基于自适应神经网络的不确定非线性系统的模糊跟踪控制

提出了一种基于模糊模型和自适应神经网络的跟踪控制方法.在系统具有未知不确定非线性特性的情况下,首先利用T＿S模糊模型对系统的已知特性进行近似建模,对基于模糊模型的模糊H∞跟踪控制律进行输出跟踪控制.并在此基础上,进一步采用RBF神经网络完全自适应控制,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,从而有效地消除系统的未知不确定性和模糊建模误差的影响,保证了非线性闭环系统的稳定性和系统的H∞跟踪性能,而不要求系统的不确定项和模糊建模误差满足任何匹配条件或约束.最后,将所提出的方法应用到一非线性混沌系统,仿真结果表明了所提出的方案不仅能够有效地稳定该混沌系统,而且能使系统输出跟踪期望输出.     

基于自适应模糊PID控制的太阳光跟踪伺服系统

针对太阳光跟踪伺服系统中传统PID控制过程中的一些问题,通过对自适应模糊PID控制系统的分析,设计了一种双轴跟踪伺服系统自适应模糊PID控制器,同时在Simulink环境中建立了方位角跟踪传动机构仿真模型并完成了仿真。仿真结果表明,太阳光跟踪伺服系统自适应模糊PID控制器较传统PID控制器具有较强的稳定性、适应性与鲁棒性,在太阳光跟踪伺服系统控制领域具有重要的实用价值与应用空间。     

机械臂轨迹跟踪控制--基于EC-RBF神经网络的机械臂模型参考自适应控制

针对机械臂运动轨迹控制中存在的跟踪精度不高的问题,采用了一种基于EC-RBF神经网络的模型参考自适应控制方案对机械臂进行模型辨识与轨迹跟踪控制。该方案采用了两个RBF神经网络,运用EC-RBF学习算法,采用离线与在线相结合的方法来训练神经网络,一个用来实现对机械臂进行模型辨识,一个用来实现对机械臂轨迹跟踪控制。对二自由度机械臂进行仿真,结果表明,使用该控制方案对机械臂进行轨迹跟踪控制具有较高的控制精度,且因采用EC-RBF学习算法使网络具有更快的训练速度,从而使得控制过程较迅速。     

Path following algorithm for skid-steering mobile robot based on adaptive discontinuous posture control

The kinematic model of a skid-steering mobile robot (SSMR) is manipulated using signed polar transformation which represents a discontinuous state transformation. The influence of relative position between the instantaneous center of rotation (ICR) and SSMR center of mass is considered. Then, adaptive state feedback controller is designed and stability regions are studied. Subsequently, a point-to-point tracking algorithm is introduced to track a trajectory that is defined by a set of way-points, which is the more realistic case of dangerous exploration or landmine detection purposes. The closed-loop system is simulated using MATLAB environment and experimentally validated using a modified TURTLEBOT3 Burger. Results show that the proposed controller reaches almost zero steady state error with smooth paths for point stabilization, moreover, good tracking capabilities are demonstrated. The proposed control system integrates both posture and tracking algorithm, thus achieve trajectory tacking which is defined by a set of way-points.     

Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints

This article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.     

基于终端滑模机器人模糊自适应路径跟踪控制

对质心位置未知的移动机器人系统设计了基于快速终端滑模的模糊自适应路径跟踪控制方法。该方法采用模糊逻辑系统逼近控制器中的未知函数,基于李亚普诺夫稳定性分析方法对未知参数设计自适应律,并设计鲁棒控制器来补偿逼近误差。该方法不但可以保证闭环系统中的所有信号有界,而且可使跟踪误差在有限时间内收敛到原点的小邻域内。仿真结果验证了方法的有效性。     

一类基于模糊逻辑系统的非线性系统自适应跟踪控制

利用非线性不确定系统的动态数学模型和模糊逻辑系统，对不确定性的输出信息，设计出被控系统的自适应鲁棒跟踪控制器和模糊逻辑系统参数估计的自适应律．在较弱的假设条件下，证明了这种控制器能使被控系统的状态及参数估计误差一致终极有界．仿真实例表明，所提出的方法是有效的．     

基于PLC的中央空调模糊自适应PID监控系统

针对中央空调系统存在非线性、不确定性和干扰性等问题,提出了利用MATLAB实现模糊自适应PID控制,并对系统进行了编程仿真实验。仿真结果表明,将该方法应用在中央空调系统中是可行的,但是MATLAB不能与现场设备进行直接数据通信。因此将MATLAB和PLC控制相结合,利用OPC技术实现MATLAB和PLC之间的动态数据交换,达到智能控制的目的。     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.     

基于改进当前统计模型的自适应无源跟踪算法

针对无源跟踪中,标准当前统计模型无法自适应调整加速度极限值的缺点,设计了一种修正系数来通过机动目标的当前加速度自适应调整模型的加速度极限值,同时利用模糊控制的方法对修正系数的取值进行实时调整,实现了对当前统计模型的改进。最后结合容积卡尔曼滤波算法构造基于改进当前统计模型的自适应无源跟踪算法。仿真结果表明,相比基于标准当前统计模型的自适应跟踪算法,新算法对非机动目标、弱机动目标以及强机动目标都有更好的跟踪效果。     

基于模糊控制与预测控制切换的翼伞系统航迹跟踪控制

以翼伞系统的六自由度模型为基础,针对翼伞系统的平面航迹跟踪问题,对已有的预测控制器进行改进,提出模糊控制和广义预测控制相互切换的控制模式．利用横向轨迹误差法,在翼伞偏航角误差较大的情况下,采用模糊控制,直至偏航角误差达到设定的较小范围内,切换为广义预测控制,对翼伞航迹进行精确的制导,在一定程度上减少了处理器的运算量．采用真实的翼伞参数建立仿真模型,结果验证了这一控制方法的有效性．     

基于自适应AGBFN的不确定非线性系统的跟踪控制

针对一类具有未知不确定性的非线性系统,提出了一种基于观测器的自适应不对称高斯基函数网络(AGBFN)跟踪控制方案.当系统只有输出可以测量时,通过设计观测器对其进行在线状态估计,进而构造反馈控制律和自适应控制律.所提出的完全自适应AGBFN,可以在线更新网络所有参数,克服了传统RBF网络对称性约束,提高了网络的适应性和学习能力,可以有效地对消系统未知不确定项的影响.证明了闭环系统所有误差信号最终一致有界,且系统输出较好地跟踪参考模型输出.仿真结果表明了所提出方法的有效性.     

