Robust control of a spatial robot using fuzzy sliding modes

In order to improve the performance of the sliding mode controller, fuzzy logic sliding mode controller is proposed in this study. The control gain of the conventional sliding mode controller is tuned by a fuzzy logic rule base and, also dynamic sliding surfaces are obtained by changing their slopes using the error states of the system in another fuzzy logic algorithm. These controllers are then combined in order to enhance the performance. Afterwards, proposed controllers were used in trajectory control of a three degrees of freedom spatial robot, which is subjected to noise and parameter variations. Finally, the controllers introduced are compared with a PID controller which is commonly used for control of robotic manipulators in industry. The results indicate the superior performance of the proposed controller.     

Design of fuzzy logic controllers based on generalized T-operators

Since Zadeh first proposed the basic principle of fuzzy logic controllers in 1968, the and operators have been popular in the design of fuzzy logic controllers. In this paper, the general concept of T-operators is introduced into the conventional design methods for fuzzy logic controllers so that a general and flexible methodology for the design of these fuzzy logic controllers is available. Then, by computer simulations, studies are made so as to determine the relations between the various T-operators and the performance of a fuzzy logic controller. It is concluded that the performance of the fuzzy logic controller for a given class of plants very much depends upon the choice of the T-operators.     

Some aspects of synthesis of probabilistic fuzzy controllers

This paper deals with some aspects of analysis and design problems of a class of control algorithms called probabilistic fuzzy controllers.The notion of random intersection coefficient is proposed here.The decomposition of a multi-dimensional probabilistic fuzzy controller is discussed as well. Such decomposition provides uniform expressions for both single-input, single-output and multi-dimensional probabilistic fuzzy controllers and it improves the computational efficiency.As an example, a two-input and two-output control system is presented as well.     

城市主干道交通信号灯模糊线控制的探讨

本文首先提出城市交通系统线控制的两级递阶结构：第一级，用模糊逻辑控制器确定单路口交通信号灯的周期和绿信比；第二级，用模糊相位控制器确定相邻两路口的相位差；两级间用模糊转换开关协调；然后，提出具体的实现方法。     

A general fuzzy control scheme for nonlinear processes with stability analysis

In this paper we develop a general fuzzy control scheme for nonlinear processes. Assuming little knowledge about the dynamics of the controlled process, the proposed scheme starts by probing the process at different points in its operating region to generate a fuzzy quantisation. A simple local controller is then designed at each fuzzy locality. A fuzzy inference mechanism then links up tje local controllers to form a global controller which can be further refined by the learning algorithm. By employing a newly developed structure-adaptive fuzzy modelling scheme, the appropriate fuzzy rule-base for the inference mechanism can be extracted stably and efficiently. The conditions for the stability of the global controller are rigourously established. Simulation results are presented to illustrate the effectiveness of the scheme.     

Fuzzy speed control and stability analysis of a networked induction motor system with time delays and packet dropouts

Connecting a spatially distributed system with sensors, actuators, and controllers as a networked control system by a shared data network can reduce the wiring and cost remarkably. Networked control strategy has been utilized in remote operation of linear systems. Nonlinearity is the major barrier in implementing a networked control scheme on an induction motor, which is the most widely used motor in industrial applications. In this case, we designed a sliding mode flux observer to linearize the induction motor model, such that the application of the networked control scheme is feasible. Due to the variable QoS, a fuzzy logic speed controller is proposed to adapt various network conditions. As part of the networked controller, a state predictor is designed to compensate the time delay in the feedback channel. In stability analysis, the upper bounds of time delays and packet dropouts are both given in terms of the Lyapunov theorem. Finally, simulations are conducted employing TrueTime toolbox to demonstrate the effectiveness of the control strategy.     

基于观测器的模糊Delta算子系统的控制器设计

对于非线性模糊系统控制器和观测器的分析和设计,提出一种统一方法。利用Delta域离散T—S模糊模型对非线性系统建模,并基于李雅普诺夫稳定性理论给出模糊状态反馈控制器和观测器的设计策略,将所得结果归结为求解一组线性矩阵不等式。同时结论表明:分离性原理对Delta算子T—S模糊系统仍然成立。所得结果可将现有关于连续和离散T—S模糊系统的相关结论统一于Delta算子框架内。     

直流电机电流控制中两种模糊控制器的比较

提供了两种直流电机模糊控制器,其中一个采用了极大反模糊器而另一个采用了中心平均模糊器,我们研究了这两种模糊控制器对直流电机进行控制的过程和结果,最后得出了第二种控制器优于第一种控制器。     

Neuro-fuzzy technique for navigation of multiple mobile robots

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning, Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of sensors for measuring the distances of obstacles around it and an image sensor for detecting the bearing of the target. It is found that the FLC having Gaussian membership function is best suitable for navigation of multiple mobile robots. Then a hybrid neuro-fuzzy technique has been designed for the same problem. The neuro-fuzzy technique being used here comprises a neural network, which is acting as a pre processor for a fuzzy controller. The neural network considered for neuro-fuzzy technique is a multi-layer perceptron, with two hidden layers. These techniques have been demonstrated in simulation mode, which depicts that the robots are able to avoid obstacles and reach the targets efficiently. Amongst the techniques developed neuro-fuzzy technique is found to be most efficient for mobile robots navigation. Experimental verifications have been done with the simulation results to prove the authenticity of the developed neuro-fuzzy technique.     

