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.     

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     

Multimachine power system stabilizer based on optimal multistage fuzzy PID attendant honey bee mating optimization

This article presents a new strategy based on multistage fuzzy PID controller for damping power system stabilizer in multimachine environment using Honey Bee Mating Optimization (HBMO). The proposed technique is a new metaheuristic algorithm which is inspired by mating procedure of the honey bee. Actually, the mentioned algorithm is used recently in power systems which demonstrate the good reflex of this algorithm. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Hence, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by HBMO that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to single machine connected to infinite bus and IEEE 3–9 bus power system. The proposed technique is compared with other techniques through integral of the time multiplied absolute value of the error and figure of demerit. © 2015 Wiley Periodicals, Inc. Complexity 21: 234–245, 2016     

Wall-following of mobile robot based on fuzzy genetic algorithm to linear interpolating

Traditional fuzzy controller has some disadvantages, such as inferiorly adaptability due to the invariable membership function parameters and too many subjective factors. So in this paper, we firstly put forward a new method to fuzzy inference based on the idea of linear interpolating. This method overcomes the shortcoming of conventional fuzzy controller such as the character of multi-relay and the conflict of rule numbers and real-time. Then we use genetic algorithm to off-line optimize the membership function parameters of fuzzy controller, which is used in the controlling course of mobile robot following straight wall. The result shows the optimizing control strategy is more effective in the aspect of following precision than the traditional fuzzy controller.     

Hardware-in-the-loop experimental study on a fractional order networked control system testbed

Networked Control Systems (NCS) are of great interest in many industries because of their convenience in data sharing and manipulation remotely. However, there are several problems along with NCS itself due to the uncertainties in network communication. One issue inherent to NCS is the network-induced delays which may deteriorate the performance and may even cause instability of the system. Therefore a controller which can make the plant stable at large values of delay is always desirable in NCS systems. Our past work on Optimal Fractional Order Proportional Integral (OFOPI) controller showed that fractional order PI controllers have larger jitter margin (maximum value of delay for which system is stable) for lag-dominated systems when compared to traditional Proportional Integral Derivative (PID) controllers, whereas integer order PID controllers have larger jitter margin for delay-dominated systems. This paper aims at the design process of a tele-presence controller based on OFOPI tuning rules. To illustrate this, an extensive experimental study on the real-time Smart Wheel networked speed control system is performed using hardware-in-the-loop control. The real-time random delay in the world wide network is collected by pinging different locations, and is considered as the delay in our simulation and experimental systems. Comparisons are made with existing integer order PID controller. It is found that the proposed OFOPI controller is a promising controller and has faster response time than the traditional integer order PID controllers. Since the plant into consideration viz. the Smart Wheel is a delay-dominated system, it is verified that PID achieves larger jitter margin as compared to OFOPI tuning rules. Simulation results and real-time experiments showing comparisons between OFOPI and OPID tuning rules prove the significance of this method in NCS.     

多机电力系统直接自适应输出反馈模糊控制

针对多机电力系统励磁控制模型,考虑电力系统的状态不完全可测及多变量、非线性等特点,以T-S模糊逻辑系统直接逼近控制器,设计出基于状态观测器的直接自适应输出反馈模糊控制器,并通过李亚普诺夫函数进行了稳定性证明.算法具有很好的鲁棒性和动态性能,仿真结果表明所设计的控制器能够快速有效地改善系统在大干扰下的暂态稳定性.     

遗传算法在自动泊车辅助系统模糊控制中的应用

随着汽车工业的发展,自动泊车辅助系统已逐渐成为汽车的必备装置.对自动泊车控制过程进行了分析,设计了自动泊车辅助系统模糊控制器,并将遗传算法应用于模糊控制器参数寻优过程,较为有效的确定了模糊控制器的参数,使用遗传算法工具箱对模糊控制器的隶属度函数进行了优化.并在Matlab环境下,对自动泊车模糊控制进行了仿真研究,论述了遗传算法在改善模糊控制效果中的应用.     

