Development of a Fuzzy Rule-Based Multi-Objective Hybrid Controller for Static Synchronous Compensator

Static synchronous compensator (Statcom) is a powerful new device for power systems, which can be used for various purposes. The multi-objective demands are quite different in nature, e.g. continuous linear control for voltage maintaining, and discrete bang-bang control for oscillation damping. Unfortunately, they often conflict with each other. In this respect, a supplementary damping control together with an independent voltage control is normally used. However, inevitable small disturbance and uncertainties will cause problems in the coordination of the two functions. To overcome such difficulties, a fuzzy rule-based hybrid controller is proposed in this paper, which incorporates conventional linear voltage control along with the fuzzy rule-based supplementary power damping control to form a unified global controller for Statcom. Because only simple fuzzy rules and a few input signals are involved, it is very easy to implement in a practical power system. The simulation performed on a Single- Machine-Infinite-Bus (SMIB) power system and an actual large power system demonstrates the effectiveness of the proposed approach.     

一种改进遗传算法在抽油机节能器中的应用

提高油田抽油机的电能利用率一直是人们关心的一个热点。传统的方法是采用双模控制技术（bang—bang和PID控制相结合）,但由于模型的不确定性等因素,难以获得满意的控制效果。为此该文设计了一种基于改进遗传算法的PID模糊控制器,通过检测抽油机的电压与电流之间的相位差,实时构建不同工况条件下的系统模型,采用改进遗传算法与模糊控制相结合的技术,优化PID整定参数。仿真和实验表明,控制器具有很好的控制效果。     

交流伺服系统模糊滑模控制器设计

针对交流伺服系统的位置控制，设计出一种新型滑模控制器，以前馈信号作为平均控制，采用模糊推理来调节开关控制的幅度，仿真研究表明，该控制器能较好地实现对指令信号的跟踪，具有较强的抗参数摄动和抗干扰能力，表现出良好的控制性能。     

The describing functions for a constrained-range integration process with bang-bang input and dead zone

Recently, considerable attention has been devoted to the analysis of high-order systems containing severe nonlinearities separated by linear functions, particularly those using bang-bang or on-off controllers. These have proven to be very satisfactory in the attitude control systems of manned or unmanned spacecraft and satellites. This paper develops describing functions for a particularly complicated multiple nonlinearity: a tri-stable (bang-bang with dead zone) characteristic, followed by a linear integrator with a constrained range of integration. It should be noted that constraining range of integration is not equivalent to simple limiting of an integrator's output. This system of nonlinearities has not previously been treated in the literature, although it is founds for example, in satellite attitude controllers where on-off torques give rise to constrained momentum wheel angular velocities or in guided missile hydraulic actuators with bang-bang power spool and control surface position limits. This frequency-variant nonlinearity has three distinct modes of operation and, therefore, is quite different from the usual single nonlinearity considered for describing function application. The describing function and boundary equation for each mode are derived in the paper, and numerical examples are given. The analytical results were found to agree well with results from an analog computer simulation. These describing functions may be used to size power actuators ands therefores should be very useful for preliminary systems design.     

Fuzzy sliding-mode control with rule adaptation for nonlinear systems

Abstract: A fuzzy sliding-mode control with rule adaptation design approach with decoupling method is proposed. It provides a simple way to achieve asymptotic stability by a decoupling method for a class of uncertain nonlinear systems. The adaptive fuzzy sliding-mode control system is composed of a fuzzy controller and a compensation controller. The fuzzy controller is the main rule regulation controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the adaptive fuzzy controller. Fuzzy regulation is used as an approximator to identify the uncertainty. The simulation results for two cart–pole systems and a ball–beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for a tunnelling robot manipulator are given to demonstrate the effectiveness of the system.     

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.     

