采用RBF网络的喷雾机喷杆自适应动态面跟踪控制

为了实现喷雾机喷杆快速而准确地伺服跟踪农作物冠层高度，选用电液伺服系统作为其位置调节装置，系统建模以喷雾机喷杆为负载的电液伺服系统.首先，充分考虑系统的强非线性和参数不确定因素，建立完整的数学模型；然后，采用动态面方法设计控制器，通过RBF网络对不确定项和非线性函数进行逼近，在控制律中加入阻尼项补偿干扰对系统性能的影响；基于Lyapunov稳定性方法，证明闭环系统信号最终一致有界；最后，对某喷雾机喷杆系统进行仿真验证，结果表明设计控制器具有良好的仿形跟踪控制性能.     

含非线性项的不确定离散时滞系统鲁棒稳定性分析

针对一类同时存在非线性项和不确定项的离散时滞系统,研究了系统的鲁棒稳定性问题.通过构造Lyapunov函数并利用Schur补引理以线性矩阵不等式(LMI)形式给出了系统鲁棒稳定的充分条件;利用离散时滞系统鲁棒稳定性的充分条件,采用LMI技术,设计出基于LMI的状态反馈鲁棒控制器;理论证明该方法设计的控制器保证闭环系统鲁棒渐近稳定.     

Feng混沌系统参数识别及自适应模糊控制器的反步设计

设计了参数观测器,对Feng混沌系统中未知参数进行识别.采用反步控制方法,在存在输入扰动项的情况下,构造模糊自适应控制器控制Feng混沌系统.模糊逻辑系统用来逼近反步控制方法中出现的未知、非线性且结构复杂的函数.和传统的反步控制方法相比,本文采用的控制器结构简单,易于实现.仿真的结果验证了本文控制方法的有效性.     

考虑执行器特性的机械臂全阶滑模控制

针对动力学模型中带有参数未知特性的机械臂位置和速度跟踪问题,提出一种新型的基于神经网络和全阶滑模的控制策略.该策略考虑了执行器的动力学特性,然后基于跟踪误差,建立了全阶滑模面,并利用径向基网络对模型未知特性进行逼近,进而设计鲁棒自适应控制律使得系统状态到达滑模面并沿滑模面收敛到平衡点.理论分析证明了所设计的控制策略可在克服抖振问题的同时保证闭环系统的渐近稳定性.二连杆机械臂系统的数值仿真结果验证了所提出方法的有效性.     

基于免疫函数的输入死区未知严格反馈非线性系统投影自适应指令滤波反步控制

为解决含有未知项以及输入死区的严格反馈非线性系统跟踪控制问题,提出一种基于免疫函数的投影自适应指令滤波有限时间控制方法.该方法使用免疫函数构造扩张状态观测器对具有输入死区控制系统中的未知项进行逼近,并使用指令滤波解决反步法中微分爆炸问题,建立滤波误差补偿机制降低滤波误差对跟踪精度的影响,同时使用投影算子保证了自适应参数的有界性.与现有文献中基于障碍李雅普诺夫函数的自适应反步约束控制相比较,文章可同时约束系统状态、补偿跟踪误差以及自适应参数在预设的范围内,保证了闭环系统中所有信号有界,结合有限时间控制加快了控制系统的收敛速度.最后仿真结果表明了文章控制方法的有效性.     

基于RBF神经网络的鲁棒因子滑模变结构多自由度机械臂精确跟踪控制研究

针对具有时变干扰的不确定多自由度机械臂,文章设计了基于RBF神经网络的含有鲁棒因子的滑模变结构高精度跟踪控制方法.针对时变干扰,设计鲁棒因子,将其嵌入滑模变结构控制器,克服了时变干扰对系统跟踪性能的影响.将RBF神经网络控制算法结合鲁棒因子滑模变结构控制,估计多自由度机械手臂系统的不确定因素.采用Lyapunov函数方法,证明了系统的稳定性.对比分析了计算力矩法滑模变结构控制方法,仿真结果证明,基于RBF神经网络的鲁棒因子滑模控制,针对具有时变干扰的含有不确定因素的多自由度机械手臂系统,具有较为精确的跟踪性能.     

