A practical fuzzy controllers scheme of overhead crane

This paper presents fuzzy-based design for the control of overhead crane.Instead of analyzing the complex nonlinear crane system,the proposed approach uses simple but effective way to control the crane.There are twin fuzzy controllers which deal with the feedback information,the position of trolley crane and the swing angle of load,to suppress the sway and accelerate the speed when the crane transports the heavy load.This approach simplifies the designing procedure of crane controller;besides,the twin controller method reduces the rule number when fulfilling the fuzzy system.Finally,experimental results through the crane model demonstrate the effectiveness of the scheme.     

Overhead cranes fuzzy control design with deadzone compensation

This article proposes a simple but effective way to control 3D overhead crane. The proposed method uses PID control at the start for rapid transportation and fuzzy control with deadzone compensation when the crane is close to the goal for precise positioning and moving the load smoothly. Only the remaining distance and projection of swing angle are applied to design the fuzzy controller. No plant information of crane is necessary in this approach. Therefore, the proposed method greatly reduces the computational efforts. Several experiments illustrated the encouraging effectiveness of the proposed method through a scaled 3D crane model. The nonlinear disturbance, such as abrupt collision, is also taken into account to check the robustness of the proposed method.     

Adaptive B-spline-based fuzzy sliding-mode control for an auto-warehousing crane system

In this paper, an adaptive B-splined-based fuzzy sliding mode control (ABFSM) is presented to the application of auto-warehousing crane motion control. The ABFSM comprises an adaptive fuzzy identification controller (AFIC) and a B-spline-based compensation controller (BCC). The AFIC is designed to approximate the ideal controller of a crane system. To alleviate the load of fuzzy rule base construction, only the information from the sliding surface is used as the input of AFIC such that the conciseness and translucency of the control system can be upgraded. On the other hand, the BCC aims to compensate the approximation error of the AFIC. With the introduction of the B-spline function, the boundary of the approximation error can be represented by means of polynomial mapping. Thus, the design of the compensation controller can be achieved based on the characteristics of the B-spline function. In this paper, the objective of the ABFSM is to track the distance-speed reference trajectory of the crane control system. With the tuning law of the AFIC and the BCC, the stability can also be guaranteed by means of Lyapunov function. To validate the performance of the proposed approach, the ABFSM is applied to auto-warehousing crane motion control under various conditions for x, y, and z directions, respectively. From the simulation the advantages of the proposed ABFSM are demonstrated, where the capability to handle the uncertainty with efficiency is verified.     

Vibration control of load for rotary crane system using neural network with GA-based training

A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

一类非线性系统的自适应滑模模糊控制

针对一类具有多个子系统的欠驱动非线性系统提出了一种自适应滑模模糊控制方法. 首先通过分析模糊控制与边界层滑模控制的相似性,提出了滑模模糊控制方法;然后根据滑模 面斜率和各子系统控制对于系统动态性能的影响,分别采用模糊推理根据系统状态自动地实时 调节滑模面斜率和各子系统在系统控制中的作用;最后通过简单的滑模模糊控制器实现对具有 多个子系统的欠驱动非线性系统的控制.将该方法应用于吊车的运输控制中,仿真结果证明了 其有效性和鲁棒性.     

Tracking and load sway reduction for double‐pendulum rotary cranes using adaptive nonlinear control approach

Because of the existence of rotational boom motion, the load sway characteristics is more complex. In particular, when the sway presents double‐pendulum phenomenon, the design of the controller is more challenging. Furthermore, the uncertain parameters and external disturbances in crane system make it difficult for traditional control methods to obtain satisfactory control performance. Hence, this paper presents an adaptive nonlinear controller based on the dynamic model of double‐pendulum rotary crane. Unlike a traditional method, the proposed one does not need to linearize the crane system for controller design; therefore, the control performance can be guaranteed even if the system states are far away from the equilibrium point. By using Lyapunov technique and LaSalle's invariance theorem, it is strictly proved that the whole control system is asymptotically stable at the equilibrium point. The effectiveness of the presented controller is demonstrated via comparative simulations.     

