this problem, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for GAIMCSs. The HSFRBNC uses a radial basis-function neural-network to regulate the parameters of the SOFC for achieving appropriate values in real time. It not only overcomes the difficulty of finding appropriate parameters of the SOFC but also reduces the time needed to establish suitable fuzzy control rules for manipulating the GAIMCS. Experimental results showed that the HSFRBNC has better control performance than the SOFC in controlling the GAIMCS.     

Evolutionary algorithms for fuzzy control system design

This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process, an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance index that specifies the desired control behavior. Genetic learning processes deal with the automated design of the fuzzy rule base. Their objective is to generate a set of fuzzy if-then rules that establishes the appropriate mapping from input states to control actions. We describe two applications of genetic-fuzzy systems in detail: an evolution strategy that tunes the scaling and membership functions of a fuzzy cart-pole balancing controller and a genetic algorithm that learns the fuzzy control rules for an obstacle-avoidance behavior of a mobile robot     

移动机器人路径跟踪模糊控制系统设计及仿真

本文的主要研究内容是模糊逻辑在移动机器人已知全局路径的情况下通过对局部路径的分析进行轨迹跟踪运动中的应用。利用模糊逻辑在移动机器人运动控制中的优越性,结合驾驶员的丰富经验设计了移动机器人的模糊控制器,包括一个预瞄距离确定器和一个运动模糊控制器。设计出移动机器人运动控制进行仿真的方法及程序流程,对其进行计算机仿真验证控制...     

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     

Hierarchical fuzzy force control for industrial robots

In this paper, we present a hierarchical force control framework consisting of a high-level control system based on fuzzy logic and the existing motion control system of a manipulator in the low level. In order to adapt various contact conditions, an adaptable fuzzy force control scheme has been proposed to improve the performance. The ability of the adaptable force control system is achieved by tuning the scaling factor of the fuzzy logic controller (FLC). A Mitsubishi MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical fuzzy force control system. Successful experiments for various contact motions are carried out. Additionally, discussion of a peg-in-hole insertion is presented, and the experimental results are given     

自平衡机器人的模糊-PD控制

由于自平衡机器人是一种两轮左右平行布置的机器人,像传统的倒立摆一样,它的本身是一个自然不稳定体,必须要施加强有力的控制手段才能使之稳定.为了提高两轮自平衡机器人设计的可靠性,针对不稳定、非线性、强耦合的两轮自平衡机器人系统,运用牛顿力学方法建立了转向运动的数学模型.在模型中建立了模糊控制器,并用模糊控制加比例控制的方法...     

Temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance     

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.     

A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control

Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified     

Balancing control of a unicycle robot with double gyroscopes using adaptive fuzzy controller

This paper presents a unicycle robot which utilizes the precession effect of a double-gyroscope for lateral balancing and designs an adaptive fuzzy controller to realize the balance control according to its dynamic model. The double gyroscope structure of the unicycle robot can eliminate the pitch angle interference caused by the precession effect and improve the robot's lateral anti-interference ability. An adaptive fuzzy controller is designed based on the dynamic equations of the unicycle robot to improve its robustness. The adaptive controller part improves the anti-interference ability of the unicycle robot, and the fuzzy controller part is used as decoupling controller to reduce the interference of coupling. Simulation and experimental results to verify the anti-interference ability and decoupling effect of the designed controller.     

Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments

In this paper, a neurofuzzy-based approach is proposed, which coordinates the sensor information and robot motion together. A fuzzy logic system is designed with two basic behaviors, target seeking and obstacle avoidance. A learning algorithm based on neural network techniques is developed to tune the parameters of membership functions, which smooths the trajectory generated by the fuzzy logic system. Another learning algorithm is developed to suppress redundant rules in the designed rule base. A state memory strategy is proposed for resolving the "dead cycle" problem. Under the control of the proposed model, a mobile robot can adequately sense the environment around, autonomously avoid static and moving obstacles, and generate reasonable trajectories toward the target in various situations without suffering from the "dead cycle" problems. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

Fuzzy temporal rules for mobile robot guidance in dynamicenvironments

The paper describes a fuzzy control system for the avoidance of moving objects by a robot. The objects move with no type of restriction, varying their velocity and making turns. Due to the complex nature of this movement, it is necessary to realize temporal reasoning with the aim of estimating the trend of the moving object. A new paradigm of fuzzy temporal reasoning, which we call fuzzy temporal rules (FTRs), is used for this control task. The control system has over 117 rules, which reflects the complexity of the problem to be tackled. The controller has been subjected to an exhaustive validation process and examples are shown of the results obtained     