Switching fuzzy controller design based on switching Lyapunov function for a class of nonlinear systems

This paper presents a switching fuzzy controller design for a class of nonlinear systems. A switching fuzzy model is employed to represent the dynamics of a nonlinear system. In our previous papers, we proposed the switching fuzzy model and a switching Lyapunov function and derived stability conditions for open-loop systems. In this paper, we design a switching fuzzy controller. We firstly show that switching fuzzy controller design conditions based on the switching Lyapunov function are given in terms of bilinear matrix inequalities, which is difficult to design the controller numerically. Then, we propose a new controller design approach utilizing an augmented system. By introducing the augmented system which consists of the switching fuzzy model and a stable linear system, the controller design conditions based on the switching Lyapunov function are given in terms of linear matrix inequalities (LMIs). Therefore, we can effectively design the switching fuzzy controller via LMI-based approach. A design example illustrates the utility of this approach. Moreover, we show that the approach proposed in this paper is available in the research area of piecewise linear control.     

Self-learning of a fuzzy controller using cell mapping in the sense of a quadratic optimum

A self-learning method based on the concept of cell mapping is presented to design automatically a fuzzy controller in the sense of a quadratic optimum for an unknown system. The construction of the system's cell mapping is directly obtained from experimental data instread of numerical calculations from system equations. The continuous state and control spaces of the system are first divided into many discrete fuzzy cells. A series of learning signals is applied to excite the system for training, and then all the states and control signals are gathered in a data buffer. Then, the real-valued data in the buffer are transformed to the fuzzy-valued cell chains which consist of the next state cell, initial control cell, quadratic cost and degree for optimization. A fuzzy control table is created to store these fuzzy cell chains with less cost and higher degree and is optimized through accumulated learning experience. Finally, the well learned fuzzy control table is used as an optimal fuzzy controller to transfer globally the system from any initial state cell to the target state cell. Once the state reaches the target state cell, the controller is switched from the fuzzy controller to a state feedback controller to the system within the target state cell.     

Direct adaptive fuzzy control for a class of MIMO nonlinear systems

This article presents a direct adaptive fuzzy control scheme for a class of uncertain continuous-time multi-input multi-output nonlinear (MIMO) dynamic systems. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller that can achieve control objectives. The adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. The stability analysis of the closed-loop system is performed using a Lyapunov approach. In particular, it is shown that the tracking errors are bounded and converge to a neighborhood of the origin. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance.     

三自由度机械臂的继电式控制

本文介绍了一个具有三个自由度机器人手臂的继电式控制器的设计方法。通过公式,表达了该机器人手臂的数学模型、继电式控制器和其电气拖动装置的特征。利用VAX11/750计算机进行继电式控制器开关时间的计算,以及手臂移动的模拟。而利用现有的M6800单板机作继电式控制器,对一台试验性机器人手臂进行了实际的控制试验。这台试验性机器人手臂同数学模型机是相似的。这个试验,证实了实际结果同模型机器人的计算结果是相当一致的。     

Fuzzy sliding mode control for an under-actuated system with mismatched uncertainties

This article presents a robust fuzzy sliding mode controller. The methodology of sliding mode control provides an easy way to control under-actuated nonlinear systems with uncertainties. The structure of the sliding surface is designed as follows. First, decouple the entire system into second-order systems so that each subsystem has a separate control target expressed in terms of a sliding surface. Second, from the sliding surface of subsystems, organize the main sliding surface system. Third, generate a control input for the main sliding surface to make whole subsystems move toward their sliding surface. A fuzzy controller is used to obtain a smooth boundary layer to the sliding surface. Finally, the fuzzy sliding mode controller presented is used to control an under-actuated nonlinear system, and confirms the validity of the proposed approach and its robustness to uncertainties.     

Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties

A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.     