网络化马尔可夫跳变系统的鲁棒无源控制

针对基于网络的凸多面体不确定离散时间马尔可夫跳变系统,研究其鲁棒无源控制问题.在网络诱导时滞是时变且有界的情况下,基于李雅普诺夫稳定性理论,通过构造参数依赖的随机李雅普诺夫泛函和运用广义系统变换,提出了不依赖模态的无源控制器存在的时滞依赖充分条件.所设计的鲁棒无源控制器保证了相应的闭环系统是鲁棒随机稳定且具有指定耗散率.将鲁棒无源控制器设计问题转化为一组线性矩阵不等式的可解性问题.仿真算例证明了本文方法的有效性.     

非线性时滞系统自适应模糊动态面控制

针对未知非线性系统控制器设计过程中引入逼近器过多的问题,提出一种简化的自适应模糊动态面控制器设计方案.在控制器设计过程中,仅采用一个模糊逻辑系统作为逼近器,使得所有的未知项得到补偿,同时采用自适应技术在线辨识未知参数和逼近误差上界.文中的控制方案克服了传统backstepping控制器中"复杂性膨胀"的问题.通过构造合适的Lyapunov函数,证明闭环系统的所有信号为半全局最终一致有界.仿真实例验证所提出的控制方案的有效性.     

基于多模型自适应估计的航天器相对导航

研究适用于非线性不确定系统的多模型自适应估计(MMAE)算法,基于一系列不同的系统噪声方差阵建立多个模型,使得由多个并行滤波器构成的算法适用于存在未知干扰的导航系统.针对非线性不确定系统,分析了多模型自适应估计算法的稳定性,给出了算法误差有界的充分条件。重点研究用于远距离航天器相对导航的MMAE算法,精确获取空间目标的相对位置,对于执行空间任务或实现航天器避碰具有重要意义.为了改善导航系统的可观度,采取双视线测量目标跟踪策略.以基于双视线矢量测量的空间目标相对导航问题为例,对扩展卡尔曼滤波(EKF),非线性鲁棒卡尔曼滤波(NRF)和MMAE算法进行了对比研究,结果表明,系统中存在未知干扰的情况下,所设计的MMAE算法具有优于传统EKF和NRF的性能.     

不确定非线性系统的鲁棒适应H_∞几乎干扰解耦问题

本文研究了带有不确定参数的非线性系统的鲁棒适应 H∞ 控制的几乎干扰解耦问题 .运用改进的加幂积分器技巧与递归设计方法 ,构造性地设计了一种光滑鲁棒动态反馈控制律 ,在保证闭环系统内稳定的基础上 ,使系统达到干扰衰减 .     

电液位置伺服系统的鲁棒自适应控制

针对由于参数不确定性、非线性等因素导致的电液位置伺服系统跟踪控制问题,基于Lyapunov(李雅普诺夫)稳定性理论,提出了一种具有参数自适应能力的鲁棒自适应反步方法.通过设计的自适应律来抑制由于参数不确定性对系统跟踪控制性能的影响,设计的鲁棒控制律使得系统具有全局一致渐近稳定性能.此外,还对伺服阀换向引起的不连续性进行了近似处理.以伺服阀控对称缸系统为控制对象,仿真结果表明,和传统的PD控制方法相比,在参数不确定性的情况下,该控制方法使得电液伺服系统的位置跟踪误差波动较小,且能以较快速度渐近收敛到0,同时所需要的伺服阀输入电压信号值也更小,相关不确定参数在经过较短时间后均可以收敛到其稳定值,从而验证了所提出算法的有效性.     

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.     

