基于模糊神经网络的机器人滑模自适应控制

滑模控制（SMC）响应快,对系统参数和外部扰动呈不变性,可保证系统的渐近稳定性,但其缺点是控制存在很强的抖动;而模糊神经网络（FNN）具有模糊系统和神经网络共同的特点。将滑模控制和模糊神经网络控制有机结合,利用简单得到的学习信号对模糊神经网络进行在线学习,通过平滑切换函数实现直接自适应控制策略。对两连杆机械手的仿真研究表明,在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能有效减小滑模控制的抖动问题。     

机器人的低通滤波滑模控制

滑模控制响应快,对系统参数和外部扰动呈不变性,可保证系统的渐进稳定性,但其缺点是控制存在很强的抖动。在一般滑模控制的基础上引入低通滤波器（LPF）,则保证了轨迹跟踪误差的快速收敛,同时使系统具有很强的鲁棒性。通过对两连杆机械手的仿真研究,表明在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能消除滑模控制的抖动问题。     

分数阶Chen混沌系统的径向基函数神经滑模控制

针对带有参数扰动和外部干扰的分数阶Chen混沌系统, 提出一种径向基函数(RBF)神经滑模控制方法. 设计滑模切换函数, 将其作为RBF神经网络的唯一输入, 网络的权值可依据滑模趋近条件在线调整. 基于Lyapunov稳定性理论, 分析了该方法的稳定性. 仿真结果表明该控制方法简化了常规神经网络控制结构的复杂性, 削弱了滑模控制的抖振程度, 对参数扰动和外部干扰具有较好的鲁棒性.     

永磁同步电机的自适应反演滑模变结构控制

针对永磁同步电机提出一种基于反演的PMSM自适应滑模控制方案.设计基于反演的滑模变结构位置控制器,通过RBF神经网络实现系统参数变化和外部负载扰动等引起的不确定上界值的在线辨识,减小滑模控制器的控制量,并引入饱和函数来减弱系统的"抖动"现象.理论分析和仿真结果对比表明,基于RBF神经网络的自适应反演滑模控制对参数变化和外部负载扰动具有很好的鲁棒性,永磁同步电动机获得了很好的跟踪效果.     

基于模糊神经网络的滑模控制

研究了一类不确定性非线性系统的滑模变结构控制,提出了一种基于模糊神经网络（Ｆｕｚｚｙ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋｓ）的滑模变结构设计方法,设计了控制器的结构,利用动态反向传播算法实现滑模控制,这种方法与一般变结构控制相比不但具有强的鲁棒性而且还能有效地消除抖动现象,同时在设计中不需要知识系统中不确定性和扰动的上界,另外还运用Ｌｙａｐｕｎｏｖ函数从理论上分析上了系统的稳定性。仿真结果说明了本文所提     

基于RBF神经网络补偿的直线伺服系统滑模鲁棒跟踪控制

永磁直线伺服系统具有高速、高响应和直接驱动等优点,但负载扰动、端部效应、非线性摩擦及系统参数变化会降低系统的伺服性能.为了在保证系统的跟踪性能的基础上.消除上述不确定性因素的影响,本文提出一种将变结构控制(VSC)和径向基函数神经网络(RBFNN)相结合的鲁棒跟踪控制策略.变结构控制具有快速响应,对不确定因素的不变性的优点.但是其“抖振”现象将影响直线伺服系统的平稳性和定位精度.采用径向基函数神经网络来模拟端部效应、参数变化、摩擦和外部负载等不确定因素,引入带死区的目标函数以缩短学习过程.通过RBFNN的补偿控制来减弱“抖振”输入的程度,进一步提高系统的稳态精度.仿真结果表明,该方案对直线伺服系统不确定性有很强的鲁棒性,同时,系统具有较好的跟踪性能,大大提高了直接驱动直线伺服系统的鲁棒跟踪精度.     

PMSM调速系统的自学习滑模控制

针对永磁同步电机存在对外界扰动及内部参数摄动较为敏感的问题,提出了一种自学习滑模控制方法.该方法设计了一类非线性光滑函数,并将其应用于扩张状态观测器和滑模趋近律中,同时采用最速下降法对滑模控制器增益参数进行自学习实时更新.仿真结果表明,与常规PI矢量控制方法相比,该控制方法不仅响应速度快、控制精度高,而且对系统外部扰动具有很强的抗扰能力.     

基于神经网络的PMSM自适应滑模控制

结合滑模控制和神经网络各自的优点,对永磁同步电机(PMSM)提出了一种基于神经网络的PMSM自适应滑模控制方案.首先设计了带积分操作的滑模变结构位置控制器,通过递归神经网络的在线学习来实时估计系统参数变化和外部负载扰动等不确定性的界限,减小滑模控制器的控制量.进而,在滑模控制器中又引入饱和函数取代符号函数,进一步减弱"抖振"现象.理论分析和实验仿真对比研究的结果表明所提出方法具有优越的动态性能和鲁棒性.     

3 DoF模块化机械臂轨迹跟踪策略

针对具有外部扰动和系统参数不确定的机械臂轨迹跟踪控制问题,提出了一种改进的自适应神经网络滑模跟踪控制方法.首先建立了三自由度(DoF)机械臂动力学模型,分别采用计算力矩法和基于改进趋近律的神经滑模控制法控制其名义部分和非名义部分.所提方法结合了径向基函数(RBF)神经网络与基于趋近律的滑模控制,使控制系统自适应地补偿机...     