自组织结构的变论域模糊控制

针对变论域模糊控制,提出一种新的自组织结构的变论域模糊控制方法。自组织结构算法可以调整变论域模糊系统结构以及动态获得模糊规则,进一步减小变论域模糊控制项的稳态逼近误差。通过进一步理论分析可知,自组织结构算法仅仅保证了系统瞬时的切换是平稳的,但不能保证系统的闭环稳定性。给出了所提出控制方法的适用条件。通过与固定模糊系统结构的变论域模糊控制比较,仿真结果表明,所提出控制方法不仅使得系统的稳态跟踪误差更平稳,而且使得输入控制信号更加平滑。     

Real-time stable self-learning FNN controller using genetic algorithm

A kind of real-time stable self-learning fuzzy neural network (FNN) control system is proposed in this paper. The control system is composed of two parts: (1) A FNN controller which use genetic algorithm (GA) to search optimal fuzzy rules and membership functions for the unknown controlled plant; (2) A supervisor which can guarantee the stability of the control system during the real-time learning stage, since the GA has some random property which may cause control system unstable. The approach proposed in this paper combine a priori knowledge of designer and the learning ability of FNN to achieve optimal fuzzy control for an unknown plant in real-time. The efficiency of the approach is verified by computer simulation.     

基于牛顿插值算法的模糊控制器

提出一种基于牛顿在线插值算法的模糊控制器,介绍该控制器的设计方法。该方法既简化了合成推理运算,又能满足模糊控制规则的完整性要求,从本质上消除由于量化误差和调节死区给模糊控制系统带来的稳态误差与颤振现象。通过仿真证明系统的性能得到明显改善。     

Tracking control of chaotic spinning disks via nonlinear dynamic output feedback with input constraints

In many control engineering applications, it is impossible or expensive to measure all the states of the dynamical system and only the system output is available for controller design. In this study, a new dynamic output feedback control algorithm is proposed to stabilize the unstable periodic orbit of chaotic spinning disks with incomplete state information. The proposed control structure is based on the T‐S fuzzy systems. This investigation also introduces a new design procedure to satisfy a constraint on the T‐S fuzzy dynamic output feedback control signal. This procedure is independent of the exact value of initial states. Finally, computer simulations are accomplished to illustrate the performance of the proposed control algorithm. © 2015 Wiley Periodicals, Inc. Complexity 21: 148–159, 2016     

A two-stage approach to self-learning direct fuzzy controllers

In this paper, a novel approach is presented to fine tune a direct fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pretraining, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished using the sign of the dependency of the plant output with respect to the control signal and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method. The enhanced features of the proposed algorithm are demonstrated by various simulation examples.     

Chaos suppression on a class of uncertain nonlinear chaotic systems using an optimal H∞ adaptive PID controller

This paper introduces an optimal H_∞ adaptive PID (OHAPID) control scheme for a class of nonlinear chaotic system in the presence system uncertainties and external disturbances. Based on Lyapunov stability theory, it is shown that the proposed control scheme can guarantee the stability robustness of closed-loop system with H_∞ tracking performance. In the core of proposed controller, to achieve an optimal performance of OHAPID, the Particle Swarm Optimization (PSO) algorithm is utilized. To show the feasibility of proposed OHAPID controller, it is applied on the chaotic gyro system. Simulation results demonstrate that it has highly effective in providing an optimal performance.     

ETSK模糊模型辨识及模糊控制算法

本文提出一种基于扩张原理的ＥＴＳＫ（ＥｘｔｅｎｄｅｄＴＳＫ）模型,导出了该模型的输入输出解析式,给出了辨识这种模型的方法。本文还导出了ＥＴＳＫ模型的一种等价形式——变权ＴＳＫ模型,从而将ＥＴＳＫ模型规则后件中的模糊数及其扩展运算转化为普通数的运算,使基于ＥＴＳＫ模型的模糊控制算法ＭＢＦＣ（Ｍｏｄｅｌ－ＢａｓｅｄＦｕｚｚｙＣｏｎｔｒｏｌ）易于实现。仿真辨识结果表明,ＥＴＳＫ模型的辨识效果和预报精度优于ＴＳＫ和ＬＭ模型;ＭＢＦＣ算法的控制效果优于通常模型ＰＩ控制算法     

A conformal mapping based fractional order approach for sub-optimal tuning of PID controllers with guaranteed dominant pole placement

