Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.

Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

Command filtered integrated estimation guidance and control for strapdown missiles with circular field of view

In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated estimation guidance and control nonlinear model with limited actuator deflection angle is established considering the seeker's FOV constraint.The boundary time-varying integral barrier Lyapunov function(IBLF)is employed in backstepping design to constrain the body line-of-sight(BLOS)in IEGC system to fit a circular FOV.Then,the nonlinear adaptive controller is designed to estimate the changing aerodynamic parameters.The generalized extended state observer(GESO)is designed to estimate the acceleration of the maneuvering targets and the unmatched time-varying disturbances for improving tracking accuracy.Furthermore,the command filters are used to solve the"differential expansion"problem during the backstepping design.The Lyapunov theory is used to prove the stability of the overall closed-loop IEGC system.Finally,the simulation results validate the integrated system's effectiveness,achieving high accuracy strikes against maneuvering targets.

Asymmetric time-varying integral barrier Lyapunov function based adaptive optimal control for nonlinear systems with dynamic state constraints

This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.

具有时变输出约束的非线性多智能体系统自适应最优包含控制

本文对具有时变输出约束和未建模动态的不确定严格反馈非线性多智能体系统,提出了一种最优包含控制方法.利用一种新型积分型障碍Lyapunov函数处理输出约束,利用动态信号处理未建模动态,利用动态面控制方法设计前馈控制器,结合自适应动态规划和积分强化学习方法设计最优反馈控制器,利用神经网络在线逼近相应代价函数,并设计权重更新律.理论分析证明了所有跟随者的输出收敛到领导者生成的凸包中,全部跟随者组成的闭环系统是半全局一致最终有界的,同时,跟随者的输出保持在给定的约束集中,代价函数达到最小.仿真结果验证了所提出方法的有效性.

改进无模型自适应迭代学习的直线电机滑模控制

二维直线电机具有非线性、多变量及强耦合等特点,其精确模型无法获得,同时,在实际过程中由于参数摄动及扰动而造成的运行不稳定现象,致使对其跟踪控制十分困难。针对以上问题,根据无模型自适应迭代学习控制不依赖被控系统精确数学模型的特点,及滑模控制具有可设计且与对象参数和扰动无关的滑动模态,提出一种改进无模型自适应迭代学习的滑模结构复合控制策略。在无模型自适应迭代学习控制方案的准则函数中加入误差变化率,并对其收敛性进行分析论证;然后在紧格式动态线性化基础上,设计指数趋近律滑模控制,使改进无模型自适应迭代学习的滑模控制复合策略能够克服不稳定现象并具有很强的鲁棒性,从而进一步提高系统响应速度和控制精度。最后通过仿真和实物验证,控制精度稳定在1μm范围内,并与其他控制方案相比,验证了所提方法的准确性和有效性。

基于改进MPC与RBF-PID的智能车轨迹跟踪控制

为提升智能汽车轨迹跟踪控制的稳定性和精度,提出一种基于时域参数自适应MPC与RBF-PID的轨迹跟踪控制方法.首先搭建车辆横纵向动力学模型;其次分析控制时域与预测时域对跟踪精度的影响,设计时域参数自适应MPC横向控制器并进行仿真验证;然后基于径向基函数(RBF)神经网络整定PID控制参数,设计分层式纵向控制器,并设计折线速度曲线与PID算法进行对比;最后以车速为耦合点构建智能汽车横纵向综合控制系统,并在蛇形工况下对横纵向综合控制系统进行仿真实验.结果表明,所设计的横向控制器能保证低速时的跟踪精度和高速时的车辆稳定性,纵向控制器可有效提高速度跟踪精度,横纵向综合控制系统能实现车辆在变车速工况下对轨迹的精准跟踪,同时保证良好的行驶稳定性与舒适性.

冷链运输车空调冷风机运行自适应PID控制方法研究

冷链运输车空调冷风机运行环境复杂,导致传统的比例积分微分(PID)控制过程难以准确设定控制参数。为解决这一问题,设计冷链运输车空调冷风机运行自适应PID控制方法。首先,通过分析冷链运输车空调冷风机工作过程,选取多个控制参数,以降低复杂度。然后,设计基于自适应PID控制器的控制模型,以送风温度和送风压力作为控制量,将冷、热水流量及风机转速作为被控制量,求解控制量和被控制量的传递函数。最后,根据传递函数与期望值间的耦合关系,对控制分量的增减进行调整,以实现冷链运输车空调冷风机运行的自适应PID控制。试验结果表明,该方法能够使冷链运输车内温度保持稳定。该方法控制精准度高、鲁棒性强、实用价值高,可以确保货物在整个冷链运输过程中的质量和安全。

自适应积分逆推滑模励磁控制研究

电力系统参数摄动和外界干扰等因素对励磁系统影响很大,该文选择易于测量的状态变量建立鲁棒励磁控制系统模型,结合逆推设计方法和自适应滑模控制理论设计励磁控制器。按照逆推设计方法,逐步构造了李亚普诺夫函数,在构造虚拟控制信号时引入误差的积分项;给出阻尼系数的自适应律;设计了新的滑模面,从而得到最终的控制规律,对干扰进行抑制。仿真实验结果表明,系统在动静态响应性能上均优于常规逆推控制,说明这种控制方法能够改善系统的电压和功角稳定性。

发电机的非线性自适应逆推综合控制

发电机励磁和汽门系统是一个典型的多变量、非线性、强耦合、不确定复杂系统,其综合控制将会改善电力系统稳定性和动态品质,所以设计简单、有效的综合控制器既必要又困难.针对单机无穷大励磁与汽门系统,运用自适应逆推方法和系统的Lyapunov函数,获得了发电机的非线性综合控制器和参数替换律,文中给出了该控制器的具体设计步骤.由于在控制器设计中没有运用任何线性化方法,因而所得控制器充分利用了系统的非线性特性;同时考虑了发电机阻尼系数的不确定性,使得控制器对系统参数的变化具有很强的鲁棒性.数字仿真结果表明,所设计的控制器具有鲁棒性,并可有效地提高电力系统的稳定性.