基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统设计

为保证全向轮机器人在移动过程中所捕获到的目标对象能够完全符合理想目标设定条件,准确追踪目标节点的运动行为,设计基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统;根据CAN主控框架的部署形式,按需连接核心管控电路与I/O跟踪模块;分别以转向控制器、速度控制器为例,完善全向轮控制结构的物理作用能力,实现机器人目标跟踪控制系统的硬件设计;在此基础上,定义频繁项集合,按照具体的关联规则特征描述结果,确定挖掘程序指令的执行能力,得到准确的关联离散度指标计算结果,实现控制系统的关联规则挖掘,再联合相关硬件设备结构,完成基于关联规则挖掘的全向轮移动机器人目标跟踪控制系统设计;分析对比实验结果可知,随着关联规则挖掘控制系统的应用,全向轮机器人在移动过程中所捕获到的目标对象能够将理想目标完全包含在内,机器人目标跟踪结果准确,可以辅助全向轮移动机器人更加准确地追踪目标节点的运动行为,符合实际应用需求。     

Direct adaptive neural network controller for a class of nonlinear systems based on fuzzy estimator of the control error

A new approach of direct adaptive control of single input single output nonlinear systems in affine form using single-hidden layer neural network (NN) is introduced. In contrast to the algorithms in the literature, the weights adaptation laws are based on the control error and not on the tracking error or its filtered version. Since the control error is being expressed in terms of the NN controller, hence its weights updating laws are obtained via back-propagation concept. A fuzzy inference system (FIS) with heuristically defined rules is introduced to provide an estimate of this error based on the past history of the system behaviour. The stability of the closed loop is studied using Lyapunov theory. A fixed structure is then proposed for the FIS and the design parameters reduce to the parameters of the NN. The method is reproducible and does not require any pre-training of the network weights.     

自适应神经网络四旋翼无人机有限时间轨迹跟踪控制

针对带有模型不确定性和未知外部干扰的四旋翼无人机轨迹跟踪控制问题,提出一种基于径向基(radial basis function, RBF)神经网络的自适应全局快速终端滑模控制方法,确保系统对期望轨迹的有限时间跟踪。该方法考虑到全局快速终端滑模控制在实际应用中的适应性和抖振问题,利用RBF神经网络替代等效控制量,以神经网络的在线学习能力补偿系统内部的不确定性和未知的外部干扰,有效地降低了系统的抖振;根据Lyapunov方法导出的自适应律在线调整神经网络权值,以保证闭环系统的稳定性。通过一系列仿真算例和飞行实验验证了该方法的有效性与可行性,结果表明:该控制方法相对于滑模控制的抖振更小,具有更好的收敛性和抗干扰能力,同时对模型的参数摄动具有更强的鲁棒性。     

全状态参数最优控制的鲁棒自适应励磁控制

在backstepping励磁设计中,虚拟控制函数的参数缺乏最优约束,为了克服此不足,通过坐标变换得到了新的励磁系统动态模型和参数替换律,结合反馈线性化和最优控制策略,在不确定参数和外部干扰两种条件下,给出了具有全状态参数最优控制的鲁棒自适应励磁控制设计方法.为了验证所提方法的正确性,以具体军用电站励磁系统模型为例,进行了仿真实验.仿真结果表明该方法在保证系统鲁棒稳定性和L2增益干扰抑制的同时,可明显提高状态参数的收敛速度和优化控制能力.     

A novel adaptive vehicle speed recommender fuzzy system for autonomous vehicles on conventional two-lane roads

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two-lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system.     

基于模糊PID控制的人工腿位置伺服系统设计与仿真

智能人工腿是机器人学和生物医学领域一个备受关注的研究课题,以前研制的智能人工腿,其汽缸内针阀开度的控制都是采用步进电机所构成的开环系统,位置精度不高,在本文中,我们将设计一个具有位置的速度反馈的闭环控制系统并引入基于Fuzzy推理的整顿PID控制策略,以提高控制系统的智能性,鲁棒性,快速性和准确性。本文首先概述了智能人工腿的控制原理和TMS320F240数字信号处理器（DSP）的主要特点,其次设计了一种基于Fuzzy-PID的直流电机位置伺服控制系统的结构,最后对该位置伺服系统进行了计算机仿真,结果表明,本文所提出的设计方法是正确的,可行的,可以有效地用于智能人工腿的行走控制。     

基于模糊控制的发动机动力性能分布式测控系统

提出了一种发动机动力性能分布式自适应测控系统。给出了基于Profibus-DP总线和USS协议的测控系统的网络结构、软硬件设计方法及工作原理。采用SiemensMasterDriver矢量控制器和AFE整流单元,结合参数自适应模糊PID控制器,实现了对测控装置的精确控制。利用工控机和第三方软件实现了对多台测控装置的实时远程监控。实际应用表明,系统组态灵活、编程方便、自适应能力强、可靠性高。