基于动态递归模糊神经网络的自适应电液位置跟踪系统

提出了动态递归模糊神经网络(DRFNN)以在线估计电液位置跟踪系统中包括非线性、参数不确定性、负载干扰等在内的未知动态非线性函数,基于lyapunov稳定性理论推导出DRFNN可调参数和估计误差的界的自适应律,并构造出稳定的自适应控制器.实验结果表明:基于DRFNN的自适应控制器可使电液位置跟踪系统具有较强的鲁棒性和满意的跟踪性能.     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态 神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系 统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项 和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神 经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经 网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法 较基于静态神经网络的自适应方法具有更好的性能.     

一类纯反馈力学系统的自适应模糊动态面控制

针对一类纯反馈形式的不稳定力学系统,提出自适应模糊动态面控制方法。在一般动态面控制的设计框架下,引入模糊系统逼近模型的未知函数,设计自适应律在线估计模糊系统权参数和模型未知参数,通过Lyapunov方法证明得出闭环系统半全局稳定。该策略避免了传统反演设计存在的“微分爆炸”现象,并且解决了纯反馈系统控制设计中通常存在的循环设计问题。仿真结果表明,控制系统能够克服不确定性,且能够简单有效地实现跟踪控制。     

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快、精度高,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

基于自结构动态递归模糊神经网络的无人机姿态控制*

针对无人机非线性、强耦合等特点,提出了基于该自结构动态递归模糊神经网络的姿态控制系统,给出了基于Lyapunov函数的系统稳定性证明。对四层模糊神经网络进行了优化和改进,设计了自结构动态递归模糊神经网络,该网络可以根据系统状态在线更新权值、创建/删除节点、优化网络结构。仿真表明：该控制方法的突出优点是,在兼顾考虑了系统中的不确定性因素、非线性因素及外部干扰并存的情况下,保证系统的稳定性和跟踪性能;同时此网络结构比固定结构的模糊神经网络响应速度快,因此更具优越性。     

基于反推的电液比例系统共振频率控制

针对电液比例系统的非线性以及不确定性因素对破碎频率控制的影响,设计反推的频率控制方法。建立电液比例阀控液压马达的破碎机构的非线性模型,特别是考虑了比例阀的动态频率和阻尼特性,将系统的各不确定性参数对破碎频率的影响等效为液压马达轴上的负载波动;设计基于反推的控制律,构建各步骤的误差信号、虚拟控制信号和目标函数间的关系,在不确定性有界的前提下保证了液压马达输出转速渐近跟踪的稳定性。给出频率控制系统的模型参数,并在基于Matlab6．5的环境下进行了仿真研究以及车载试验,结果表明,对于具有不确定性因素的电液比例频率控制系统,反推控制策略具有较好的控制性能,能够满足共振式破碎机的工程应用要求。     

基于反推的电液比例系统共振频率控制

针对电液比例系统的非线性以及不确定性因素对破碎频率控制的影响,设计反推的频率控制方法.建立电液比例阀控液压马达的破碎机构的非线性模型,特别是考虑了比例阀的动态频率和阻尼特性,将系统的各不确定性参数对破碎频率的影响等效为液压马达轴上的负载波动;设计基于反推的控制律,构建各步骤的误差信号、虚拟控制信号和目标函数间的关系,在不确定性有界的前提下保证了液压马达输出转速渐近跟踪的稳定性.给出频率控制系统的模型参数,并在基于Matlab6.5的环境下进行了仿真研究以及车载试验,结果表明,对于具有不确定性因素的电液比例频率控制系统,反推控制策略具有较好的控制性能,能够满足共振式破碎机的工程应用要求.     

移动机器人编队的递归模糊神经网络滑模控制

研究非完整移动机器人编队控制优化问题,由于动态模型存在诸多不稳定性,针对领航者-跟随者l-ψ控制结构,提出了一种Back stepping运动学控制器与自适应神经滑模控制器相结合的新型控制策略.采用动态递归模糊神经网络(dynam-ic recurrent fuzzy neural network,DRFNN)对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿.所提方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;根据Lyapunov方法的设计过程,保证了控制系统的稳定;仿真结果表明了改进方法对机器人编队优化控制的有效性.     

小型无人直升机反步自适应控制

针对具有强非线性、高度耦合以及参数不确定性特点的小型无人直升机系统,提出一种基于小脑模型关节控制器（Cerebellar Model Articulation Control,CMAC）神经网络的自适应反步控制方法,该方法采用小脑模型关节控制器神经网络在线学习系统不确定性以及反步控制中各阶虚拟控制量的导数信息,设计鲁棒控制项克服CMAC神经网络在线学习系统不确定性的误差,控制律由反步法回归递推得到。仿真结果表明,在模型参数不确定和存在较大误差的情况下,所设计的控制律具有理想的姿态跟踪性能以及良好的鲁棒性。     

Adaptive coordinated passivation control for generator excitation and thyristor controlled series compensation system

The problem of transient stability for a single machine infinite bus system with the generator excitation and thyristor controlled series compensation (TCSC) is addressed via the coordinated passivation method. The system does not need to be linearized. Two types of uncertainties, namely, the damping coefficient uncertainty and the modeling error of TCSC, are considered. First, an excitation control input and a parameter updating law are obtained simultaneously via adaptive back-stepping and Lyapunov methods to achieve stability of the zero dynamics subsystem. Then, a reactance modulated input is derived to ensure the feedback passivity of the whole system, based on which a stabilizing controller for the closed-loop system is designed. Simulation results show that the proposed controller produces better transient performance than the conventional direct feedback linearization controller.     

