一类非确定欠驱动系统的串级模糊滑模控制

本文针对一类含有非确定项的欠驱动系统提出了一种串级模糊滑模控制方法.该方法先选取状态变量中两个相关联的系统状态构造第一级滑动平面,然后将第一级滑模函数作为一个广义状态,与剩下系统状态中的一个状态构造第二级滑动平面,直到所有的系统状态都包含在内构造最后一级滑动平面.同时考虑到系统模型中存在的不确定项,利用模糊逻辑的逼近功能进行估计,文中采用Lyapunov方法求取了控制器的控制律以及模糊逼近的有关参数的自适应律.该串级模糊滑模控制器能够保证各级滑动平面的稳定性,并且在仿真实验中得到了验证.     

具有性能约束的欠驱动系统分层滑模控制北大核心CSCD

针对具有性能约束的二阶欠驱动系统的跟踪控制问题,提出了自适应鲁棒滑模控制策略。首先,利用约束转化方程将带有状态性能约束的系统转化成无约束系统,通过引入预设性能方程可以同时保证系统的动态性能和稳态性能满足预设目标;然后,针对转化后的无约束系统设计了自适应分层滑模控制策略,并通过Lyapunov方法严格地证明了控制系统的稳定性并获得了自适应律;最后,通过小车倒立摆对比仿真实验证明了控制器的有效性和优越性。     

欠驱动惯性轮摆系统全局滑模控制

针对欠驱动惯性轮摆的镇定控制问题,本文提出了一种新型的滑模鲁棒控制策略,可在系统受到不确定性与外界干扰影响的情况下,实现全局渐近镇定控制.区别于现有方法,本文方法无需切换,且能将无驱动的摆杆摇起至竖直向上位置的同时,确保惯性轮回到初始位置.具体而言,首先对惯性轮摆系统的非线性模型进行非奇异坐标变换,将其变为类积分器形式.随后,根据转换后系统的形式,构造了一种新型的滑模面;经严格分析知,当系统状态处于该滑模面上时,它们将渐近收敛于平衡点.在此基础之上,设计了滑模控制律以确保系统状态始终处于该滑模面上,以实现镇定控制.最后,通过仿真验证了所提控制方法的有效性与鲁棒性,并与现有方法进行了对比.     

非匹配不确定高阶非线性系统的滑模控制新方法

对具有非匹配不确定的高阶非线性系统,采用改进的高阶滑模微分器获取已知状态的任意阶微分估计值,再以恰当阶次的状态微分估计值之差,得到非匹配不确定项及其微分的估计值,证明了其误差任意小.为避免奇异性和抖振,采用两种方案设计了滑模控制器,并设计鲁棒项提高系统鲁棒性.基于Lyapunov~论证明了系统稳定性.同现有其他方法相比,该方法具有适用范围更广、收敛速度快、控制精度高、运算量小、保守性低等优点.最后仿真证明了本文所有结论.     

水平欠驱动机械臂的反步自适应滑模控制

针对二自由度水平欠驱动机械臂系统,提出了基于分层滑模控制思想的反步自适应滑模控制方法．该方法能够在不对系统状态模型进行复杂坐标变换,并且没有约束方程限制的前提下实现对欠驱动系统的反馈滑模控制．仿真结果表明了该方法的有效性,而且优化后的控制器具有较好的适应性和控制效果．     

欠驱动刚体航天器姿态机动滑模控制研究

针对欠驱动刚体航天器的姿态机动控制问题,提出一种滑模变结构姿态控制器的设计方法.首先给出3轴稳定的欠驱动航天器姿态动力学和运动学模型,分析其模型特点;然后,设计了欠驱动刚体航天器的渐近稳定滑模控制律,并证明了其李雅普诺夫意义下的全局渐近稳定性.最后的仿真结果表明,该方法能够有效实现欠驱动航天器的姿态控制,且系统具有全局稳定性和鲁棒性.     