基于视觉与行为模型的机器人目标跟踪

由于存在通信延时等问题，月球机器人必须具备一定智能来进行行为控制。Brooks提出机器人的包容结构理论重点在于强调机器人不同控制层间的联系，以及机器人不同行为功能的分配。本文提出一种视觉与行为模型，主要描述在同一控制层内视觉传感器与行为控制之间的关系，它将机器人的视觉行为与运动行为紧密联系起来，在运动的同时计算地面光流场变化来学习当前机器人动作状态，利用颜色信息的多窗口目标跟踪获取目标属性，最后采用强化学习方法，规划加器人动作．提高了运动控制的稳定度和精确席。     

移动机器人轨迹跟踪的模糊PID-P型迭代学习控制

本文针对移动机器人轨迹跟踪控制问题的研究,提出了一种基于移动机器人运动模型的模糊开闭环PID-P型非线性离散迭代学习控制方法,给出了PID-P型迭代学习的收敛条件及其证明过程,并采用模糊控制的原理整定PID三个学习增益矩阵的参数.该控制方法提高了移动机器人对特定轨迹的重复跟踪能力,具有算法实现简单的特点.实验仿真结果表明,采用模糊开闭环PID-P型迭代学习控制算法对轨迹跟踪是可行有效的.     

超声电机驱动的机器人的模糊神经网络控制

超声电机的小质量、大转矩、响应快等特点,是其成为小型直接驱动机器人执行器的基础。通过对超声电机的伺服特性研究及关节型机器人动力学分析,提出了超声电机驱动的机器人的模糊神经网络控制的复合控制方法,该方法中,模糊子控制器实现了关节定位的初步控制,神经网络子控制器起到降解稳态误差、提高控制精度作用。文中较详细地研究了复合控制器的结构及两个子控制器的设计问题。为了检验控制效果,对控制系统进行了仿真,结果表明,采用这种复合控制器可获得较高的位置精度。     

Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control

This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC.     

Fuzzy neural network-based real-time self-reaction of mobile robot in unknown environments

In this paper, a hybrid intelligent method including fuzzy inference and neural network is presented for real-time self-reaction of a mobile robot in unknown environments. A neural network with fuzzy inference (fuzzy neural network, FNN) presented can effectively improve the learning speed of the neural network. The method can be used to control a mobile robot based on the present motion situations of the robot in real-time; these situations include the distances in different directions between the obstacles and the robot provided by ultrasonic sensors, the target orientation sensed by a simple optical range-finder and the movement direction of the robot. Simulation results showed that the above method can quickly map the fuzzy relationship between the inputs and the output of the control system of the mobile robot.     

基于STM32的智能搬运机器人的研究与设计

针对老年人和行动不便者日常生活困难的问题,设计了一款可以进行基本日常操作的智能搬运机器人。机器人以STM32单片机为主控制器,通过红外线、超声波等传感器获取外界环境信息,对履带式行走机构和机械臂进行控制。履带结构较为平稳,具有良好的越障能力;机械臂自由度高,可完成360度全方位无死角的的抓取活动。本文使用Creo进行建模,实现机器人的运动仿真,优化机器人机械结构,借助Keil进行程序编译,解决机器人运动的算法问题。智能机器人利用多个传感器作为“感觉器官”,凭借稳定的履带行走机构和高自由度的机械臂,实现超声避障、智能循迹、定距抓取等多个功能。     

Position Synchronization Control Algorithm of Legged Robot Based on DSP Centralized Control

When the traditional RRT algorithm is used to control the position of the legged robot synchronously, its adaptability is poor and it is easy to be disturbed by the surrounding environment, which leads to the low stability of the robot when avoiding obstacles. The position synchronization control algorithm of the legged robot based on DSP centralized control is proposed. The parameter on-line fuzzy self-tuning PID algorithm is used in the position loop of the control system of the robot car controller to improve the adaptive ability of the robot. The inverse Fourier transform of cross power spectrum is used to determine the position and orientation angle of the robot, and the displacement theorem of the robot is used to control the position of the robot synchronously. The experimental results show that the proposed algorithm can effectively synchronize and control the robot running safely and accurately in obstacle-free environment. The average time is 18 s, the maximum robustness is 0.45, and the maximum control error rate is 0.25. It has the advantages of high control efficiency, high robustness and small error, which ensures the stable operation of the robot.