Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

Fault-Tolerant Control of Nonlinear Systems Based on Fuzzy Neural Networks

Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters＇ variation fault and load torque disturbance.

A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.

Neuro-fuzzy predictive control for nonlinear application

Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

Decentralized Control Based on FNNSMC for Interconnected Uncertain Nonlinear Systems

A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large scale systems with unknown bounds of high order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of known bounds of uncertainties and disturbances. The FNNSMC, which incorporates the fuzzy neural networks (FNN) and the SMC, can eliminate the chattering by using the continuous output of the FNN to replace the discontinuous sign term in the SMC. The bounds of uncertainties and disturbances are also not required in the FNNSMC design. The simulation results show that the FNNSMC has more robustness than the SMC.

Intelligent technology-based control of motion and vibration using MR dampers

Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.In this study,two control strategies are proposed for protecting buildings against dynamic hazards,such as severe earthquakes and strong winds,using one of the most promising semiactive control devices,the magnetorheological (MR) damper.The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper.These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms.The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms.The second control strategy involves the design of a fuzzy controller and an adaptation law.The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn't require a prior knowledge of the combined building-damper system,this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.

汽车半主动座椅悬架自适应模糊神经滑模控制

针对含有人体模型的5自由度座椅悬架系统,设计一种基于自适应模糊神经网络(Adaptive Neuro-Fuzzy Inference System,ANFIS)的滑模控制器(ANFIS-SMC)。首先,设计一种时变滑模面,通过调整其斜率使系统状态点快速到达滑模面,从而提升系统控制速度;其次,通过ANFIS对切换增益在线调节,用切换项消除干扰,实现控制器在复杂多源干扰下精确控制;最后,仿真验证采用双曲正切函数代替切换项中的符号函数,使得输出更连续,有效降低抖振。仿真结果表明:该控制器能有效提高系统的跟踪性能和响应速度,对不确定性干扰具有较好的鲁棒性,带有该控制器的座椅悬架乘坐舒适性明显改善。

Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control

This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches.The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network(NFN)and a linear state`-space model.The estimation of parameters for Hammerstein systems can be achieved by employing hybrid signals,which consist of step signals and random signals.First,based on the characteristic that step signals do not excite static nonlinear systems,that is,the intermediate variable of the Hammerstein system is a step signal with different amplitudes from the input,the unknown intermediate variables can be replaced by inputs,solving the problem of unmeasurable intermediate variable information.In the presence of step signals,the parameters of the state-space model are estimated using the recursive extended least squares(RELS)algorithm.Moreover,to effectively deal with the interference of measurement noises,a data filtering technique is introduced,and the filtering-based RELS is formulated for estimating the NFN by employing random signals.Finally,according to the structure of the Hammerstein system,the control system is designed by eliminating the nonlinear block so that the generated system is approximately equivalent to a linear system,and it can then be easily controlled by applying a linear controller.The effectiveness and feasibility of the developed identification and control strategy are demonstrated using two industrial simulation cases.

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡,且无法实现系统性能整体优化的问题,本文提出一种制冷站非线性预测控制策略,优化目标函数设计为满足建筑冷量需求的同时,尽可能提高系统整体能效.为解决上述两个优化目标之间的矛盾关系,本文采用模糊逻辑设计了优化目标权重自适应模块,实时求取权重因子最优解;针对非线性系统在线优化求解困难问题,本文提出了基于神经网络的非线性滚动优化算法,采用神经网络作为反馈优化控制器,并将系统优化目标函数作为在线寻优性能指标,结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进行在线寻优,算法计算量小,占用存储空间适中,便于采用低成本的现场控制器实现制冷站预测控制.仿真实验结果表明,本文所提出的预测控制策略与PID控制相比,在未加入优化目标函数权重自适应模块情况下,系统平均能效比提高约32.5%;进行优化目标函数权重自适应寻优后,系统平均能效提高约39.43%.

矿用宽体车主动油气悬架的复合控制方法研究

由于矿用宽体车被动油气悬架不能根据路面情况以及车载变化达到实时调整车姿来满足车辆平顺性要求,为进一步改善矿车行驶平顺性,提出一种神经网络和模糊PID控制相结合的主动悬架复合控制方法。建立单气室油气弹簧数学模型,并经实验验证了模型的正确性;在此基础上以C级路面作为输入,建立1/2车辆动力学模型,以悬架输出力为控制对象,对控制器的设计进行了详细研究,并对矿车前半车身垂向及侧倾方向的振动特性进行了对比分析。结果表明:与被动悬架相比,所设计的主动控制策略使车身垂直加速度降低了38.45%,侧倾角加速度降低了27.16%,轮胎动载荷降低了32.68%,悬架动扰度降低了34.8%,极大地改善了车辆行驶平顺性。