基于逆系统方法的内模控制在吊车消摆运动中的应用

针对非线性、欠驱动的桥式吊车水平运动系统，先运用非线性逆系统方法将其转化为伪线性系统，然后按照线性系统理论设计内模控制器来控制吊车的水平定位．同时设计了一个角度反馈控制器来确保摆角迅速衰减．最后和常规PID、模糊控制做了比较．实验结果表明，本方案能保证定位无静差、摆角迅速衰减，同时具有良好的抗干扰能力和较强的鲁棒性，控制效果优于PID和模糊控制．     

基于能量分析的桥式起重机防摆控制方法

针对欠驱动桥式起重机在自动化驾驶研究中负载升/落吊运动与台车水平位移联动时,负载摆动抑制效果和控制性能不能满足实际工程需要,易造成安全事故的问题,提出一种基于能量分析的桥式起重机联动系统非线性耦合防摆控制器.采用非线性耦合控制方法,构造新型储能函数,设计出非线性耦合防摆控制器.利用LaSalle不变性原理和Lyapunov方法对该闭环反馈系统稳定性进行严格的数学分析.理论推导、仿真与实验结果表明:相比于非线性跟踪控制器和局部反馈线性化控制器,所提非线性耦合防摆控制器具有更佳的控制性能,不仅提高了负载的吊运效率,而且能够有效抑制和快速消除负载摆角;在添加外部扰动的情况下,仍能取得良好的控制效果,具有较强的鲁棒性,为桥式起重机联动系统提供了一种新的防摆控制方法.     

模糊控制在双螺杆挤出压片机中的应用及MATLAB仿真实现

双螺杆挤出压片机的螺杆电机由于受到负载的非线性影响,使得压片机系统的鲁棒性较差;根据模糊控制器对参数变化不敏感,鲁棒性强的优点,采用模糊控制器来控制螺杆电机调速,可提高螺杆电机的鲁棒性,改良双螺杆挤出压片机的机器性能;通过MATLAB/Simulink仿真软件,建立一个带有模糊控制器的双螺杆挤出压片机螺杆直流电机控制系统模型;仿真试验表明,模糊控制器在电机调速中的应用是可行的;该方法改良了电机调速系统的性能,提高了压片机控制系统的适应能力和鲁棒性。     

Application of intelligent sliding mode control with moving sliding surface for overhead cranes

In this paper, neural-based fuzzy logic sliding mode control with moving sliding surface has been designed for supervision of an overhead crane. A mathematical model has been established of the crane, and equations of motion have been obtained. First, the suitable sliding surface coefficient has been determined for the fixed sliding surface in the design of sliding mode control. The sliding surface has been moved by using neural-based fuzzy logic algorithm to eliminate disadvantage of the regular sliding mode control. By application of this control algorithm, the control performance incredibly increased. In the application, during the carriage of the load to a target which was 1 m away by a crane with 3 kg of load and 100 cm of rope length, the parameters of effected controllers were updated and their training was realized. In order to display the insensitiveness of the controller to parametric uncertainty, the value of the load was taken as 8 kg and the length of the rope was taken as 3 m and controls for a different target were realized. MATLAB program was used for numerical solutions, and results were examined graphically. Obtained results displayed the success of the algorithm of neural-based fuzzy logic sliding mode control.     

A PLC-based modified-fuzzy controller for PWM-driven induction motor drive with constant V/Hz ratio control

In this paper, a method to develop and design a fuzzy-hybrid control on an industrial controller to control speed of an induction motor and implementing a constant V/Hz ratio control is presented. Detailed discussions on the controller for a PWM-driven induction motor drive system, the system identification for the model transfer function, and the analysis on output responses and the associated manipulated variables are presented. The control objective is to provide an effective control action to sudden changes in reference speed and/or load torque. A switching type controller consisting of two control modes are devised: a PID-type fuzzy controller consisting of a PI-type and a PD-type fuzzy controller, and a conventional PID. At the early phase of the control action, the control task is handled by the PID-type fuzzy controller. At a later phase when the absolute of error is less than a threshold value, the input of integrator at the output side is no longer given by fuzzy action but fed by the incremental PID action. In term of control action, this is an enhanced proportional and derivative action when the actual value is closed to reference. Detailed evaluations of the controller's performance based on pre-defined performance indices under several conditions are presented. The findings demonstrate the ability of the control approach to provide a viable control solution in response to the different operating conditions and requirements.     

起重机升降运动系统自适应Backstepping控制研究

通过分析起重机升降运动系统各组成部分的关联关系,建立了起重机升降运动系统数学模型,从而将电动机的运动状态和负载的运动状态有效地统一起来;以该模型为基础,将负载运动位置作为控制变量,采用自适应Backstepping控制策略设计了一种起重机升降运动系统负载位置控制器。仿真结果表明,该控制器具有较快的响应速度和较小的跟踪误差,能够有效抑制负载突变和参数摄动的影响,具有良好的鲁棒性。     

基于免疫遗传算法优化的集装箱桥吊防摇控制

桥吊系统存在严重的非线性,如果采用常规的PID控制器很难对其有较好的控制效果.可以采用基于遗传算法优化模糊控制器的方法对其控制,但采用普通遗传算法优化模糊控制器具有收敛慢的缺点,因此,本文提出了基于免疫遗传算法优化模糊控制的方法.仿真结果表明该控制器对桥吊防摇有很好的控制效果.且这种控制方法对于参数未知,时变负载扰动大的系统具有很好的工程实践价值.     