基于LS-SVM的BTT导弹动态逆反演控制

基于LS -SVM理论、动态逆控制和反演控制方法,针对具有非匹配不确定性的BTT导弹非线性数学模型,设计了一种基于LS- SVM的BTT导弹动态逆反演控制律;对传统反演控制律设计的不足之处,采用LS- SVM法逼近BTT导弹控制系统中带有未知成分非线性函数,从而实现了无需精确数学模型的全新控制律,避免了因建模误差对控制带来的不良影响,在此基础上,利用李亚普诺夫方法证明了系统的稳定性和收敛性;仿真结果表明,文章所设计的控制律,对导弹控制系统中存在的不确定因素具有较强的鲁棒性和自适应性.     

Adaptive back-stepping pitch angle control for wind turbine based on a new electro-hydraulic pitch system

A new electro-hydraulic pitch system is proposed to smooth the output power and drive-train torque fluctuations for wind turbine. This new pitch system employs a servo-valve-controlled hydraulic motor to enhance pitch control performances. This pitch system is represented by a state-space model with parametric uncertainties and nonlinearities. An adaptive back-stepping pitch angle controller is synthesised based on this state-space model to accurately achieve the desired pitch angle control regardless of such uncertainties and nonlinearities. This pitch angle controller includes a back-stepping procedure and an adaption law to deal with such uncertainties and nonlinearities and hence to improve the final pitch control performances. The proposed pitch system and the designed pitch angle controller have been validated for achievable and efficient power and torque regulation performances by comparative experimental results under various operating conditions.     

Adaptive Sliding Mode Control for Re-entry Attitude of Near Space Hypersonic Vehicle Based on Backstepping Design

Combining sliding mode control method with radial basis function neural network (RBFNN), this paper proposes a robust adaptive control scheme based on backstepping design for re-entry attitude tracking control of near space hypersonic vehicle (NSHV) in the presence of parameter variations and external disturbances. In the attitude angle loop, a robust adaptive virtual control law is designed by using the adaptive method to estimate the unknown upper bound of the compound uncertainties. In the angular velocity loop, an adaptive sliding mode control law is designed to suppress the effect of parameter variations and external disturbances. The main benefit of the sliding mode control is robustness to parameter variations and external disturbances. To further improve the control performance, RBFNNs are introduced to approximate the compound uncertainties in the attitude angle loop and angular velocity loop, respectively. Based on Lyapunov stability theory, the tracking errors are shown to be asymptotically stable. Simulation results show that the proposed control system attains a satisfied control performance and is robust against parameter variations and external disturbances.      

A globally stable saturated desired compensation adaptive robust control for linear motor systems with comparative experiments

The recently proposed saturated adaptive robust controller is integrated with desired trajectory compensation to achieve global stability with much improved tracking performance. The algorithm is tested on a linear motor drive system which has limited control effort and is subject to parametric uncertainties, unmodeled nonlinearities, and external disturbances. Global stability is achieved by employing back-stepping design with bounded (virtual) control input in each step. A guaranteed transient performance and final tracking accuracy is achieved by incorporating the well-developed adaptive robust controller with effective parameter identifier. Signal noise that affects the adaptation function is alleviated by replacing the noisy velocity signal with the cleaner position feedback. Furthermore, asymptotic output tracking can be achieved when only parametric uncertainties are present.     

路径规划; 态势评估; 模糊逻辑; 贝叶斯网络

针对非线性动态系统辨识和控制的特点，对4层模糊神经网络进行了优化和改进，形成了动态模糊神经网络，提高了网络的稳定性和对动态系统的辨识能力，同时给出了基于Lyapunov函数稳定收敛定理的各权向量以及权矩阵学习速率的自适应调整算法．应用于非线性动态系统的辨识和控制仿真试验表明，改进后的动态模糊神经网络与模糊神经网络相比，可取得更好的辨识精度和跟踪控制效果。     

Adaptive sensorless robust control of AC drives based on sliding mode control theory

This paper focuses in the design of a new adaptive sensorless robust control to improve the trajectory tracking performance of induction motors. The proposed design employs the so‐called vector (or field oriented) control theory for the induction motor drives, being the designed control law based on an integral sliding‐mode algorithm that overcomes the system uncertainties. This sliding‐mode control law incorporates an adaptive switching gain in order to avoid the need of calculating an upper limit for the system uncertainties. The proposed design also includes a new method in order to estimate the rotor speed. In this method, the rotor speed estimation error is presented as a first‐order simple function based on the difference between the real stator currents and the estimated stator currents. The stability analysis of the proposed controller under parameter uncertainties and load disturbances is provided using the Lyapunov stability theory. The simulated results show, on the one hand that the proposed controller with the proposed rotor speed estimator provides high‐performance dynamic characteristics, and on the other hand that this scheme is robust with respect to plant parameter variations and external load disturbances. Finally, experimental results show the performance of the proposed control scheme. Copyright © 2006 John Wiley & Sons, Ltd.