直流电机模型时变参数的在线辨识方法分析

为解决直流电动机实际运行中模型参数因受到运行环境、工况等条件的影响而导致其建模不精确的问题,研究和比较了递推最小二乘法、指数遗忘的递推最小二乘法、矩阵规范化的递推最小二乘法、投影算法、正则化的最小二乘法5种实时参数估计方法。引入平方误差积分准则、时间平方误差积分准则、绝对误差积分准则和时间绝对误差积分准则,对实时参数估计的精度和效果进行评判。计算机仿真结果表明,对于时变的直流电动机模型参数,RLSEF算法具有在误差积分准则意义下的最优辨识精度。

考虑视场角约束的捷联导引与控制一体化设计

针对现有导引与控制一体化设计方法在用于捷联战术导弹时存在无法保证弹目视线满足导引头视场角约束的问题,将捷联解耦原理和状态约束控制方法相结合提出一种新型考虑视场角约束的捷联导引与控制一体化设计方法。首先引入捷联解耦原理建立捷联导引与控制一体化设计模型,将其转换为严格反馈系统;其次,针对系统模型存在的不确定扰动,设计了一种对不确定上界平方进行估计的自适应律;第三,针对视场角约束问题,采用积分型障碍Lyapunov函数结合动态面控制设计了一体化导引与控制规律。最后通过Lyapunov理论证明了闭环系统的稳定性和所有信号的一致有界。数值仿真结果校验了设计方法的有效性。

基于积分型切换函数的自适应神经网络控制

针对一类具有未知函数控制增益的非线性系统,利用RBF神经网络的逼近能力,依据滑模控制原理,提出了一种直接自适应神经网络控制器设计新方案。通过引入积分型切换函数及逼近误差自适应补偿项,监督控制用饱和函数代替符号函数,根据李雅普诺夫稳定性理论,证明了闭环系统是全局稳定的,跟踪误差收敛到零。该算法应用于连续搅拌型化学反应器CSTR(Continuous Stirred Tank Reactor),仿真结果显示,该算法能很好地使CSTR跟踪给定的温度信号,表明了该控制策略的有效性。

一种新型综合监控联动功能模型的设计与实现

综合监控系统(ISCS)的联动功能实时高效地解决各分散独立的多个子系统协调控制问题,是轨道交通中应急处理机制的核心,是缩短救援时间、减少损失、减少事故影响至关重要的一环。该文介绍并分析一种包括处理多元化触发机制、在线自定义自适应诊断及辅助决策支持在内的联动功能的模型及实现。此功能的应用,提高了综合监控系统应对事件的智能处理能力和响应能力,保障轨道交通运营安全稳定。

一种改进的非线性纯反馈系统的自适应动态面控制

研究了一类带有未知扰动的非线性纯反馈系统的控制问题.通过引入动态面技术,结合神经网络逼近方法,并利用积分型Lyapunov函数,提出了一种改进的自适应动态面控制方案.在传统后推设计方法的每一步中引入一阶滤波器进行设计,避免了对一些非线性函数,如虚拟控制,反复求导而造成的计算复杂性问题.与现有研究结果相比,该方案放宽了对于控制系统的要求,有效地减少了可调参数的数目,无需虚拟控制增益系数导数的信息.理论分析证明了闭环控制系统是半全局一致终结有界的,且跟踪误差收敛到一个小的邻域内.

基于网络的液压马达伺服位置系统自适应鲁棒积分控制

针对基于网络的液压伺服控制系统面临的网络延时和阀控马达建模结构不确定性问题,提出了基于Pade定理和反步推导方法合成的误差符号鲁棒积分自适应控制器。该控制器使用Pade定理近似处理时变网络引起的延时,降低延时对控制系统跟踪性能的影响,应用自适应率逼近系统结构不确定性和延时误差值,采用误差符号控制方法补偿剩余的结构不确定性。通过构造合适的Lyapunov函数,验证了闭环系统的全局稳定性,保证闭环系统所有信号的有界性和跟踪误差渐进收敛性。仿真结果表明了该控制方法的高精度跟踪性能。

鲁棒自适应PID控制的感应电机伺服系统

开发以李亚普诺夫稳定性理论为基础的PID控制的感应电机伺服系统,分析了基于李亚普诺夫稳定性理论的PID自适应控制技术。针对一类非线性系统,利用反步递推技术,设计了一种新型的自适应控制方法。详细讨论了该控制方法的几个关键环节,PID控制器中的比例系数、积分时间常数和微分时间常数自适应调整、以及控制器不确定项的边界值的自适应估计律均通过李亚普诺夫稳定性理论推导得出。仿真和实验结果验证了控制方法的有效性和可行性。

水泥生料磨机系统化学成分的自适应控制和参数估计

本文针对水泥生料磨机系统的成分控制问题，提出了一种新型参数估计算法和自适应控制系统．新算法利用被控过程的先验知识避免了由于输入信号不“持续激励”而引起的估计错误．在新控制系统中，增设了基于新算法的成分估计器并采用了改进最小方差自校正控制器．仿真实验和实际应用结果都已证实：估计算法和控制方案是成功的．与普通最小二乘法相比，新算法具有更好的收敛性和更高的精度．控制方案已在工业控制机上实现，并用于水泥厂实时控制，运行结果是令人鼓舞的．