Hybrid Adaptive Type‐2 Fuzzy Tracking Control of Chaotic Oscillation Damping of Power Systems

In this paper a novel hybrid direct/indirect adaptive fuzzy neural network (FNN) moving sliding mode tracking controller for chaotic oscillation damping of power systems is developed. The proposed approach is established by providing a tradeoff between the indirect and direct FNN controllers. It is equipped with a novel moving sliding surface (MSS) to enhance the robustness of the controller against the present system uncertainties and unknown disturbances. The major contribution of the paper arises from the new simple tuning idea of the sliding surface slope and intercept of the MSS. This study is novel because the approach adopted tunes the sliding surface slope and intercept of MSS using two simple rules simultaneously. One advantage of the proposed approach is that the restriction of knowing the bounds of uncertainties is also removed due to the adaptive mechanism. Moreover, the stability of the control system is also presented. The proposed controller structure is successfully employed to damp the complicated chaotic oscillations of an interconnected power system, when such oscillations can be made by load perturbation of a power system working on its stability edges. Comparative simulation results are presented, which confirm that the proposed hybrid adaptive type‐2 fuzzy tracking controller shows superior tracking performance.     

Robust adaptive fuzzy control of unknown chaotic systems

This paper presents a robust adaptive fuzzy control algorithm for controlling unknown chaotic systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal controller, based on sliding-mode control. The robust controller is designed to compensate for the difference between the fuzzy controller and the ideal controller. The parameters of the fuzzy system, as well as uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the stability of the controlled system. Numerical simulations show the effectiveness of the proposed approach.     

Modeling and controlling the mobile harbour crane system with virtual prototyping technology

Virtual simulation of the real behaviour of mobile harbour crane (MHC) without using the traditional build-and-test method is an imperative approach to the design stage that can increase the quality of the product by reducing manufacturing cost and errors. This paper introduces an engineering model that describes the mechanical behaviour of MHC, and the control design for increasing the position accuracy. Based on a concept of the MHC, a virtual mechanical model was created using SOLIDWOKS, which was then exported to the Automatic Dynamic Analysis of Mechanical System (ADAMS) environment. This model was simulated to investigate the dynamic behaviour of the MHC system. In addition, an adaptive siding mode PID controller was also developed in MATLAB/Simulink to control the crane trolley position and suppress the swing angle of the load. This co-simulation demonstrates the reliability of the mechanical and control functionalities of the developed system.     

基于嵌套饱和方法的吊车系统非线性控制

针对吊车系统定位和防摆的控制要求,提出了一种基于嵌套饱和方法的非线性控制策略。对吊车系统动力学方程进行部分反馈线性化,并通过坐标变换将其转化为便于控制器设计的严格前馈级联规范型;在此基础上利用嵌套饱和非线性控制方法设计了吊车定位防摆控制器。仿真结果表明,该方法在较小的控制力作用下实现了吊车系统的定位和防摆,并且对于吊车系统参数的变化具有很强的鲁棒性。     

Robust adaptive fuzzy control of twin rotor MIMO system

This paper considers designing an adaptive fuzzy controller to position the yaw and pitch angles of a twin rotor MIMO system (TRMS) in two degrees of freedom. The goal of the controller is to stabilize the TRMS in a desired position or track a specified trajectory. The parameters of the fuzzy controller are updated using the gradient descent algorithm in order to increase its robustness against external disturbances and/or changes in system parameters. Moreover, the stability of the overall closed-loop system is guaranteed based on the Lyapunov stability theory. The proposed controller is applied to a TRMS with heavy cross coupling between its axes. Experimental results show good performance of the proposed controller as compared to the non-adaptive fuzzy and PID controllers, especially when there are system uncertainties and external disturbances.     

多变量模糊自校正控制器及其应用

本文介绍了表达ＭＩＳＯ动态系统的模糊模型，并提出了有关参数和结构的在线辨识算法。根据辨识的模糊模型，利用Ｃｌａｒｋｅ的单变量广义预测控制（ＧＰＣ）原理［１］设计了多变量模糊自校正控制器。仿真研究表明，该模糊自校正控制方法应用于火电机组负荷系统的控制，可以收到良好的效果。