Modeling and control of nonlinear systems using novel fuzzy wavelet networks: The output adaptive control approach

This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems. The control signal is comprised of two parts. The first part arises from an adaptive fuzzy wave-net based controller that approximates the system structural uncertainties. The second part comes from a robust H_∞ based controller that is used to attenuate the effect of function approximation error and disturbance. Moreover, a new self structuring algorithm is proposed to determine the location of basis functions. Simulation results are provided for a two DOF robot to show the effectiveness of the proposed method.     

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.     

On-Line Constrained Predictive Control Algorithm using Multi-Objective Fuzzy-Optimization and a Case Study

Model predictive control (MPC) has been used in process control systems with constraints, however, the constrained optimization problem involved in control systems has generally been solved in practice in a piece-meal fashion. To solve the problem systemically, in this paper, the Multi-Objective Fuzzy-Optimization (MOFO) is used in the constrained predictive control for online applications as a means of dealing with fuzzy goals and fuzzy constraints in control systems. The conventional model predictive control is integrated with the techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the fuzzy goals and the fuzzy constraints of the control problem is combined by using a decision function from the fuzzy theory, so it is possible to aggregate the fuzzy goals and the fuzzy constraints using fuzzy operators, e.g. t-norms, s-norms or the convex sum. It is shown that the model predictive controller based on MOFO allows the designers a more flexible aggregation of the control objectives than the usual weighting sum of squared errors in MPC. The efficiency of the presented algorithm is validated by the visual robot path planning.     

Optimization of Fuzzy Controller for an Uninhabited Airplane Using Genetic Algorithms

The design of a fuzzy logic based controller for an uninhabited airplane using genetic algorithms for parameter optimization is illustrated. The airvehicle mission requires that a prescribed trajectory be followed with a satisfactory accuracy. Fuzzy control modules are present in each of the four control channels. Inputs are position and velocity errors. The parameters of the fuzzy controller are: trapezoidal membership functions, five linguistic values, and height defuzzification method associated with peak value. The scaling factors of the fuzzy controller are optimized by means of a genetic algorithm such that, a performance index, based on errors from a stationary flight path, is minimized. The genetic algorithm is based on binary genetic representation, an elitist roulette wheel selection technique and two genetic operators: mutation and crossover. The performance of the resulting optimal fuzzy controller is assessed through numerical simulation.     

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.     

Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton–Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.     

Fuzzy fractional order sliding mode controller for nonlinear systems

In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PD^α, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.     

Algoritmo compensador neuronal discreto de dinámica en robots móviles usando filtro de Kalman extendido

This paper presents the design of an algorithm based on neural networks in discrete time for its application in mobile robots. In addition, the system stability is analyzed and an evaluation of the experimental results is shown.The mobile robot has two controllers, one addressed for the kinematics and the other one designed for the dynamics. Both controllers are based on the feedback linearization. The controller of the dynamics only has information of the nominal dynamics (parameters). The neural algorithm of compensation adapts its behaviour to reduce the perturbations caused by the variations in the dynamics and the model uncertainties. Thus, the differences in the dynamics between the nominal model and the real one are learned by a neural network RBF (radial basis functions) where the output weights are set using the extended Kalman filter. The neural compensation algorithm is efficient, since the consumed processing time is lower than the one required to learning the totality of the dynamics. In addition, the proposed algorithm is robust with respect to failures of the dynamic controller. In this work, a stability analysis of the adaptable neural algorithm is shown and it is demonstrated that the control errors are bounded depending on the error of approximation of the neural network RBF. Finally, the results of experiments performed by using a mobile robot are shown to test the viability in practice and the performance for the control of robots.     

Modelling and control of flexible joint robot based on Takagi–Sugeno fuzzy approach and its stability analysis via sum of squares

This article proposes a Takagi–Sugeno (T-S) fuzzy model of single-link rotary flexible joint robot. The proposed control method is based on parallel distributed control. The parameters of T-S controller are improved by distributed population genetic algorithm (GA) with chaos GA. Using Hermite–Biehler theorem in distributed population, GA is made to have a fast convergence. Dividing search space into several sub-spaces causes a better response, and chaos disturbance helps the whole algorithm to reach a best answer. The stability of the controller is analysed via the sum of squares programming, and finally, it is implemented on the plant.     

模糊输入时滞系统的输出反馈控制及稳定性分析

针对一类状态不可测的模糊输入时滞系统,应用平行分布补偿算法(PDC),设计了模糊观测器,提出了基于模糊观测器的输出反馈控制方法,给出了保证模糊时滞系统渐近稳定的新的充分条件.应用广义Lyapunov函数和线性矩阵不等式方法,证明了模糊输入时滞系统的渐近稳定性,同时给出了控制和观测增益矩阵的分离设计算法.仿真结果进一步验证了所提出的方法和条件的有效性.     