Robust fuzzy control of a class of fuzzy bilinear systems with time-delay

This paper presents robust fuzzy controllers for a class of T–S fuzzy bilinear systems (FBSs) with time-delay. First, we adopt the parallel distributed compensation (PDC) method to design a fuzzy controller to stabilize the T–S FBS with time-delay. The stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities (LMIs). Secondly, we propound some LMI conditions to set up the robust controller to stabilize the uncertain T–S FBS with time-delay. Finally, the validity and applicability of the proposed schemes are demonstrated by simulations.     

Adaptive fuzzy sliding mode control of the cart-pole underactuated system

In this work, an intelligent control scheme is proposed for the stabilization of the cart-pole underactuated system. The adopted approach is primarily based on a smooth sliding mode controller, but an adaptive fuzzy inference system is embedded within the boundary layer in order to improve the control performance. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A new hybrid algorithm based on optimal fuzzy controller in multimachine power system

In this article, a new methodology based on fuzzy proportional‐integral‐derivative (PID) controller is proposed to damp low frequency oscillation in multimachine power system where the parameters of proposed controller are optimized offline automatically by hybrid genetic algorithm (GA) and particle swarm optimization (PSO) techniques. This newly proposed method is more efficient because it cope with oscillations and different operating points. In this strategy, the controller is tuned online from the knowledge base and fuzzy interference. In the proposed method, for achieving the desired level of robust performance exact tuning of rule base and membership functions (MF) are very important. The motivation for using the GA and PSO as a hybrid method are to reduce fuzzy effort and take large parametric uncertainties in to account. This newly developed control strategy mixed the advantage of GA and PSO techniques to optimally tune the rule base and MF parameters of fuzzy controller that leads to a flexible controller with simple structure while is easy to implement. The proposed method is tested on three machine nine buses and 16 machine power systems with different operating conditions in present of disturbance and nonlinearity. The effectiveness of proposed controller is compared with robust PSS that tune using PSO and the fuzzy controller which is optimized rule base by GA through figure of demerit and integral of the time multiplied absolute value of the error performance indices. The results evaluation shows that the proposed method achieves good robust performance for a wide range of load change in the presents of disturbance and system nonlinearities and is superior to the other controllers. © 2014 Wiley Periodicals, Inc. Complexity 21: 78–93, 2015     

Applications of an IMC based PID Controller tuning strategy in atmospheric and vacuum distillation units

Applications of internal model control (IMC) based single loop controller tuning in atmospheric and vacuum distillation units were investigated. The robust IMC-PID controller not only inherits the virtues that the IMC controller has, but also has a simple and general structure such as that of a PID controller. Tuning and optimization of controllers becomes more convenient using the IMC-PID controller. It can also become easier to achieve in a distributed control system (DCS) via control module configuration. In order to make it easier to apply in industrial processes, the modeling problem of the industrial process should be resolved. In this paper, a convenient closed-loop system identification strategy based on new Luus-Jaakola (NLJ) algorithm was presented, meanwhile, the principle of IMC-PID was interpreted. A software package was developed, capable of collecting actual data on-line, obtaining the process model and optimizing the parameters of the controllers. It was applied in an atmospheric and vacuum distillation unit of a refinery to tune the PID parameters of all controllers. The application results demonstrate the validity of the proposed method.     

Long-range predictive adaptive fuzzy relational control

A class of long-range predictive adaptive fuzzy relational controllers is presented. The plant behavior is described over an extended time horizon by a fuzzy relational model which is identified based on input-output closed-loop observations of the plant variables. In this class of adaptive controllers the control law attempts to minimize a quadratic cost over an extended control horizon. When used with linear models, this approach has revealed a significant potential for overcoming the limitations of one-step ahead schemes, such as the stabilization of non-minimum phase plants. Here, a uniform framework is adopted for implementing both the fuzzy model and the fuzzy controller, namely distributed fuzzy relational structures gaining from their massive parallel processing features and from the learning capabilities typical of the connectivist approaches. Issues such as maintenance during the adaptation process of the meaning of linguistic terms used at both fuzzy systems interfaces are addressed, namely by introducing a new design methodology for on-line fuzzy systems interface adaptation. The examples presented reinforce the claim of the usefulness of this new approach.     