Stable fuzzy neural tracking control of a class of unknown nonlinear systems based on fuzzy hierarchy error approach

In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzy neural controller is constructed from the fuzzy neural network with a set of fuzzy rules. The corresponding network parameters are adjusted online according to the control law and update law for the purpose of controlling the plant to track a given trajectory. A stability analysis of the unknown nonlinear system is discussed based on the Lyapunov principle. In order to improve the convergence of the nonlinear dynamical systems, a fuzzy hierarchy error approach (FHEA) algorithm is incorporated into the adaptive update and control scheme. The simulation results for an unstable nonlinear plant demonstrate the control effectiveness of the proposed adaptive fuzzy neural controller and are consistent with the theoretical analysis.     

一类不确定多输入模糊双线性系统的鲁棒H∞控制

针对一类带有参数不确定性和干扰的多输入模糊双线性系统(FBS)的鲁棒H_∞控制问题,使用并行分布补偿算法(PDC)设计了模糊控制器,得到了整个模糊控制系统鲁棒全局稳定的充分条件,控制器的设计可以通过求解一系列线性矩阵不等式(LMI)获得.仿真例子验证了方法的有效性.     

Self-tuning of 2 DOF control based on evolving fuzzy model

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters.The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.     

通信网络系统中解决信息拥塞的模糊控制方法

针对网络系统中信息拥塞问题, 提出了一个全新的解决方案——用模糊控制的方法来解决通信网络中信息拥塞问题, 以国内外大量文献的理论为基础, 通过对计算机通信网络信息传输中相关量的分析, 构造出应用于网络信息传输中的模糊控制器, 仿真分析了模糊控制器在多种不同情况下的控制效果, 并与已有的算法进行了比较, 结果表明该模糊控制器具有较好的适应性和较强的鲁棒性.     

Decoupled fuzzy sliding-mode control

A decoupled fuzzy sliding-mode controller design is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear systems with only five fuzzy control rules. The ideas behind the controller are as follows. First, decouple the whole system into two second-order systems such that each subsystem has a separate control target expressed in terms of a sliding surface. Then, information from the secondary target conditions the main target, which, in turn, generates a control action to make both subsystems move toward their sliding surface, respectively. A closely related fuzzy controller to the sliding-mode controller is also presented to show the theoretical aspect of the fuzzy approach in which the characteristics of fuzzy sets are determined analytically to ensure the stability and robustness of the fuzzy controller. Finally, the decoupled sliding-mode control (SMC) is used to control three highly nonlinear systems and confirms the validity of the proposed approach     

移动焊接机器人及混合控制

针对目前焊接系统自动化程度低、控制方式单一等特点，介绍了一种采用旋转电弧传感器的移动焊接机器人系统，给出了相关部分的原理和结构；针对该机器人，设计了含多种控制方法的混合控制器。整个系统采用模糊控制、PI控制、bang-bang控制相结合的方法，对控制策略进行了分析，通过实验论证了系统整体的性能。     

A multivariable predictive fuzzy PID control system

In this paper, a novel multivariable predictive fuzzy-proportional-integral-derivative (F-PID) control system is developed by incorporating the fuzzy and PID control approaches into the predictive control framework. The developed control system has two main units referred as adaptation and application parts. The adaptation part consists of a F-PID controller and a fuzzy predictor. The incremental control actions are generated by the F-PID controller. The controller parameters are adjusted with the predictive control approach. The fuzzy predictor provides the multi-step ahead predictions of the plant outputs. Therefore, the F-PID controller parameters are adjusted by minimizing the errors between the predicted plant outputs and reference trajectories over the prediction horizon. The fuzzy predictor is trained with an on-line training procedure in order to adapt the changes in the plant dynamics and improve the prediction accuracy. The Levenberg–Marquardt (LM) optimization method with a trust region approach is used to adjust both the controller and predictor fuzzy systems parameters. In the application part, an identical F-PID controller of the adaptation part is used to control the actual plant. The adjusted parameter values are transferred to this identical controller at each time step. The performance of the proposed control system is tested for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) nonlinear control problems. The adaptation, robustness to noise, disturbance rejection properties together with the tracking performances are examined in the simulations.