A novel conformal mapping based fractional order (FO) methodology is developed in this paper for tuning existing classical (Integer Order) Proportional Integral Derivative (PID) controllers especially for sluggish and oscillatory second order systems. The conventional pole placement tuning via Linear Quadratic Regulator (LQR) method is extended for open loop oscillatory systems as well. The locations of the open loop zeros of a fractional order PID (FOPID or PI^λD^μ) controller have been approximated in this paper vis-à-vis a LQR tuned conventional integer order PID controller, to achieve equivalent integer order PID control system. This approach eases the implementation of analog/digital realization of a FOPID controller with its integer order counterpart along with the advantages of fractional order controller preserved. It is shown here in the paper that decrease in the integro-differential operators of the FOPID/PI^λD^μ controller pushes the open loop zeros of the equivalent PID controller towards greater damping regions which gives a trajectory of the controller zeros and dominant closed loop poles. This trajectory is termed as “M-curve”. This phenomena is used to design a two-stage tuning algorithm which reduces the existing PID controller’s effort in a significant manner compared to that with a single stage LQR based pole placement method at a desired closed loop damping and frequency.     

A neural fuzzy control system with structure and parameter learning

A general connectionist model, called neural fuzzy control network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples by machine learning techniques, and the connectionist structure can be trained to develop fuzzy logic rules and find membership functions. Associated with the NFCN is a two-phase hybrid learning algorithm which utilizes unsupervised learning schemes for structure learning and the backpropagation learning scheme for parameter learning. By combining both unsupervised and supervised learning schemes, the learning speed converges much faster than the original backpropagation algorithm. The two-phase hybrid learning algorithm requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a reinforcement neural fuzzy control network (RNFCN) is further proposed. The RNFCN is constructed by integrating two NFCNs, one functioning as a fuzzy predictor and the other as a fuzzy controller. By combining a proposed on-line supervised structure-parameter learning technique, the temporal difference prediction method, and the stochastic exploratory algorithm, a reinforcement learning algorithm is proposed, which can construct a RNFCN automatically and dynamically through a reward-penalty signal (i.e., “good” or “bad” signal). Two examples are presented to illustrate the performance and applicability of the proposed models and learning algorithms.     

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.     

孵化控制系统的模糊免疫PID控制算法研究

家禽孵化是一个复杂的生物学过程,实现其自动控制水平有着重大意义.针对孵化系统是一个多变量、强耦合、大滞后的复杂动态系统,提出一种模糊免疫P ID控制方法,该方法根据模糊控制原理对P ID参数进行在线修改,利用生物免疫机理调整非线性函数,然后用免疫修正进一步调整P ID系统参数,使被控对象具有良好的性能,实现了家禽孵化设备中温度、湿度和含氧量的智能控制.系统投入运行表明,动态响应好,控制精度高,鲁棒性高,易于各种孵化的实现,从而提高了孵化率.     

An entire strategy for precise system tracking control of a revolving thin flexural link,part I: Finite element modeling and direct tuning controller design

An entire control strategy including a design based model, controller design, and system output modification for a distributed parameter system is illuminated by application to feedback control of a revolving thin flexural link. In Part I, a very realizable actuator and a sensor, which uses a motor and a tachometer, are applied to design the control system. The finite element modeling and the state space representation are obtained for the purpose of control system analysis and computer simulation. Instead of relying on parameter identification subroutines, a controller design based on directly tuning the parameter of the gain makes the closed-loop absolutely stable and good for system tracking control. This control system design scheme is robust, insensitive to system parameter changes, and this algorithm cannot depend on traditionally priori knowledge such as the system dimension, exact model, or observer design. The performance included in the presence of all the high frequency dynamics can be effectively shown through the computer simulation, and one is led to speculate that this design scheme may perform quite well in the real world implementation.     

Fuzzy control of original UPOs of unknown discrete chaotic systems

This paper presents a fuzzy algorithm for controlling original unstable periodic orbits of unknown discrete chaotic systems. In the modeling phase, only input–output data pairs provided from the true system are required. The fuzzy model is developed using Gaussian membership functions and consequent functions where the Levenberg–Marquardt computational algorithm is employed for the model parameters calculation. In the controller design phase, the L₂-stability criterion is used, which forms the basis of the main design principle. Simulation results are given to illustrate the effectiveness and control performance of the proposed method.