A robot-balancer on a cylinder

The problem of stabilizing the equilibrium of a robot placed on a cylinder which can roll along a horizontal plane is investigated. There is no slip in any of the external contacts. Control is achieved by means of the electromechanical angular acceleration of a flywheel on the robot. Steady motions are studied. The basic procedures for stabilizing the robot in a vertical position are analysed in a non-linear formulation. It is shown that the corresponding linear system is completely controllable. A coordinate and velocity controller with saturation is constructed. The domain in which the system can be stabilized is found in connection with the boundedness of the control function. The effect of measurement errors is examined. The control characteristics are calculated for certain actual robot parameters.     

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.     

基于GA-BP的模糊神经网络控制器与Elman辨识器的系统设计

提出了一种基于神经网络的模糊控制系统 ,该系统由模糊神经网络控制器和模型辨识网络组成 .文中介绍了模糊神经网络控制器采用遗传算法离线优化与 BP算法在线调整 ,给出了具体控制算法 ,推导了变形 Elmam网络的系统辨识算法 .仿真结果表明了此法的可行性和有效性 .     

基于模糊传感器的机器人动态障碍环境中的运动控制

对自主式机械手在动态和部分已知且存在运动障碍环境中的运动规划和控制进行了研究,解决了自由碰撞运动控制中具有普遍意义的问题。利用人工势能场的机器人导航控制技术由模糊控制实现,系统的稳定性由李雅普诺夫原理保证。模糊控制器为机器人伺服提供控制指令,使机器人在不可预知的环境中能实时地、自主地选择到达目标的路径和方向。在动态环境的实时控制中,基于传感器的运动控制是处理未知模型和障碍物的重要控制方式。     

分层模糊控制器在四足步行机器人中的应用

本文针对四足步行机器人模糊控制器规则庞大，逻辑复杂的问题，提出了一种分层模糊控制器的设计方法。该方法不依赖被控对象的数学模型，将状态变量分层以降低多变量系统的设计复杂性，仿真和实验结果显示了该方法的有效性。     

一类MIMO非线性系统的稳定自适应模糊控制

针对一类 MIMO不确定非线性系统 ,基于一种修改的李亚普诺夫函数并利用 I型模糊系统的逼近能力 ,提出一种分散自适应模糊控制器设计的新方案。该方案不但能够避免现有的一些自适应模糊 /神经网络控制器设计中对控制增益一阶导数上界的要求 ,而且能够避免控制器的奇异问题。通过理论分析 ,证明闭环控制系统是全局稳定的 ,跟踪误差收敛到零。仿真结果表明了该方法的有效性。     

自适应模糊变结构控制的研究

本文主要研究一类具有未知常数控制增益的非线性系统的自适应模糊控制问题,提出了一种能够利用专家的语言信息和数字信息的自适应模糊变结构控制器的设计方案。通过理论分析,证明了模糊变结构控制系统是全局稳定的,跟踪误差可收敛到零的一个邻域内     

An adaptive sliding mode backstepping control for the mobile manipulator with nonholonomic constraints

To solve disturbances, nonlinearity, nonholonomic constraints and dynamic coupling between the platform and its mounted robot manipulator, an adaptive sliding mode controller based on the backstepping method applied to the robust trajectory tracking of the wheeled mobile manipulator is described in this article. The control algorithm rests on adopting the backstepping method to improve the global ultimate asymptotic stability and applying the sliding mode control to obtain high response and invariability to uncertainties. According to the Lyapunov stability criterion, the wheeled mobile manipulator is divided into several stabilizing subsystems, and an adaptive law is designed to estimate the general nondeterminacy, which make the controller be capable to drive the trajectory tracking error of the mobile manipulator to converge to zero even in the presence of perturbations and mathematical model errors. We compare our controller with the robust neural network based algorithm in nonholonomic constraints and uncertainties, and simulation results prove the effectivity and feasibility of the proposed method in the trajectory tracking of the wheeled mobile manipulator.     

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.     

T–S fuzzy-model-based robust stabilization for a class of nonlinear discrete-time networked control systems

In this paper, the robust stabilization problem is investigated for a class of nonlinear discrete-time networked control systems (NCSs). To study the system stability and facilitate the design of fuzzy controller, Takagi–Sugeno (T–S) fuzzy models are employed to represent the system dynamics of the nonlinear discrete-time NCSs with effects of the approximation errors taken into account, and a unified model of NCSs in the T–S fuzzy model is proposed by modeling the approximation errors as norm-bounded uncertainties in system metrics, where non-ideal network Quality of Services (QoS), such as data dropout and network-induced delay, are coupled in a unified framework. Then, based on the Lyapunov–Krasovskii functional, sufficient conditions are derived for the existence of a fuzzy controller. By these criteria, two approaches to design a fuzzy controller are developed in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are provided to show the effectiveness of the proposed methods.