机械臂系统自适应神经网络滑模控制器设计

针对机器人系统滑模控制器设计存在的抖振问题,提出了一种新型的具有可变滑模增益的控制器设计方案。在传统滑模控制器设计的基础上,该控制方案的创新之处在于所设计控制器的开关增益可实现动态自适应调整。采用径向基函数神经网络(radialbasis function neural network, RBFNN),使开关增益随关节参数动态改变,以适应系统的未建模动态及未知扰动。通过加入适当的自适应控制算法,有效地抑制逼近误差及外部扰动。并且,通过李雅普诺夫方法证明了系统的轨迹跟踪误差可渐近收敛到0。最后,仿真结果表明,所设计的方案降低了系统抖振,同时可有效地提高跟踪精度。     

挖掘机轨迹跟踪的滑模变结构控制

对液压挖掘机工作装置的轨迹跟踪进行了研究。在分析了液压挖掘机工作装置的动力学方程的基础上,针对其复杂的非线性,提出了一种新的液压挖掘机工作装置轨迹跟踪方法,即应用机器人学理论,建立了三自由度液压挖掘机工作装置的拉格朗日动力学模型,设计了带低通滤波器的滑模控制器,利用低通滤波器的滤除高频信号的功能,消除控制信号的抖动,给出沿规划轨迹工作所需的控制量,并给出了控制系统的设计方法。对三自由度工作装置进行了仿真研究,其结果表明,所设计的控制器对设定轨迹的跟踪具有良好的动态特性,对系统的不确定性具有较强的鲁棒性,在存在模型误差和外部扰动的情况下,该方案既能达到高精度快速跟踪的目的,又能消除滑模控制的抖动问题。     

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances.      

基于反演设计的机器人自适应动态滑模控制

针对机器人跟踪控制问题,设计了一种新型的动态滑模控制器,采用反演（backstepping）方法设计一种新的切换函数,将不连续项转移到了控制的一阶导数中,得到了输入的平滑性的动态滑模控制律。该控制律能保证轨迹跟踪误差的快速收敛性和参数不确定的鲁棒性,仿真实例验证了该控制算法的有效性。     

An Adaptive Neural Sliding Mode Controller for MIMO Systems

Here, a novel adaptive neural sliding mode controller (ANSMC) is proposed to handle the coupling and dynamic uncertainty of MIMO systems. The structure of this model-free new controller is based on a radial basis function neural network (RBFNN) which is derived from Lyapunov stability theory and relaxing Kalman–Yacubovich lemma to monitor the system for tracking a user-defined reference model. The weights of RBFNN can be initialized at zero, then, a novel online tuning algorithm is developed based on Lyapunov stability theory. A boundary layer function is introduced into the updating law to cover the parameter errors and modeling errors, and to guarantee the state errors converge into a specified error bound. An e-modification is added into the updating law to release the assumption of persistent excitation and obtain the appropriate values of the connecting weights of a RBFNN. To evaluate the control performance of the proposed controller, a two-link robot system is chosen as the simulation case. The numerical simulations results show that this novel controller has very good tracking accuracy, stability and robustness.     

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.     

RBFNN‐Based Identification and Compensation Mechanism for Disturbance‐Like Parametric Friction with Application to Tractor‐Trailer Vehicles

This paper develops an effective identification and compensation mechanism for the disturbance‐like parametric friction of a typical underactuated tractor‐trailer vehicle system. To begin with, a parametric friction model is proposed to describe various friction effects associated with the system velocity, and then a disturbance‐like parametric friction concept is introduced by considering the motion characteristics of tractor‐trailer vehicle. Next, the radial basis function neural network (RBFNN) is employed to identify the friction due to its high convergence rate, superior approximation precision and local‐minima avoidance ability. Afterwards, a sliding mode control (SMC) is utilized to compensate the identified friction due to its numerous merits, such as strong robustness and fast convergence. On the basis of the effective combination of identification and compensation mechanism, a favorable transient performance can be achieved during the desired velocity tracking process. Lastly, the simulation results confirm that the RBFNN‐based disturbance‐like parametric friction identification and compensation mechanism can effectively improve the trajectory tracking performance of tractor‐trailer vehicle.     

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.      

倾转翼飞机过渡段定高飞行控制研究

针对倾转翼飞机过渡段控制存在的时变、欠驱动、强耦合等非线性特点,采用滑模控制来对其进行控制,然后在此基础上引入RBF神经网络,利用其非线性映射能力有效解决了滑模控制中存在的误差问题,进一步改善了系统的动态性能。研究表明,采用基于RBF神经网络的滑模控制方法,可有效提高倾转翼飞机过渡段定高飞行的控制精度,同时也证明了在处理时变、欠驱动、强耦合的非线性系统时,滑模控制与神经网络结合具有其独特的优势。     

基于自适应模糊滑模控制的船舶航向控制器设计

针对船舶运动系统中固有的非线性、模型不确定性和风、浪、流等的干扰.提出了自适应模糊滑模控制(AFSMC)策略解决船舶的航向控制问题.通过采用模糊逻辑系统逼近系统未知函数,将滑模控制技术与自适应模糊控制技术相结合,设计了船舶航向AFSMC控制器.在滑模边界层内应用PI (proportional-integral)控制代替滑模控制中的切换项,削弱了滑模控制带来的抖振现象.借助李亚普诺夫函数证明了船舶运动系统中的信号都一致有界并利用Barbalat引理证明了跟踪误差渐近收敛到零.在参数摄动和外界干扰情况下进行了航向保持与改变仿真试验,采用AFSMC控制器得到了与无摄动和无干扰情况下相似的输出响应.实验结果表明,所提控制器能有效地处理系统不确定性和外界干扰,控制性能良好,具有很强的鲁棒性.