不确定电液位置伺服系统的自适应终端滑模控制

为了解决非线性、不确定电液伺服系统的位置跟踪控制问题,提出了一种基于反步法的自适应终端滑模控制方法.该方法将自适应控制和终端滑模方法结合在一起,一方面,提出的自适应控制律可以对电液伺服系统中的不确定性参数进行有效在线估计和补偿;另一方面,通过引入误差吸引子到滑模趋近律中得到变系数趋近律,设计的终端滑模控制律不仅能够消除普通终端滑模控制律中的非奇异项,还大大降低了滑模面的抖震.最终,根据Lyapunov稳定性理论,位置跟踪误差的有限时间稳定性得以严格证明.将该方法与积分反步滑模控制和线性滑模控制方法进行了对比研究,仿真结果验证了该方法在电液伺服系统位置跟踪控制方面良好的鲁棒性和跟踪精度.     

Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems

A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC.     

Adaptive control of uncertain nonlinear systems using mixed backstepping and Lyapunov redesign techniques

In this paper, a robust adaptive control law for a class of uncertain nonlinear systems is proposed. The proposed controller guarantees asymptotic output tracking of systems in the strict-feedback form with unknown static parameters, and matched and unmatched dynamic uncertainties. This controller takes advantages of a robust stability property of the Lyapunov redesign method and a systematic design procedure of the backstepping technique. In fact, the backstepping technique is employed to enrich the Lyapunov redesign method to compensate for not only matched - but also unmatched-uncertainties. On the other hand, using the Lyapunov redesign method in each step of the conventional backstepping technique makes backstepping robust. The suggested controller is designed through repeatedly utilizing the Lyapunov redesign method in each step of the backstepping technique. Simulation results reveal the efficiency of the Lyapunov redesign-based backstepping controller.     

Adaptive type-2 fuzzy backstepping control of uncertain fractional-order nonlinear systems with unknown dead-zone

A new problem of adaptive type-2 fuzzy fractional control with pseudo-state observer for commensurate fractional order dynamic systems with dead-zone input nonlinearity is considered in presence of unmatched disturbances and model uncertainties; the control scheme is constructed by using the backstepping and adaptive technique. To avoid the complexity of backstepping design process, the dynamic surface control is used. Also, Interval type-2 Fuzzy logic systems (IT2FLS) are used to approximate the unknown nonlinear functions. By using the fractional adaptive backstepping, fractional control laws are constructed; this method is applied to a class of uncertain fractional-order nonlinear systems. In order to better control performance in reducing tracking error, the PSO algorithm is utilized for tuning the controller parameters. Stability of the system is proven by the Mittag–Leffler method. It is shown that the proposed controller guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the origin. The efficiency of the proposed method is illustrated with simulation examples.     

Chaos synchronization of coupled neurons via adaptive sliding mode control

In this paper, an adaptive neural network (NN) sliding mode controller (SMC) is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh–Nagumo (FHN) neurons under external electrical stimulation. The controller consists of a radial basis function (RBF) NN and an SMC. After the RBFNN approximating the uncertain nonlinear part of the error dynamical system, the SMC realizes the desired control property regardless of the existence of the approximation errors and external disturbances. The weights of the NN are tuned online based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization is obtained by the proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.     

Chaos control in AFM system using sliding mode control by backstepping design

This paper presents a robust algorithm to control the chaotic atomic force microscope system (AFMs) by backstepping design procedure. The proposed feedback controller is composed by a sliding mode control (SMC) and a backstepping feedback, so its implementation is quite simple and can be made on the basis of the measured signal. The developed control scheme allows chaos suppression despite uncertainties in the model as well as system external disturbances. The concept of extended system is used such that a continuous sliding mode control effort is generated using backstepping scheme. It is guaranteed that under the proposed control law, uncertain AFMs can asymptotically track target orbits. The converging speed of error states can be arbitrary turned by assigning the corresponding dynamics of the sliding surfaces. Numerical simulations demonstrate its advantages by stabilizing the unstable periodic orbits of the AFMs and this method can also be easily extended to elimination chaotic motion in any types of chaotic AFMs.     

Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping

In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.