微型电动汽车主动悬架系统振动控制仿真

为提高汽车操纵稳定性,减少路面激励影响,提出微型电动汽车主动悬架系统振动控制研究。综合电动助力转向系统、主动悬架系统和路面激励的运动学特征,建立电动汽车整车运动学模型,便于集成控制;将神经网络和模糊算法相结合,以网络学习模式为基础,设计一个包含输入层、函数层、规则层、归一化层和输出层的模糊神经网络;以车辆动力学模型为输入,确定每层神经元数量,制定网络训练的模糊规则,利用归一化因子完成所有模糊集合的归一化处理;采用重心法完成输出结果的去模糊化,获得最终控制量。仿真结果表明,所提方法降低了横摆角速度的抖动幅度,提高了车辆协调控制能力,即使控制回路中存在时滞,也能保证算法的有效性。

整车磁流变半主动悬架控制策略研究

针对磁流变阻尼器复杂的非线性和整车悬架系统耦合控制的难点,结合磁流变阻尼器迟滞特性,采用通用性好的Spencer现象模型建立正向模型,结合映射能力强的BP神经网络构建磁流变阻尼器逆模型;接着采用不需要精确模型的模糊算法和强鲁棒性滑模算法建立整车七自由度磁流变悬架控制器,对车辆悬架系统进行控制。仿真结果表明:建立的磁流变阻尼器逆模型能够准确反馈输入控制电流,且基于Spencer现象模型的整车磁流变阻器结合所提出的控制算法进行仿真控制,其整车性能指标显著提升。

汽车半主动空气悬架自适应模糊神经网络控制

考虑空气悬架弹簧刚度可调的特性,建立了车辆5自由度的半主动悬架非线性动力学模型.提出了一种基于自适应模糊神经网络系统结构的模型,参考自适应控制方法来研究汽车半主动空气悬架的非线性控制问题,并考虑半车模型前后悬架的输入时滞,对其进行了仿真分析.研究结果表明:该控制方法能够使人体垂直加速度、车身垂直加速度和俯仰角加速度都得到很大的衰减,可在一定程度上减少路面对车身的振动冲击,提高汽车的行驶平顺性.

车辆主动悬架的神经网络模糊控制

利用神经网络来实现在车辆主动悬架中的模糊控制。通过对主动悬架实体装置的台架实验研究,在不同激励信号的作用下,神经网络模糊控制器都具有很好地抑制车体振动的特点,其控制效果明显优于PID控制。在车辆主动悬架中,神经网络模糊控制具有设计新颖,实用性强,特别对于减振效果要求较高的车辆,更能发挥其优点。

基于神经网络的半主动悬架自适应模糊控制

提出了基于神经网络的自适应模糊控制策略。模糊控制主要用来对付系统的非线性 ;神经网络根据振动响应的方差递推结果来辨识车体的振动情况实时调节模糊控制器的量化因子 ,使模糊控制器对路面的变化具有自适应的能力。在半主动悬挂 1/ 4车非线性模型的基础上进行了仿真研究。

TIG焊背面熔宽的神经网络模糊控制

TIG(TungstenInertGas)焊接过程是一个高度非线性、强耦合、时变的系统 ,针对这一特点 ,本文设计了单层神经网络模糊控制器 ,给出了学习算法。该控制器可以自动学习模糊控制规则 ,并随系统的变化自动调节模糊控制规则。采用普通CCD(ChargedCoupleDevice)摄像机拍摄熔池的正面图像 ,提取出熔池正面几何参数 ,利用熔池正面几何参数与背面熔宽的关系模型 ,对背面熔宽进行实时控制。仿真及试验结果表明 。

半主动悬架模糊动态建模与神经网络控制

进行可调减振器外特性试验,拟合其阻尼系数与步进电动机转角之间的非线性关系,基于模糊动态模型理论,建立车辆半主动悬架模糊动态模型.设计半主动悬架模糊神经网络控制策略,研制半主动悬架模糊神经网络控制器.在仿真的基础上,进行实车道路试验.结果表明,模糊动态半主动悬架模糊神经网络控制有效地衰减车身垂直振动,改善车辆行驶姿态,提高乘坐舒适性及行驶安全性,协调整车综合性能.

汽车非线性半主动悬架的模糊神经网络控制

考虑磁流变减振器阻尼力和悬架弹性元件非线性特性 ,建立车辆 6自由度的半主动悬架非线性动力学模型。提出了一种基于模糊神经网络系统结构的模型参考自适应控制方法来研究汽车半主动悬架的非线性控制问题 ,并考虑半车模型前后悬架的输入时滞 ,对其进行了仿真研究。研究结果表明 :运用模糊神经网络非线性控制方法能够使人体和车身垂直加速度、俯仰角加速度都得到很大的衰减 ,证实这种模糊神经网络控制方法可大大减少路面对车身的振动冲击 。