Minimizing the number of variable parameters for optimizing the fuzzy-controller

In the case of a few input and output variables fuzzy systems have a large number of variable parameters which make the practical design and optimization of fuzzy controllers more difficult. It is necessary to reduce the number of variable parameters to simplify the design of fuzzy controllers and to make it accessible to automated design methods. In this paper, the response characteristics and the quality of fuzzy controllers were analysed by using different variable parameters. The quality of a controller is evaluated by the deformation of the characteristic field under consideration of a similarity criterion and the Fourier analysis. It is shown that the reduction in the number of variable parameters does not necessarily result in a restriction of the quality of the fuzzy controller.     

Fuzzy control of thyristor-controlled series compensator in power system transients

Power system transient stability is one of the most challenging technical areas in electric power industry. Thyristor-controlled series compensation (TCSC) is expected to improve transient stability and damp power oscillations. TCSC control in power system transients is a nonlinear control problem. This paper presents a T–S-model-based fuzzy control scheme and a systematic design method for the TCSC fuzzy controller. The nonlinear power system containing TCSC is modelled as a fuzzy “blending” of a set of locally linearized models. A linear optimal control is designed for each local linear model. Different control requirements at different stages during power system transients can be considered in deriving the linear control rules. The resulting fuzzy controller is then a fuzzy “blending” of these linear controllers. Quadratic stability of the overall nonlinear controlled system can be checked and ensured using H^∞ control theory. Digital simulation with NETOMAC software has verified that the fuzzy control scheme can improve power system transient stability and damp power swings very quickly.     

模糊Delta算子系统的鲁棒镇定

研究一类基于Delta算子描述的T-S模糊模型状态反馈镇定设计问题。首先将全局模糊模型按隶属函数划分成若干子空间,并被表示成不确定系统的形式;采用分段Lyapunov函数法,得到鲁棒稳定化控制律存在的充分条件．该条件被进一步等价表示成一组线性矩阵不等式的可解性问题。克服了以往设计法中需要求解一公共正定矩阵P的不足,也无需求解繁琐的Riccati方程。所得结果可将连续和离散模糊系统的有关结论统一到Delta算子框架内。     

Hardware Implementation of Fuzzy PID Controllers

For traditional hardware implementation of fuzzy PID controllers, it is large at computation and bad in real-time performance, so, a kind of PID control algorithm, whose gain parameters could be tuned by their fuzzy system, was selected as studying example for a novel idea of hardware implementation. In this paper, authors presented hardware network of memory address mapping to implement fuzzy PID control algorithm, and designed the corresponding hardware system. The idea actually realizes fusion of hardware and intelligent algorithm. The implementation effectively simplified hardware circuits, the whole controller is very simple without CPU. Meanwhile, it is very easy to use, only connecting the sensor/transducer, the driver and the actuator is OK. The controller is very rapid in response, it need only two A/D conversion periods for outputting a required control signal. So the implementation could meet real-time performance effectively.     

Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem

The use of fuzzy logic has, in the last twenty years, become standard practice in the field of control. The reason lies in the fuzzy logic’s ability to relatively quickly transfer uncertain experience and knowledge about the observed object’s behaviour into the process of decision making. Nevertheless, one of the biggest problems that arises when using a fuzzy approach is the large number of fuzzy rules that have to be processed in order to produce one decision (i.e. one control output). The number of rules in a fuzzy controller primarily originates from the number of input variables that are entering the decision process and one possible solution for decreasing it is to use the method of decomposition. Its main goal is to implement the equivalent control functionality with a hierarchy of simpler fuzzy controllers. Their main characteristic is a lower number of input variables, which as a consequence leads to a smaller number of fuzzy rules. In our paper we apply the decomposition approach to the classical complex control case of the Truck-and-Trailer (T&T) reverse parking control problem. In such cases the implementation of control using only one fuzzy controller is very complex and the existing solutions, in some details, even deviate from the classical fuzzy approach. Our solution is, on the other hand, based only on the uncertain knowledge about the behaviour of the T&T driver and the results achieved are even better than those achieved by using the existing solutions.     

A Nonlinear Adaptive Controller: A Comparison between Fuzzy Logic Control and Neurocontrol

This paper explores, from a surface-fitting viewpoint, two algorithmswhich are often applied in the field intelligent control: fuzzyself-organizing controllers and neural networks. Both methodologiesadapt internal model parameters in response to the plant's input-outputmapping. However, while the convergence of single-layer neuralnetworks has been studied in great detail, very few theoremshave been proved about self-organizing fuzzy logic controllers.In this paper, it is shown that B-splines can provide a frameworkfor choosing the shape of the fuzzy sets. Then the operatorschosen to implement the underlying fuzzy logic are examined,showing how they can produce ‘smooth’ control surfaces.It is now possible to make a direct comparison between fuzzylogic controllers and feedforward neural networks, demonstratingthat, in a forward-chaining mode, storing the plant's behaviourin terms of weights or rule confidences is equivalent. Finally,three training rules for the self-organizing fuzzy controllerare derived.