基于高阶滑模的自适应Super-Twisting控制系统设计

目前设计的自适应Super-Twisting控制系统由于不具备抖振情况感知能力,导致响应时间过长,控制精度较低。为了解决上述问题,基于高阶滑模设计了一种新的自适应Super-Twisting控制系统。系统硬件由采集模块、存储模块、控制模块、电路模块组成。采集模块利用AD8036采集器,主要负责采集Super-Twisting的控制参数,并对控制信号进行调理,存储模块选择TDB7659存储器,主要负责存储由采集模块采集的控制参数数据,控制模块是控制系统的核心,主要由微控制器、D/A转换器单元、信号输入通道、输出通道、电源单元组成,控制系统的电路主要负责为各个模块提供所需的电压。引入高阶滑模理论,通过主程序完成系统初始化、设计Super-Twisting 控制器、实现自适应Super-Twisting控制。实验结果表明,设计的基于高阶滑模的自适应Super-Twisting控制系统能够精准地感知抖振情况,缩短响应时间,提高控制精度。     

机械臂自适应反演超螺旋全局终端滑模控制

针对机械臂系统存在的系统参数摄动、非线性摩擦及外部干扰等不确定问题,提出一种自适应反演超螺旋全局终端滑模轨迹跟踪控制方法。该方法基于反演法、Lyapunov理论和全局快速终端滑模理论设计控制器,保证系统稳定性及全局收敛性,增强系统的鲁棒性。为解决系统集总扰动上界未知的问题,采用自适应技术设计切换控制律,抵消不确定性的影响,同时引入超螺旋算法抑制滑模控制固有的抖振现象。最后,通过理论分析和仿真算例验证了该控制器的有效性与可行性。     

Control for Underactuated Systems Using Sliding Mode Observer

In this work, first we estimate all the system’s state vector, with guarantied precision, for a category of second order underactuated mechanical systems (UMS), exploiting the triangular observer (TO) model that suits to the structure of these systems. Then we propose a sliding mode controller (SMC). The latter uses the estimated states given by the observer. The underactuated system is decomposed into two subsystems, where the sliding surface is constructed in two levels for each subsystem. The proposed controller guaranties the tracking performances, with minimization of chattering phenomenon, due to the constructed observer, even for system with uncertainties. Simulation results show the effectiveness of this strategy of control.

     

非匹配不确定系统的自适应反演准滑模控制

对于一类n阶非线性系统,提出一种自适应反演准滑模控制方法,控制的前n-1步采用自适应反演算法消除非匹配不确定性的影响,在最后一步,为改进跟踪效果,结合了可变边界层的思想,设计了准滑模控制方法,达到系统的n个状态快速收敛的目的.最终系统中不满足匹配条件的部分也具有较好的鲁棒性.与自适应反演线性滑模方法相比具有更好的跟踪性,理论分析证明了控制系统在削弱抖振的同时也能保证稳态精度,仿真实验证明了该方法的可行性.     

A New Adaptive Sliding Mode Control of Uncertain Nonlinear Systems

In this paper, a new adaptive sliding mode control is proposed to control nonlinear systems with parametric uncertainties and matched and unmatched external disturbances. The proposed method first combines immersion and invariance (I&I) adaptive scheme with sliding mode control (SMC), which preserves the advantages of the two methods. The proposed method is different from the approach of combining the backstepping adaptive scheme and sliding mode control in the parameter estimation law, which allows for prescribed dynamics to be assigned to the estimation error and is easier to tune. Finally, the method is applied to control a class of power systems, and simulation results show the advantages of the proposed method.     

An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems

An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability.

     

不确定离散系统自适应趋近律滑模控制技术研究

针对受噪声干扰的含有未知参数的离散系统控制问题,首先利用强跟踪滤波器对未知参数进行估计,接着提出了一种自适应离散趋近律滑模控制方法．深入研究分析了该离散趋近律方法的抖振问题,设计了基于该新型趋近律的滑模控制器．仿真结果表明所设计的控制器能有效降低抖振,改善控制性能．     

Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

In this paper, an adaptive proportional-derivative sliding mode control (APD-SMC) law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties, and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved. The Lyapunov techniques and the LaSalle's invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.      

广义不确定系统的模糊滑模控制*

研究了广义不确定系统的模糊滑模控制问题。控制结构中采用模糊系统来补偿动态不确定性,利用李亚普诺夫理论,证明了闭环系统的所有状态是全局有界的。